LongTermIntelligence.com
type: Organization
description: Machine-intelligence architecture and operations for governed AI agents, multi-agent systems, control planes, evaluation, observability, memory, security, and human authority.

LongTermIntelligence.com
type: WebSite
publisher: LongTermIntelligence.com

What does LongTermIntelligence.com do?
type: Question
accepted answer: Answer
  text: LongTermIntelligence.com is a machine-intelligence architecture and operations firm. It helps enterprises design agentic AI, manage multi-agent swarms, build control planes, evaluate behavior, preserve human authority, and make evidence-backed decisions about what should move from pilot to production.
is part of: Machine intelligence, agentic AI, and governed swarm management

Machine intelligence, agentic AI, and governed swarm management
type: WebPage
description: LongTermIntelligence.com architects machine intelligence, agentic AI, multi-agent control planes, evaluation, and governed swarms for enterprise operations.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-08-01
date modified: 2026-08-01
keywords: machine intelligence
keywords: artificial intelligence
keywords: AI agents
keywords: agentic AI
keywords: agentic swarm management
keywords: multi-agent systems
keywords: AI control plane
keywords: AI governance
about: machine intelligence
about: agentic AI
about: agentic swarm management
about: multi-agent systems
about: governed autonomy
main entity: What does LongTermIntelligence.com do?

What capabilities make machine intelligence dependable?
type: Question
accepted answer: Answer
  text: Dependable machine intelligence requires architecture, agentic swarm orchestration, a control plane, evaluation, observability, governed memory, human authority, security, and recovery. The missing capability is the one that currently prevents a bounded production decision.
is part of: Machine intelligence capabilities for control, coordination, and evidence

Machine intelligence capabilities for control, coordination, and evidence
type: WebPage
description: Six capabilities for architecting, evaluating, and operating governed machine intelligence and agentic swarm systems.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-08-01
date modified: 2026-08-01
keywords: machine intelligence capabilities
keywords: agent swarm architecture
keywords: AI control plane consulting
about: architecture
about: agentic swarms
about: evaluation
about: governance
main entity: What capabilities make machine intelligence dependable?

Which machine-intelligence engagement should an organization start with?
type: Question
accepted answer: Answer
  text: Start with the engagement that resolves the current decision: readiness when fit is unclear, architecture when the system shape is unclear, evaluation when release evidence is missing, a control plane when runtime authority is missing, rescue when delivery is stalled, or an operating office when multiple initiatives require ongoing governance.
is part of: Machine intelligence services for governed AI and agentic operations

Machine intelligence services for governed AI and agentic operations
type: WebPage
description: Bounded services for machine intelligence readiness, agentic swarm architecture, evaluation, control-plane implementation, and recurring operations.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-08-01
date modified: 2026-08-01
keywords: agentic swarm consulting services
keywords: machine intelligence architecture service
keywords: multi-agent evaluation
about: services
about: architecture
about: control planes
about: evaluation
main entity: Which machine-intelligence engagement should an organization start with?

What is the Machine Intelligence Readiness Sprint?
type: Question
accepted answer: Answer
  text: The readiness sprint evaluates one workflow across process value, data, integrations, agent design, authority, risk, evidence, cost, and operating ownership. It ends with a proceed, narrow, remediate, or stop decision rather than an open-ended AI roadmap.
is part of: Machine Intelligence Readiness Sprint

Machine Intelligence Readiness Sprint
type: WebPage
description: A bounded readiness sprint for machine intelligence, agentic workflows, and multi-agent systems approaching production or material expansion.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-08-01
date modified: 2026-08-01
keywords: machine intelligence readiness
keywords: AI agent readiness assessment
keywords: agentic AI production readiness
about: readiness
about: risk
about: architecture
main entity: What is the Machine Intelligence Readiness Sprint?

What is an Agentic Swarm Architecture Blueprint?
type: Question
accepted answer: Answer
  text: The blueprint defines agent roles, task and handoff graphs, state, memory, tools, identity, policies, budgets, evaluation, human authority, observability, recovery, and an implementation sequence for a bounded multi-agent system.
is part of: Agentic Swarm Architecture Blueprint

Agentic Swarm Architecture Blueprint
type: WebPage
description: A principal-led architecture blueprint for multi-agent roles, task graphs, memory, tools, policies, control planes, and reversible implementation.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-08-01
date modified: 2026-08-01
keywords: agentic swarm architecture
keywords: multi-agent architecture consulting
keywords: AI swarm design
about: agentic swarms
about: architecture
about: orchestration
main entity: What is an Agentic Swarm Architecture Blueprint?

What are Swarm Evaluation and Release Gates?
type: Question
accepted answer: Answer
  text: Swarm evaluation and release gates are repeatable scenario suites and deterministic promotion checkpoints that test task quality, coordination, tool use, policy, cost, latency, human calibration, and recovery before a multi-agent change reaches production.
is part of: Swarm Evaluation & Release Gates

Swarm Evaluation & Release Gates
type: WebPage
description: A repeatable evaluation and release-gate implementation for multi-agent outcomes, coordination, safety, cost, latency, and recovery.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-08-01
date modified: 2026-08-01
keywords: multi-agent evaluation
keywords: agentic AI release gates
keywords: AI swarm testing
about: evaluation
about: release engineering
about: agentic swarms
main entity: What are Swarm Evaluation and Release Gates?

What does Agent Control Plane Implementation deliver?
type: Question
accepted answer: Answer
  text: The engagement implements the runtime control layer for agent identity, permissions, model and tool routing, state, budgets, policy checks, approvals, telemetry, evaluation, incident containment, and shutdown.
is part of: Agent Control Plane Implementation

Agent Control Plane Implementation
type: WebPage
description: Implementation of identity, policy, routing, observability, budgets, approvals, and shutdown controls around a bounded agentic swarm workflow.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-08-01
date modified: 2026-08-01
keywords: AI agent control plane
keywords: agent governance platform
keywords: multi-agent observability
about: control planes
about: governance
about: observability
main entity: What does Agent Control Plane Implementation deliver?

What is the Machine Intelligence Operations Office?
type: Question
accepted answer: Answer
  text: The operations office provides recurring architecture, release, evaluation, incident, vendor, portfolio, cost, evidence, and risk review after the organization has enough system context to govern machine intelligence as an operating capability.
is part of: Machine Intelligence Operations Office

Machine Intelligence Operations Office
type: WebPage
description: Recurring architecture, evaluation, release, incident, vendor, and portfolio oversight for machine intelligence systems and agentic swarms.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-08-01
date modified: 2026-08-01
keywords: AI operations office
keywords: machine intelligence governance advisory
keywords: agentic AI architecture retainer
about: operations
about: portfolio governance
about: architecture
main entity: What is the Machine Intelligence Operations Office?

What happens in the Joint Discovery and Swarm Architecture Workshop?
type: Question
accepted answer: Answer
  text: The workshop maps the business decision, workflow, actors, data, tools, current agents, authority, failure modes, evidence, and constraints, then identifies the smallest credible workstream and the conditions under which it should not proceed.
is part of: Joint Discovery & Swarm Architecture Workshop

Joint Discovery & Swarm Architecture Workshop
type: WebPage
description: A paid workshop to map a machine intelligence opportunity, test technical fit, define workstream boundaries, and recommend the smallest credible next scope.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-08-01
date modified: 2026-08-01
keywords: AI discovery workshop
keywords: agentic swarm workshop
keywords: multi-agent architecture discovery
about: discovery
about: partners
about: architecture
main entity: What happens in the Joint Discovery and Swarm Architecture Workshop?

How is LongTermIntelligence.com work commercially structured?
type: Question
accepted answer: Answer
  text: Commercial scope is organized around bounded decisions and named deliverables rather than a generic AI transformation. A discovery, assessment, architecture, evaluation, pilot, rescue, or operating engagement should state assumptions, exclusions, evidence, decision rights, and the next gate.
is part of: A commercial model built around bounded decisions

A commercial model built around bounded decisions
type: WebPage
description: Commercial principles, engagement shapes, assumptions, and scope boundaries for LongTermIntelligence.com services.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-08-01
date modified: 2026-08-01
keywords: agentic AI consulting pricing
keywords: machine intelligence engagement cost
keywords: AI architecture commercial model
about: pricing
about: services
main entity: How is LongTermIntelligence.com work commercially structured?

How should enterprises scale machine intelligence and AI agents?
type: Question
accepted answer: Answer
  text: Enterprises should scale through shared architecture, identity, evaluation, evidence, and authority standards while leaving business units accountable for process outcomes. Autonomy expands only after a narrower tier demonstrates value and control.
is part of: Enterprise machine intelligence that can scale without losing control

Enterprise machine intelligence that can scale without losing control
type: WebPage
description: Architecture, control-plane, evaluation, and operating support for enterprise agent portfolios and multi-agent workflows.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-08-01
date modified: 2026-08-01
keywords: enterprise machine intelligence
keywords: enterprise agentic AI governance
keywords: multi-agent enterprise architecture
about: enterprise
about: portfolio governance
about: control planes
main entity: How should enterprises scale machine intelligence and AI agents?

How can government and regulated organizations use machine intelligence responsibly?
type: Question
accepted answer: Answer
  text: Start with bounded decision support or controlled workflow assistance, map the applicable mission and review requirements, preserve human authority, retain evidence, and separate engineering alignment from claims of certification or legal compliance.
is part of: Bounded machine intelligence for government and regulated environments

Bounded machine intelligence for government and regulated environments
type: WebPage
description: Specialist architecture, evaluation, control-plane, and evidence support for government teams, prime contractors, and regulated programs.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-08-01
date modified: 2026-08-01
keywords: government agentic AI
keywords: public sector multi-agent systems
keywords: regulated AI control plane
about: government
about: regulated systems
about: evidence
main entity: How can government and regulated organizations use machine intelligence responsibly?

How can LongTermIntelligence.com work with systems integrators and delivery partners?
type: Question
accepted answer: Answer
  text: LongTermIntelligence.com can own a specialized workstream in machine-intelligence architecture, agentic control, evaluation, evidence, or operations while the prime or platform partner retains the broader client, transformation, and integration mandate.
is part of: Partner delivery for governed machine intelligence and agentic AI

Partner delivery for governed machine intelligence and agentic AI
type: WebPage
description: A co-delivery model for agentic swarm architecture, evaluation, control planes, and technical evidence workstreams.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-08-01
date modified: 2026-08-01
keywords: agentic AI delivery partner
keywords: multi-agent subcontractor
keywords: AI control plane specialist
about: partners
about: teaming
about: delivery
main entity: How can LongTermIntelligence.com work with systems integrators and delivery partners?

What must be true before an AI agent may access enterprise data or tools?
type: Question
accepted answer: Answer
  text: The agent must have an accountable owner, bounded purpose, task-scoped identity, approved data and tools, enforceable policy, representative evaluation, complete telemetry, a human authority model, incident controls, and evidence supporting the claimed level of autonomy.
is part of: Security, authority, data, and operating boundaries for agentic systems

Security, authority, data, and operating boundaries for agentic systems
type: WebPage
description: A practical trust model for machine intelligence systems: identity, data, tools, policy, human authority, observability, and incident response.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-08-01
date modified: 2026-08-01
keywords: agentic AI trust center
keywords: AI agent security controls
keywords: multi-agent governance
about: trust
about: security
about: authority
about: data
main entity: What must be true before an AI agent may access enterprise data or tools?

What proof is needed for a machine-intelligence decision?
type: Question
accepted answer: Answer
  text: Proof should connect a bounded claim to a representative scenario, a reproducible run, independent review, limitations, and an explicit decision. A demonstration is not production evidence, and a projected benefit is not a realized outcome.
is part of: Claim → Scenario → Run → Review → Decision

Claim → Scenario → Run → Review → Decision
type: WebPage
description: The LongTermIntelligence.com proof model links machine intelligence claims to representative scenarios, observed runs, review, and explicit decisions.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-08-01
date modified: 2026-08-01
keywords: AI evidence model
keywords: agentic AI proof
keywords: multi-agent evaluation evidence
about: proof
about: evaluation
about: evidence
main entity: What proof is needed for a machine-intelligence decision?

What evidence should an agentic system retain?
type: Question
accepted answer: Answer
  text: Retain system identity, versions, inputs and sources, state transitions, model and tool calls, policy decisions, approvals, evaluation results, cost, incidents, outcomes, and limitations—subject to privacy, security, retention, and access controls.
is part of: Evidence for machine intelligence decisions

Evidence for machine intelligence decisions
type: WebPage
description: A structured evidence library model for architecture, evaluation, authority, control, incidents, releases, and operations of agentic systems.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-08-01
date modified: 2026-08-01
keywords: agentic AI evidence library
keywords: multi-agent system documentation
keywords: AI decision records
about: evidence
about: documentation
about: operations
main entity: What evidence should an agentic system retain?

What should a team prepare before a machine-intelligence review?
type: Question
accepted answer: Answer
  text: Prepare the process map, baseline, representative cases, system and data flows, current models or agents, tool and identity inventory, authority limits, risks, prior evaluation, incidents, costs, and the decision that must be made.
is part of: Working resources for machine intelligence and agentic swarm decisions

Working resources for machine intelligence and agentic swarm decisions
type: WebPage
description: Practical checklists and discussion guides for readiness, swarm architecture, control planes, evaluation, authority, and operations.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-08-01
date modified: 2026-08-01
keywords: agentic AI checklist
keywords: AI swarm architecture checklist
keywords: machine intelligence governance resources
about: resources
about: checklists
about: architecture
main entity: What should a team prepare before a machine-intelligence review?

What are Machine Intelligence Field Notes?
type: Question
accepted answer: Answer
  text: Machine Intelligence Field Notes are reviewed, source-aware articles about the architecture, governance, evaluation, security, observability, memory, and operating realities of enterprise AI agents and multi-agent systems. They emphasize durable engineering decisions rather than model-release news or unsupported predictions.
is part of: Operating guidance for durable machine intelligence

Operating guidance for durable machine intelligence
type: WebPage
description: Research and practical guidance on agentic swarms, control planes, multi-agent evaluation, memory, authority, and operations.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-08-01
date modified: 2026-08-01
keywords: machine intelligence insights
keywords: agentic swarm management articles
keywords: AI control plane guidance
about: insights
about: architecture
about: operations
main entity: What are Machine Intelligence Field Notes?

What makes machine intelligence durable?
type: Question
accepted answer: Answer
  text: Durable machine intelligence remains useful, governable, understandable, portable, recoverable, and accountable as models, vendors, data, people, and requirements change.
is part of: The Long-Term Intelligence Framework for governed AI systems

The Long-Term Intelligence Framework for governed AI systems
type: WebPage
description: A six-dimension framework for durable machine intelligence architecture, agentic swarm operations, and human authority.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-08-01
date modified: 2026-08-01
keywords: long-term intelligence framework
keywords: durable agentic AI
keywords: machine intelligence governance framework
about: framework
about: governance
about: architecture
main entity: What makes machine intelligence durable?

What is LongTermIntelligence.com?
type: Question
accepted answer: Answer
  text: LongTermIntelligence.com is a focused machine-intelligence architecture and operations practice for enterprise AI, agentic systems, multi-agent control, evaluation, security, human authority, and evidence-backed scale decisions.
is part of: About LongTermIntelligence.com

About LongTermIntelligence.com
type: AboutPage
description: About LongTermIntelligence.com and its principal-led approach to machine intelligence architecture, agentic swarms, evaluation, and operations.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-08-01
date modified: 2026-08-01
keywords: LongTermIntelligence.com
keywords: machine intelligence consultant
keywords: agentic swarm architect
about: about
about: practice
main entity: What is LongTermIntelligence.com?

How should an organization contact LongTermIntelligence.com?
type: Question
accepted answer: Answer
  text: Begin with public-safe context about the workflow, blocked decision, current architecture, authority limits, available evidence, stakeholders, and timing. Do not send credentials, regulated data, confidential source code, or sensitive production records before scope and handling terms are established.
is part of: Start with public-safe context

Start with public-safe context
type: ContactPage
description: Contact LongTermIntelligence.com about machine intelligence architecture, agentic swarm management, evaluation, control planes, or partner workstreams.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-08-01
date modified: 2026-08-01
keywords: contact LongTermIntelligence.com
keywords: agentic AI consultation
keywords: machine intelligence architecture inquiry
about: contact
main entity: How should an organization contact LongTermIntelligence.com?

What specialized capability does LongTermIntelligence.com provide?
type: Question
accepted answer: Answer
  text: The practice provides vendor-aware but buyer-side machine-intelligence architecture, agentic swarm design, control-plane implementation, evaluation, observability, memory, governance, implementation rescue, and recurring operations support.
is part of: Machine intelligence and agentic swarm capability statement

Machine intelligence and agentic swarm capability statement
type: WebPage
description: A concise capability statement for buyers, prime contractors, partners, and technical evaluators considering LongTermIntelligence.com.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-08-01
date modified: 2026-08-01
keywords: agentic AI capability statement
keywords: machine intelligence capability statement
keywords: multi-agent subcontractor
about: capability statement
about: partners
about: government
main entity: What specialized capability does LongTermIntelligence.com provide?

What data does the packaged LongTermIntelligence.com theme collect?
type: Question
accepted answer: Answer
  text: The packaged theme does not bundle analytics, advertising trackers, remote fonts, or third-party JavaScript. Its public-site search runs in the browser against a local index. A deployed WordPress site may collect additional data through hosting, logs, forms, plugins, backups, or services added by the operator.
is part of: A public site designed to collect less

A public site designed to collect less
type: WebPage
description: Privacy approach for the LongTermIntelligence.com WordPress theme, local search, direct contact, and optional first-party intake.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-08-01
date modified: 2026-08-01
keywords: LongTermIntelligence.com privacy
keywords: privacy-first WordPress AI site
about: privacy
about: trust
main entity: What data does the packaged LongTermIntelligence.com theme collect?

How does the LongTermIntelligence.com theme support accessibility?
type: Question
accepted answer: Answer
  text: The theme uses semantic landmarks, keyboard-operable navigation and dialogs, visible focus, touch-sized controls, responsive reflow, zoom-friendly layouts, reduced-motion support, and system fonts. Accessibility still depends on the content, plugins, and configuration added by the site operator.
is part of: Accessibility is part of technical quality

Accessibility is part of technical quality
type: WebPage
description: Accessibility practices for the LongTermIntelligence.com theme, including semantic HTML, keyboard access, reflow, zoom, reduced motion, and content responsibilities.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-08-01
date modified: 2026-08-01
keywords: LongTermIntelligence.com accessibility
keywords: accessible WordPress AI theme
about: accessibility
about: quality
main entity: How does the LongTermIntelligence.com theme support accessibility?

Does LongTermIntelligence.com certify AI systems?
type: Question
accepted answer: Answer
  text: No. The theme describes engineering and evidence alignment support. Certification, legal compliance, audit opinions, and regulatory conclusions require the appropriate accredited bodies, qualified counsel, and validated organizational controls.
is part of: Engineering alignment support—not certification

Engineering alignment support—not certification
type: WebPage
description: How LongTermIntelligence.com maps machine intelligence architecture and evidence to client-selected risk, security, quality, and management frameworks.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-07-31
date modified: 2026-08-01
keywords: AI standards alignment
keywords: agentic AI governance framework mapping
keywords: machine intelligence controls evidence
about: standards
about: evidence
about: governance
main entity: Does LongTermIntelligence.com certify AI systems?

What principles constrain LongTermIntelligence.com engineering?
type: Question
accepted answer: Answer
  text: The practice favors bounded authority, explicit state, least agency, inspectable evidence, reversible execution, representative evaluation, provider portability, honest limitations, and human accountability over claims of unconstrained autonomy.
is part of: Engineering principles for machine intelligence systems

Engineering principles for machine intelligence systems
type: WebPage
description: The LongTermIntelligence.com engineering manifesto for architecture, authority, evaluation, evidence, recovery, and durable organizational capability.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-08-01
date modified: 2026-08-01
keywords: agentic AI engineering principles
keywords: machine intelligence manifesto
keywords: multi-agent system design principles
about: engineering
about: principles
about: architecture
main entity: What principles constrain LongTermIntelligence.com engineering?

Human-readable route index
type: WebPage
description: A human-readable index of LongTermIntelligence.com services, capabilities, buyer paths, evidence, resources, company, and policy pages.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-08-01
date modified: 2026-08-01
keywords: LongTermIntelligence.com site map
about: site map

What is machine intelligence?
type: Question
accepted answer: Answer
  text: Machine intelligence is the full operational system around intelligent behavior: models, deterministic software, data, tools, memory, policy, evaluation, and accountable people. It is broader than a model or chatbot because it includes how work is authorized, observed, recovered, and improved.
is part of: Machine intelligence architecture for dependable enterprise operations

Machine intelligence architecture for dependable enterprise operations
type: TechArticle
description: What machine intelligence means, how it differs from an AI model, and the architecture required to make intelligent systems governable, observable, and useful.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-08-01
date modified: 2026-08-01
keywords: machine intelligence
keywords: machine intelligence architecture
keywords: enterprise AI architecture
keywords: AI operating system
keywords: production AI
about: machine intelligence
about: enterprise architecture
about: AI operations
author: LongTermIntelligence.com
main entity: What is machine intelligence?

What is agentic AI?
type: Question
accepted answer: Answer
  text: Agentic AI can pursue a defined objective across multiple steps, choose among approved actions, use tools, preserve state, and adapt its next step. Enterprise use requires bounded permissions, evaluation, observability, and human authority.
is part of: Agentic AI: bounded agency, tools, state, and enterprise control

Agentic AI: bounded agency, tools, state, and enterprise control
type: TechArticle
description: A practical enterprise definition of agentic AI, its architecture, where it creates value, and the controls required before AI agents take action.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-08-01
date modified: 2026-08-01
keywords: agentic AI
keywords: AI agents
keywords: enterprise AI agents
keywords: autonomous AI agents
keywords: agentic systems
about: agentic AI
about: AI agents
about: enterprise systems
author: LongTermIntelligence.com
main entity: What is agentic AI?

What is agentic swarm management?
type: Question
accepted answer: Answer
  text: Agentic swarm management is the discipline of coordinating multiple specialized AI agents while controlling their roles, task handoffs, memory, tools, identity, budgets, evaluation, failure recovery, and escalation to people.
is part of: Agentic swarm management for coordinated, governed AI work

Agentic swarm management for coordinated, governed AI work
type: TechArticle
description: How to design and manage agent swarms with specialized roles, bounded delegation, shared state, evaluation, observability, human authority, and failure recovery.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-08-01
date modified: 2026-08-01
keywords: agentic swarm management
keywords: AI agent swarm
keywords: multi-agent orchestration
keywords: AI swarm orchestration
keywords: agent teams
about: agentic swarms
about: multi-agent orchestration
about: swarm operations
author: LongTermIntelligence.com
main entity: What is agentic swarm management?

What is a multi-agent system?
type: Question
accepted answer: Answer
  text: A system in which two or more specialized agents coordinate through defined roles, messages, shared state, handoffs, and completion rules.
is part of: Multi-agent systems: architecture, coordination, and enterprise readiness

Multi-agent systems: architecture, coordination, and enterprise readiness
type: TechArticle
description: A decision guide to multi-agent system roles, topology, handoffs, shared state, orchestration, evaluation, and when a single agent is the better design.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-08-01
date modified: 2026-08-01
keywords: multi-agent systems
keywords: multi-agent architecture
keywords: AI agent orchestration
keywords: agent topology
keywords: multi-agent workflow
about: multi-agent systems
about: agent architecture
about: orchestration
author: LongTermIntelligence.com
main entity: What is a multi-agent system?

What is an agentic control plane?
type: Question
accepted answer: Answer
  text: An agentic control plane is the governing layer between agents and enterprise systems. It manages identity, permissions, tool access, state, model routing, budgets, policy checks, approvals, telemetry, evaluation, and emergency shutdown.
is part of: Agentic control plane: policy, identity, evidence, and safe execution

Agentic control plane: policy, identity, evidence, and safe execution
type: TechArticle
description: What an agentic control plane does, how it differs from orchestration, and the capabilities required to govern AI agents across models, tools, data, and workflows.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-08-01
date modified: 2026-08-01
keywords: agentic control plane
keywords: AI agent control plane
keywords: enterprise AI control plane
keywords: agent governance platform
keywords: AI orchestration control plane
about: agentic control plane
about: policy
about: identity
about: AI operations
author: LongTermIntelligence.com
main entity: What is an agentic control plane?

What is governed autonomy?
type: Question
accepted answer: Answer
  text: Governed autonomy allows an AI system to act independently only inside enforceable limits. Its actions remain observable, attributable, reviewable, and reversible, with explicit points where human authority takes over.
is part of: Governed autonomy: machine action inside explicit human authority

Governed autonomy: machine action inside explicit human authority
type: TechArticle
description: How governed autonomy combines bounded machine action with enforceable policy, evidence, reversibility, risk tiers, and named human accountability.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-08-01
date modified: 2026-08-01
keywords: governed autonomy
keywords: responsible agentic AI
keywords: human oversight AI
keywords: bounded autonomy
keywords: AI authority model
about: governed autonomy
about: human authority
about: AI governance
author: LongTermIntelligence.com
main entity: What is governed autonomy?

What does agentic AI governance include?
type: Question
accepted answer: Answer
  text: Agentic AI governance includes ownership, purpose, inventory, risk tiering, data and tool permissions, evaluation thresholds, human authority, incident response, change control, monitoring, evidence retention, and retirement.
is part of: Agentic AI governance across the full system lifecycle

Agentic AI governance across the full system lifecycle
type: TechArticle
description: A practical governance model for AI agents covering ownership, inventory, risk, authority, evaluation, change control, incidents, evidence, and retirement.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-08-01
date modified: 2026-08-01
keywords: agentic AI governance
keywords: AI agent governance
keywords: enterprise AI governance
keywords: AI governance framework
keywords: AI agent risk management
about: AI governance
about: agent lifecycle
about: risk management
author: LongTermIntelligence.com
main entity: What does agentic AI governance include?

How should AI agents be evaluated?
type: Question
accepted answer: Answer
  text: Evaluate agents with representative scenarios and explicit rubrics covering task quality, plan adherence, tool selection, data use, policy compliance, coordination, latency, cost, and recovery. Re-run evaluations when models, prompts, tools, data, or policies change.
is part of: AI agent evaluation and release evidence for production systems

AI agent evaluation and release evidence for production systems
type: TechArticle
description: How to evaluate AI agents and multi-agent systems across task quality, trajectories, tools, safety, cost, coordination, and recovery before and after release.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-08-01
date modified: 2026-08-01
keywords: AI agent evaluation
keywords: agent evaluation framework
keywords: LLM agent evaluation
keywords: multi-agent evaluation
keywords: AI release gates
about: agent evaluation
about: release gates
about: AI assurance
author: LongTermIntelligence.com
main entity: How should AI agents be evaluated?

What is agent observability?
type: Question
accepted answer: Answer
  text: Agent observability is the ability to inspect and reconstruct what an agent perceived, planned, called, changed, spent, escalated, and produced. It requires step-level traces rather than only uptime or final-output monitoring.
is part of: Agent observability for multi-step AI behavior and operations

Agent observability for multi-step AI behavior and operations
type: TechArticle
description: A practical guide to agent traces, state, tool calls, policy decisions, cost, errors, evidence, and forensic reconstruction in enterprise AI systems.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-08-01
date modified: 2026-08-01
keywords: agent observability
keywords: AI agent monitoring
keywords: LLM observability
keywords: multi-agent tracing
keywords: agent operations
about: agent observability
about: telemetry
about: AI operations
author: LongTermIntelligence.com
main entity: What is agent observability?

How does memory work in multi-agent systems?
type: Question
accepted answer: Answer
  text: Reliable agent memory separates short-lived task state, durable facts, event history, and reusable knowledge. Each layer needs provenance, access controls, retention rules, conflict handling, and tests that prevent stale or poisoned context from spreading.
is part of: AI agent memory and context architecture for multi-agent systems

AI agent memory and context architecture for multi-agent systems
type: TechArticle
description: How to design task state, durable memory, retrieval, provenance, access, retention, conflict resolution, and context isolation for AI agents and swarms.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-08-01
date modified: 2026-08-01
keywords: AI agent memory
keywords: multi-agent memory
keywords: agent context engineering
keywords: persistent agent memory
keywords: shared AI memory
about: agent memory
about: context engineering
about: knowledge systems
author: LongTermIntelligence.com
main entity: How does memory work in multi-agent systems?

What is a human authority boundary?
type: Question
accepted answer: Answer
  text: A human authority boundary identifies the point at which an agent must stop and an accountable person must review or authorize an action, especially when the action is consequential, irreversible, financially material, safety-related, or rights-impacting.
is part of: Human authority boundaries for AI agents and governed autonomy

Human authority boundaries for AI agents and governed autonomy
type: TechArticle
description: How to define the exact point where AI autonomy ends and accountable human review, approval, modification, or stop authority begins.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-08-01
date modified: 2026-08-01
keywords: human authority boundaries
keywords: human in the loop AI
keywords: human oversight AI
keywords: AI approval gates
keywords: governed autonomy
about: human authority
about: AI governance
about: approval
author: LongTermIntelligence.com
main entity: What is a human authority boundary?

Why do AI pilots fail to reach production?
type: Question
accepted answer: Answer
  text: Common blockers include unclear process economics, weak data and integration foundations, missing ownership, inadequate evaluation, security and governance gaps, unpredictable cost, and no recovery plan. The model is only one part of the production system.
is part of: AI pilot to production: architecture, evidence, and scale decisions

AI pilot to production: architecture, evidence, and scale decisions
type: TechArticle
description: A practical guide to moving AI agents from pilot to production with baselines, architecture, evaluation, authority, observability, recovery, and scale evidence.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-08-01
date modified: 2026-08-01
keywords: AI pilot to production
keywords: production AI
keywords: scale AI agents
keywords: AI production readiness
keywords: agentic AI implementation
about: pilot to production
about: AI readiness
about: scale gate
author: LongTermIntelligence.com
main entity: Why do AI pilots fail to reach production?

How can AI agents be secured?
type: Question
accepted answer: Answer
  text: Use workload identity, least agency, task-scoped credentials, approved tool catalogs, sandboxing, input and output controls, policy enforcement, complete telemetry, memory isolation, circuit breakers, incident playbooks, and human authority for consequential actions.
is part of: AI agent security: identity, least agency, tools, memory, and containment

AI agent security: identity, least agency, tools, memory, and containment
type: TechArticle
description: A defense-in-depth architecture for securing AI agents, agentic workflows, MCP connections, non-human identities, memory, generated actions, and incident recovery.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-08-01
date modified: 2026-08-01
keywords: AI agent security
keywords: agentic AI security
keywords: MCP security
keywords: non-human identity AI
keywords: secure AI agents
about: AI security
about: non-human identity
about: MCP
about: containment
author: LongTermIntelligence.com
main entity: How can AI agents be secured?

Which industries are strongest fits for governed agentic systems?
type: Question
accepted answer: Answer
  text: The strongest initial fits combine complex cross-system work, meaningful decision latency, accessible operational data, executive sponsorship, and a reason to value governance. Logistics, financial services, and insurance are priority paths; healthcare and government may require longer evidence and procurement cycles.
is part of: Industry paths for governed machine intelligence and agentic systems

Industry paths for governed machine intelligence and agentic systems
type: CollectionPage
description: Priority industry paths for agentic AI and machine intelligence in logistics, financial services, insurance, government, and regulated enterprise operations.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-08-01
date modified: 2026-08-01
keywords: agentic AI industries
keywords: enterprise AI use cases
keywords: AI agents logistics
keywords: AI agents financial services
keywords: AI agents insurance
about: industries
about: enterprise AI
about: use cases
main entity: Which industries are strongest fits for governed agentic systems?

How can agentic AI improve logistics and supply-chain operations?
type: Question
accepted answer: Answer
  text: A governed agentic system can detect an exception, gather order and carrier context, compare recovery options, calculate cost and service impact, prepare actions, and execute only within approved financial and operational limits.
is part of: Agentic AI for logistics and supply-chain exception management

Agentic AI for logistics and supply-chain exception management
type: TechArticle
description: How governed AI agents support logistics and supply-chain exception management through monitoring, alternatives, financial authority, escalation, and evidence.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-08-01
date modified: 2026-08-01
keywords: agentic AI logistics
keywords: AI agents supply chain
keywords: supply chain exception management AI
keywords: multi-agent logistics
keywords: AI logistics orchestration
about: logistics
about: supply chain
about: exception management
author: LongTermIntelligence.com
main entity: How can agentic AI improve logistics and supply-chain operations?

How can financial institutions use governed AI agents?
type: Question
accepted answer: Answer
  text: Governed agents can support compliance review, evidence assembly, case triage, research, and operations while preserving data boundaries, explainability, approval thresholds, and complete audit records.
is part of: Governed AI agents for financial services, risk, and compliance

Governed AI agents for financial services, risk, and compliance
type: TechArticle
description: Architecture and governance for financial institutions using AI agents in compliance review, case triage, evidence assembly, research, and controlled operations.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-08-01
date modified: 2026-08-01
keywords: agentic AI financial services
keywords: AI agents banking
keywords: AI compliance agents
keywords: financial services AI governance
keywords: AI audit trail
about: financial services
about: compliance
about: AI governance
author: LongTermIntelligence.com
main entity: How can financial institutions use governed AI agents?

How can insurance carriers use multi-agent systems?
type: Question
accepted answer: Answer
  text: Multi-agent systems can coordinate document intake, policy checks, fraud signals, claim triage, and adjuster support. Denials, settlements, and other consequential decisions should remain inside explicit authority and confidence boundaries.
is part of: Multi-agent AI for insurance claims and governed case operations

Multi-agent AI for insurance claims and governed case operations
type: TechArticle
description: How insurance carriers can use multi-agent systems for document intake, policy checks, claim triage, fraud signals, adjuster support, and bounded decision workflows.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-08-01
date modified: 2026-08-01
keywords: AI agents insurance
keywords: agentic AI claims
keywords: multi-agent insurance claims
keywords: AI claims triage
keywords: insurance AI governance
about: insurance
about: claims
about: case management
author: LongTermIntelligence.com
main entity: How can insurance carriers use multi-agent systems?

Which enterprise use cases are strongest for multi-agent systems?
type: Question
accepted answer: Answer
  text: Strong use cases require coordination across roles or systems, benefit from judgment under changing conditions, and have enough economic or risk value to justify evaluation and control. Supply-chain exceptions, incident response, and compliance review are priority patterns.
is part of: Enterprise agentic AI use cases selected by process fit

Enterprise agentic AI use cases selected by process fit
type: CollectionPage
description: Enterprise agentic AI use cases selected for process fit, including supply-chain exceptions, incident response, compliance review, and case operations.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-08-01
date modified: 2026-08-01
keywords: agentic AI use cases
keywords: AI agent use cases enterprise
keywords: multi-agent use cases
keywords: machine intelligence use cases
keywords: enterprise AI workflows
about: use cases
about: enterprise AI
about: process design
main entity: Which enterprise use cases are strongest for multi-agent systems?

What does an agentic supply-chain exception workflow do?
type: Question
accepted answer: Answer
  text: It monitors operational events, classifies the exception, gathers relevant order and network context, develops recovery options, compares cost and service impact, applies policy and authority limits, and records the decision and outcome.
is part of: Supply-chain exception management with governed AI agents

Supply-chain exception management with governed AI agents
type: TechArticle
description: How governed AI agents can coordinate supply-chain exceptions while preserving financial limits, approvals, observability, rollback, and accountable human decisions.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-08-01
date modified: 2026-08-01
keywords: supply chain exception management AI
keywords: AI agents logistics workflow
keywords: agentic supply chain
keywords: multi-agent supply chain
keywords: autonomous logistics exceptions
about: supply chain
about: exception management
about: agentic use case
author: LongTermIntelligence.com
main entity: What does an agentic supply-chain exception workflow do?

How can AI agents support incident response?
type: Question
accepted answer: Answer
  text: AI agents can correlate alerts, gather logs and configuration, develop hypotheses, test evidence, prepare remediation, and validate recovery. Changes to production should pass policy and human approval unless the action is narrowly pre-authorized and reversible.
is part of: AI agent incident response with human-controlled remediation

AI agent incident response with human-controlled remediation
type: TechArticle
description: A governed multi-agent pattern for incident triage, diagnosis, evidence collection, proposed remediation, approval, execution, validation, and recovery.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-08-01
date modified: 2026-08-01
keywords: AI agent incident response
keywords: agentic incident response
keywords: multi-agent remediation
keywords: AI SRE agents
keywords: AI security operations agents
about: incident response
about: resilience
about: AI operations
author: LongTermIntelligence.com
main entity: How can AI agents support incident response?

How can multi-agent systems support compliance and audit review?
type: Question
accepted answer: Answer
  text: A bounded system can collect required evidence, compare it with current approved controls or policy, identify missing or conflicting information, prepare an exception analysis, and route the case to an accountable reviewer without silently making legal or regulatory judgments.
is part of: Agentic compliance and audit review with traceable evidence

Agentic compliance and audit review with traceable evidence
type: TechArticle
description: How governed AI agents can support compliance and audit review with traceable evidence, scoped access, review thresholds, escalation, and human accountability.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-08-01
date modified: 2026-08-01
keywords: AI compliance agents
keywords: agentic compliance review
keywords: AI audit evidence
keywords: multi-agent compliance
keywords: AI governance audit
about: compliance
about: audit
about: evidence
author: LongTermIntelligence.com
main entity: How can multi-agent systems support compliance and audit review?

What is the Independent AI Scale Gate?
type: Question
accepted answer: Answer
  text: The Independent AI Scale Gate is a bounded engagement that compares process economics, non-AI alternatives, vendor and architecture choices, production readiness, evaluation results, controls, and operating ownership, then delivers an explicit scale decision with supporting evidence.
is part of: Independent AI Scale Gate

Independent AI Scale Gate
type: WebPage
description: A fixed-scope, buyer-side evaluation of one AI-enabled business process across economics, architecture, vendors, implementation, governance, and production evidence.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-08-01
date modified: 2026-08-01
keywords: independent AI assessment
keywords: AI scale gate
keywords: AI value assurance
keywords: AI production decision
keywords: AI pilot evaluation
about: independent assurance
about: scale decision
about: AI economics
main entity: What is the Independent AI Scale Gate?

What is AI implementation rescue?
type: Question
accepted answer: Answer
  text: Implementation rescue is an independent diagnosis of a stalled or underperforming AI initiative. It identifies whether the root cause is process design, data, model choice, integration, controls, user adoption, cost, or vendor delivery, then defines remediate, replace, narrow, or stop options.
is part of: AI Implementation Rescue and Independent Recovery Plan

AI Implementation Rescue and Independent Recovery Plan
type: WebPage
description: Independent diagnosis and recovery planning for stalled AI pilots, underperforming agents, escalating vendor spend, governance blocks, and production failures.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-08-01
date modified: 2026-08-01
keywords: AI implementation rescue
keywords: failed AI pilot consulting
keywords: AI project recovery
keywords: agentic AI remediation
keywords: AI vendor recovery
about: implementation rescue
about: pilot recovery
about: independent review
main entity: What is AI implementation rescue?

How should enterprises compare AI vendors, models, and agent architectures?
type: Question
accepted answer: Answer
  text: Use a client-owned evaluation harness with representative process data and explicit thresholds for quality, reliability, tool use, latency, cost, security, governance, portability, and user impact. Include conventional automation and no-AI alternatives where credible.
is part of: Vendor, Model, and Agent Architecture Evaluation

Vendor, Model, and Agent Architecture Evaluation
type: WebPage
description: Buyer-side comparison of AI models, agent frameworks, platforms, retrieval designs, automation alternatives, cost, risk, portability, and business outcomes.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-08-01
date modified: 2026-08-01
keywords: AI vendor evaluation
keywords: LLM model comparison enterprise
keywords: AI agent platform evaluation
keywords: vendor neutral AI consulting
keywords: AI architecture bake off
about: vendor evaluation
about: model evaluation
about: AI procurement
main entity: How should enterprises compare AI vendors, models, and agent architectures?

What does an AI portfolio office do?
type: Question
accepted answer: Answer
  text: An AI portfolio office governs the use-case pipeline, investment priorities, vendor choices, architecture standards, evaluation methods, evidence, risks, and scale decisions across multiple initiatives.
is part of: Machine Intelligence and AI Portfolio Office

Machine Intelligence and AI Portfolio Office
type: WebPage
description: A buyer-side AI portfolio office for use-case priorities, vendor decisions, architecture guardrails, evaluation standards, delivery review, and value evidence.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-08-01
date modified: 2026-08-01
keywords: AI portfolio office
keywords: AI center of excellence governance
keywords: fractional chief AI office
keywords: AI program governance
keywords: machine intelligence operations
about: AI portfolio
about: operating model
about: governance
main entity: What does an AI portfolio office do?

What is in the LongTermIntelligence.com research library?
type: Question
accepted answer: Answer
  text: The library combines six supplied strategy reports, reviewed topic definitions, direct-answer guidance, public architecture pages, and a machine-readable UAI memory. Source-derived claims are labeled for verification rather than converted into unsupported company proof.
is part of: Machine intelligence and agentic AI research library

Machine intelligence and agentic AI research library
type: CollectionPage
description: Research, definitions, answer-ready guidance, and operating frameworks for machine intelligence, agentic AI, multi-agent systems, and governed swarms.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-08-01
date modified: 2026-08-01
keywords: machine intelligence research
keywords: agentic AI research
keywords: AI strategy reports
keywords: agent swarm research
keywords: enterprise AI knowledge
about: research
about: machine intelligence
about: agentic AI
about: agentic swarm management
main entity: What is in the LongTermIntelligence.com research library?

How are the source reports represented?
type: Question
accepted answer: Answer
  text: Each report is included unchanged in the theme’s `/docs` directory and indexed in the UAI memory by source, heading, hash, and verification status. The Intelligence724 competitive report retains its original brand and is treated as transferable market analysis—not LongTermIntelligence.com-specific proof.
is part of: Enterprise AI strategy report library and source corpus

Enterprise AI strategy report library and source corpus
type: CollectionPage
description: Six source strategy reports covering positioning, demand generation, competition, trust, go-to-market, and editorial authority for LongTermIntelligence.com.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-08-01
date modified: 2026-08-01
keywords: enterprise AI strategy reports
keywords: AI marketing strategy
keywords: AI competitive analysis
keywords: AI trust strategy
keywords: agentic AI editorial strategy
about: strategy reports
about: source corpus
about: enterprise AI
main entity: How are the source reports represented?

Why does terminology matter in agentic AI?
type: Question
accepted answer: Answer
  text: Terms such as agent, autonomy, swarm, orchestration, memory, control plane, and human-in-the-loop are often used loosely. Precise definitions make architecture, procurement, risk, evaluation, and authority decisions testable.
is part of: Machine intelligence, agentic AI, and AI agent glossary

Machine intelligence, agentic AI, and AI agent glossary
type: WebPage
description: Reviewed definitions for machine intelligence, agentic AI, AI agents, multi-agent systems, control planes, evaluation, memory, observability, and governed autonomy.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-08-01
date modified: 2026-08-01
keywords: machine intelligence glossary
keywords: agentic AI glossary
keywords: AI agent definitions
keywords: agent swarm definition
keywords: AI control plane definition
about: glossary
about: definitions
about: machine intelligence
about: agentic AI
main entity: Why does terminology matter in agentic AI?

What is the purpose of this FAQ?
type: Question
accepted answer: Answer
  text: The FAQ provides stable, concise answers that can be read by people or retrieved by search and answer systems. Each answer avoids unsupported company performance claims and links naturally to deeper architecture or service guidance.
is part of: Machine intelligence and agentic AI frequently asked questions

Machine intelligence and agentic AI frequently asked questions
type: FAQPage
description: Direct answers about machine intelligence, agentic AI, agent swarms, control planes, governance, evaluation, observability, memory, security, and production.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-08-01
date modified: 2026-08-01
keywords: agentic AI FAQ
keywords: machine intelligence questions
keywords: AI agent FAQ
keywords: agent swarm questions
keywords: enterprise AI answers
about: FAQ
about: machine intelligence
about: agentic AI
about: agentic swarms
main entity: What is the purpose of this FAQ?

What are the LongTermIntelligence.com machine-intelligence frameworks?
type: Question
accepted answer: Answer
  text: They are public working models for structuring agentic architecture and operations. Each framework converts an ambiguous concern—readiness, authority, evaluation, risk, failure, or control-plane design—into explicit questions, artifacts, decision gates, and evidence.
is part of: Machine-intelligence frameworks for governed agentic systems

Machine-intelligence frameworks for governed agentic systems
type: WebPage
description: Practical, vendor-neutral frameworks for agentic readiness, swarm control, human authority, evaluation, risk, failure recovery, and control-plane architecture.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-07-31
date modified: 2026-08-01
keywords: machine intelligence framework
keywords: agentic AI framework
keywords: agent swarm maturity model
keywords: human authority framework
keywords: agent evaluation scorecard
keywords: AI risk register
about: machine intelligence
about: agentic systems
about: governed autonomy
about: evaluation
about: risk
main entity: What are the LongTermIntelligence.com machine-intelligence frameworks?

What is an Agent Swarm Control Maturity Model?
type: Question
accepted answer: Answer
  text: It is a structured way to assess whether a multi-agent system has repeatable roles, enforceable authority, release evidence, operational controls, and accountable ownership. The model uses four levels—Ad hoc, Defined, Managed, and Adaptive—across six domains.
is part of: Agent Swarm Control Maturity Model

Agent Swarm Control Maturity Model
type: TechArticle
description: A four-level maturity model for architecture, authority, evaluation, operations, and evidence in multi-agent and agent-swarm systems.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-07-31
date modified: 2026-08-01
keywords: agent swarm maturity model
keywords: multi-agent maturity
keywords: agentic AI maturity
keywords: swarm governance framework
about: agentic swarm management
about: multi-agent systems
about: maturity
about: governance
author: LongTermIntelligence.com
main entity: What is an Agent Swarm Control Maturity Model?

What is a human authority boundary?
type: Question
accepted answer: Answer
  text: A human authority boundary is the explicit point at which machine autonomy ends and named human authority begins. It defines what an agent may recommend, prepare, or execute; the conditions that require approval; the evidence an approver receives; and how people can override or stop the system.
is part of: Human Authority Boundary Framework

Human Authority Boundary Framework
type: TechArticle
description: A practical framework for defining where AI agents may recommend, prepare, execute, pause, escalate, or never act.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-07-31
date modified: 2026-08-01
keywords: human authority boundary
keywords: human in the loop AI
keywords: AI approval boundary
keywords: governed autonomy framework
about: human authority
about: governed autonomy
about: AI governance
about: agentic systems
author: LongTermIntelligence.com
main entity: What is a human authority boundary?

How should an enterprise evaluate AI agents?
type: Question
accepted answer: Answer
  text: Evaluate agents against representative end-to-end work with explicit acceptance thresholds. Score task quality, tool correctness, coordination, policy and authority compliance, reliability and recovery, cost and latency, and human calibration. Preserve the run evidence and tie the result to a release decision.
is part of: Agent Evaluation Scorecard

Agent Evaluation Scorecard
type: TechArticle
description: A weighted, evidence-linked scorecard for deciding whether an AI agent or multi-agent workflow should proceed, narrow, remediate, or stop.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-07-31
date modified: 2026-08-01
keywords: agent evaluation scorecard
keywords: AI agent testing
keywords: multi-agent evaluation
keywords: agent release gate
keywords: agentic AI evaluation framework
about: agent evaluation
about: release gates
about: observability
about: machine intelligence operations
author: LongTermIntelligence.com
main entity: How should an enterprise evaluate AI agents?

What belongs in a production AI-agent risk register?
type: Question
accepted answer: Answer
  text: A production agent risk register should describe the scenario, trigger, likelihood, impact, affected assets and people, preventive controls, detection signals, containment and recovery actions, owner, review cadence, and evidence. It should cover authority, identity, tools, memory, coordination, data, reliability, cost, and auditability.
is part of: Production Agent Risk Register

Production Agent Risk Register
type: TechArticle
description: A vendor-neutral risk register for identifying, owning, controlling, detecting, and responding to agentic-system failure modes.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-07-31
date modified: 2026-08-01
keywords: AI agent risk register
keywords: agentic AI risk assessment
keywords: multi-agent risk
keywords: production AI governance
keywords: agent security risk
about: agentic AI governance
about: AI-agent security
about: risk management
about: incident response
author: LongTermIntelligence.com
main entity: What belongs in a production AI-agent risk register?

What is a cascading failure in a multi-agent system?
type: Question
accepted answer: Answer
  text: A cascading failure occurs when a local error, unsupported assumption, stale state, or unauthorized action is accepted by another agent or tool and amplified across the workflow. The important unit of analysis is the boundary crossing, not only the original model output.
is part of: Multi-Agent Cascading Failure Taxonomy

Multi-Agent Cascading Failure Taxonomy
type: TechArticle
description: A practical taxonomy for recognizing how local agent errors propagate through handoffs, shared state, tools, and downstream decisions.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-07-31
date modified: 2026-08-01
keywords: multi-agent cascading failure
keywords: AI agent failure taxonomy
keywords: semantic cascade
keywords: agent handoff failure
keywords: multi-agent recovery
about: multi-agent systems
about: failure recovery
about: agent observability
about: agentic operations
author: LongTermIntelligence.com
main entity: What is a cascading failure in a multi-agent system?

What belongs in an agentic control plane?
type: Question
accepted answer: Answer
  text: An agentic control plane manages the responsibilities that must remain consistent across agents and tools: work intake, identity, policy, authority, model and tool routing, context and memory, orchestration, execution boundaries, evaluation, telemetry, budgets, incident response, and shutdown.
is part of: Agentic Control Plane Reference Architecture

Agentic Control Plane Reference Architecture
type: TechArticle
description: A vendor-neutral reference architecture for governing identity, policy, context, orchestration, execution, evaluation, evidence, and recovery across AI agents.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-07-31
date modified: 2026-08-01
keywords: agentic control plane architecture
keywords: AI agent control plane
keywords: multi-agent reference architecture
keywords: agent orchestration architecture
about: agentic control plane
about: machine intelligence architecture
about: multi-agent systems
about: AI governance
author: LongTermIntelligence.com
main entity: What belongs in an agentic control plane?

What do the LongTermIntelligence.com assessments measure?
type: Question
accepted answer: Answer
  text: The assessments measure whether a proposed or existing agentic workflow has enough business definition, architecture, authority, evaluation, operations, comparative evidence, and ownership to support the next decision. They are diagnostic aids, not certifications.
is part of: Machine-intelligence assessments

Machine-intelligence assessments
type: WebPage
description: Local, no-submission assessments for agentic readiness, pilot-to-production evidence, and vendor-neutral AI decisions.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-07-31
date modified: 2026-08-01
keywords: agentic readiness assessment
keywords: AI readiness assessment
keywords: pilot to production assessment
keywords: vendor neutrality assessment
keywords: AI maturity assessment
about: agentic readiness
about: pilot to production
about: vendor evaluation
about: evidence
main entity: What do the LongTermIntelligence.com assessments measure?

How do you assess whether an AI-agent workflow is ready?
type: Question
accepted answer: Answer
  text: Assess readiness across five independent dimensions: business and workflow, architecture and data, authority and risk, evaluation and evidence, and operations and recovery. A workflow is not ready merely because the model performs well; the surrounding operating system must be supportable and accountable.
is part of: Agentic Readiness Assessment

Agentic Readiness Assessment
type: WebPage
description: A 20-question local assessment for business fit, architecture, human authority, evaluation, evidence, operations, and recovery.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-07-31
date modified: 2026-08-01
keywords: agentic readiness assessment
keywords: AI agent readiness
keywords: machine intelligence readiness
keywords: multi-agent readiness checklist
about: agentic readiness
about: machine intelligence
about: multi-agent systems
about: governance
main entity: How do you assess whether an AI-agent workflow is ready?

What evidence should an AI pilot have before production?
type: Question
accepted answer: Answer
  text: A pilot should have a measurable baseline, representative work, controlled interfaces, explicit authority, tested failure and recovery behavior, predetermined acceptance thresholds, preserved run evidence, and named operating owners. The final output should be a go, conditional go, remediate, rebid, replace, or stop decision.
is part of: Pilot-to-Production Evidence Check

Pilot-to-Production Evidence Check
type: WebPage
description: A 12-question local check for business outcome proof, system readiness, release evidence, and operating ownership before an AI pilot scales.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-07-31
date modified: 2026-08-01
keywords: AI pilot to production checklist
keywords: AI production readiness
keywords: agentic AI pilot evidence
keywords: AI scale decision
about: pilot to production
about: evaluation
about: operations
about: AI value assurance
main entity: What evidence should an AI pilot have before production?

How can an enterprise test whether AI advice is vendor-neutral?
type: Question
accepted answer: Answer
  text: Test whether commercial relationships are disclosed, compensation is independent of selection, every alternative is evaluated against the same client-owned criteria, projected and measured results are separated, the client owns transferable artifacts, and the decision process can recommend rebid, replacement, conventional automation, or no AI.
is part of: Vendor Neutrality and AI Decision Check

Vendor Neutrality and AI Decision Check
type: WebPage
description: A 12-question local check for commercial independence, comparative evaluation, portability, exit design, and defensible AI procurement.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-07-31
date modified: 2026-08-01
keywords: vendor neutral AI consulting
keywords: AI vendor evaluation
keywords: model evaluation matrix
keywords: AI procurement checklist
keywords: vendor independence
about: vendor evaluation
about: AI procurement
about: independent assurance
about: portability
main entity: How can an enterprise test whether AI advice is vendor-neutral?

How does LongTermIntelligence.com govern public claims?
type: Question
accepted answer: Answer
  text: The site classifies claims by evidence status. Reviewed definitions and working methods may be published as such. External facts require current verification and attribution. Performance, security, compliance, partnership, and client outcome claims require direct evidence and permission. Absolute guarantees and fabricated proof are prohibited.
is part of: Claims and Evidence Policy

Claims and Evidence Policy
type: WebPage
description: The public policy for separating source recommendations, working methods, company capabilities, external facts, and client outcome claims.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-07-31
date modified: 2026-08-01
keywords: AI claims policy
keywords: AI evidence policy
keywords: responsible AI marketing claims
keywords: agentic AI proof
keywords: AI source provenance
about: trust
about: evidence
about: claims governance
about: source provenance
main entity: How does LongTermIntelligence.com govern public claims?

The Model Is Not the Operating System — what is the operational point?
type: Question
accepted answer: Answer
  text: Enterprise machine intelligence depends more on the surrounding control, context, evaluation, and operations than on a model in isolation.
is part of: The Model Is Not the Operating System

The Model Is Not the Operating System
type: BlogPosting
description: The Model Is Not the Operating System: an operational perspective on dependable enterprise machine intelligence and governed agentic systems.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-07-31
date modified: 2026-08-01
keywords: the model is not the operating system
keywords: machine intelligence operations
keywords: agentic AI architecture
about: machine intelligence
about: agentic systems
about: operations
author: LongTermIntelligence.com
main entity: The Model Is Not the Operating System — what is the operational point?

AI Agents Fail at Boundaries — what is the operational point?
type: Question
accepted answer: Answer
  text: The highest-risk failures often occur when agents hand off state, intent, authority, or outputs—not only when one model produces a wrong answer.
is part of: AI Agents Fail at Boundaries

AI Agents Fail at Boundaries
type: BlogPosting
description: AI Agents Fail at Boundaries: an operational perspective on dependable enterprise machine intelligence and governed agentic systems.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-07-31
date modified: 2026-08-01
keywords: ai agents fail at boundaries
keywords: machine intelligence operations
keywords: agentic AI architecture
about: machine intelligence
about: agentic systems
about: operations
author: LongTermIntelligence.com
main entity: AI Agents Fail at Boundaries — what is the operational point?

Context Windows Are Not Working Memory — what is the operational point?
type: Question
accepted answer: Answer
  text: A larger context window increases available input; it does not provide governed, durable, selective, or reliable memory for an enterprise agent.
is part of: Context Windows Are Not Working Memory

Context Windows Are Not Working Memory
type: BlogPosting
description: Context Windows Are Not Working Memory: an operational perspective on dependable enterprise machine intelligence and governed agentic systems.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-07-31
date modified: 2026-08-01
keywords: context windows are not working memory
keywords: machine intelligence operations
keywords: agentic AI architecture
about: machine intelligence
about: agentic systems
about: operations
author: LongTermIntelligence.com
main entity: Context Windows Are Not Working Memory — what is the operational point?

Every Agent Is a Non-Human Identity — what is the operational point?
type: Question
accepted answer: Answer
  text: An AI agent that can access data or invoke tools is an identity with delegated power, not merely a prompt or model endpoint. It needs a named owner, task-scoped credentials, least-agency limits, lifecycle controls, and reviewable activity.
is part of: Every Agent Is a Non-Human Identity

Every Agent Is a Non-Human Identity
type: BlogPosting
description: Every Agent Is a Non-Human Identity: an operational perspective on dependable enterprise machine intelligence and governed agentic systems.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-07-31
date modified: 2026-08-01
keywords: every agent is a non-human identity
keywords: machine intelligence operations
keywords: agentic AI architecture
about: machine intelligence
about: agentic systems
about: operations
author: LongTermIntelligence.com
main entity: Every Agent Is a Non-Human Identity — what is the operational point?

Human-in-the-Loop Is Not a Human Authority Model — what is the operational point?
type: Question
accepted answer: Answer
  text: A human approval step does not by itself establish accountability, useful oversight, or a safe autonomy boundary. A real authority model defines which actions a person owns, what evidence they receive, when automation must stop, and how approval, rejection, override, and escalation are recorded.
is part of: Human-in-the-Loop Is Not a Human Authority Model

Human-in-the-Loop Is Not a Human Authority Model
type: BlogPosting
description: Human-in-the-Loop Is Not a Human Authority Model: an operational perspective on dependable enterprise machine intelligence and governed agentic systems.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-07-31
date modified: 2026-08-01
keywords: human-in-the-loop is not a human authority model
keywords: machine intelligence operations
keywords: agentic AI architecture
about: machine intelligence
about: agentic systems
about: operations
author: LongTermIntelligence.com
main entity: Human-in-the-Loop Is Not a Human Authority Model — what is the operational point?

Agent Evaluation Must Be Continuous — what is the operational point?
type: Question
accepted answer: Answer
  text: Pre-release evaluation is necessary but insufficient because agent behavior depends on changing models, tools, data, memory, policies, users, and external systems.
is part of: Agent Evaluation Must Be Continuous

Agent Evaluation Must Be Continuous
type: BlogPosting
description: Agent Evaluation Must Be Continuous: an operational perspective on dependable enterprise machine intelligence and governed agentic systems.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-07-31
date modified: 2026-08-01
keywords: agent evaluation must be continuous
keywords: machine intelligence operations
keywords: agentic AI architecture
about: machine intelligence
about: agentic systems
about: operations
author: LongTermIntelligence.com
main entity: Agent Evaluation Must Be Continuous — what is the operational point?

Sandboxing Agent Execution Is Non-Negotiable — what is the operational point?
type: Question
accepted answer: Answer
  text: Reasoning should not have unrestricted authority to mutate enterprise systems. Agent-generated code and high-risk tool actions need isolated, scoped execution boundaries.
is part of: Sandboxing Agent Execution Is Non-Negotiable

Sandboxing Agent Execution Is Non-Negotiable
type: BlogPosting
description: Sandboxing Agent Execution Is Non-Negotiable: an operational perspective on dependable enterprise machine intelligence and governed agentic systems.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-07-31
date modified: 2026-08-01
keywords: sandboxing agent execution is non-negotiable
keywords: machine intelligence operations
keywords: agentic AI architecture
about: machine intelligence
about: agentic systems
about: operations
author: LongTermIntelligence.com
main entity: Sandboxing Agent Execution Is Non-Negotiable — what is the operational point?

What are the LongTermIntelligence.com comparison guides?
type: Question
accepted answer: Answer
  text: They are decision-oriented guides that compare adjacent machine-intelligence approaches by workflow fit, authority, state, coordination, evaluation, operations, cost, portability, and failure recovery. They are not vendor rankings.
is part of: Machine intelligence comparison guides for enterprise decisions

Machine intelligence comparison guides for enterprise decisions
type: CollectionPage
description: Balanced comparison guides for AI agents, workflow automation, multi-agent systems, control planes, AgentOps, LLMOps, platforms, and custom architecture.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-07-31
date modified: 2026-08-01
keywords: AI agents vs automation
keywords: single agent vs multi agent
keywords: AgentOps vs LLMOps
keywords: AI platform vs custom architecture
keywords: agent orchestration vs control plane
about: comparisons
about: machine intelligence
about: agentic AI
about: architecture decisions
main entity: What are the LongTermIntelligence.com comparison guides?

When should an enterprise use an AI agent instead of workflow automation?
type: Question
accepted answer: Answer
  text: Use workflow automation when the inputs, rules, transitions, and exceptions can be specified reliably. Consider a bounded AI agent when the work requires interpretation, planning, tool selection, or adaptation across variable inputs—and only when evaluation and authority controls can make that discretion acceptable.
is part of: AI agents vs workflow automation: when each approach fits

AI agents vs workflow automation: when each approach fits
type: TechArticle
description: Compare AI agents and deterministic workflow automation across variability, reasoning, authority, evidence, cost, integration, and failure handling.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-07-31
date modified: 2026-08-01
keywords: AI agents vs workflow automation
keywords: agentic AI vs automation
keywords: when to use AI agents
keywords: AI workflow architecture
about: AI agents
about: workflow automation
about: architecture
about: governed autonomy
author: LongTermIntelligence.com
main entity: When should an enterprise use an AI agent instead of workflow automation?

When does a multi-agent system make more sense than one AI agent?
type: Question
accepted answer: Answer
  text: Use multiple agents when the work benefits from genuinely different roles, tools, permissions, context boundaries, or independent review—and when those benefits outweigh coordination cost and failure risk. Use one bounded agent when the task can be completed with one charter, one state model, and a manageable tool set.
is part of: Single-agent vs multi-agent systems: choose coordination deliberately

Single-agent vs multi-agent systems: choose coordination deliberately
type: TechArticle
description: Compare single-agent and multi-agent architecture by specialization, context, review, state, cost, observability, and cascading-failure risk.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-07-31
date modified: 2026-08-01
keywords: single agent vs multi agent
keywords: multi-agent architecture
keywords: AI agent swarm comparison
keywords: when to use multiple agents
about: multi-agent systems
about: AI agents
about: agentic swarm management
about: architecture
author: LongTermIntelligence.com
main entity: When does a multi-agent system make more sense than one AI agent?

How is agent orchestration different from an agentic control plane?
type: Question
accepted answer: Answer
  text: Agent orchestration coordinates tasks, messages, sequencing, delegation, and retries. An agentic control plane is the broader governance and operations layer that manages identities, permissions, model and tool access, budgets, policy, evaluation, approvals, telemetry, incident controls, and shutdown across one or more orchestrators.
is part of: Agent orchestration vs an agentic control plane

Agent orchestration vs an agentic control plane
type: TechArticle
description: Understand how agent orchestration coordinates work while an agentic control plane governs identity, policy, budgets, evidence, evaluation, and shutdown.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-07-31
date modified: 2026-08-01
keywords: agent orchestration vs control plane
keywords: agentic control plane
keywords: AI orchestration governance
keywords: multi-agent control plane
about: agent orchestration
about: agentic control plane
about: AI governance
about: operations
author: LongTermIntelligence.com
main entity: How is agent orchestration different from an agentic control plane?

What is the difference between AgentOps and LLMOps?
type: Question
accepted answer: Answer
  text: LLMOps is the discipline for developing, deploying, evaluating, observing, and governing applications that use large language models. AgentOps adds the operational requirements of agents: goals, plans, state, tool calls, identities, permissions, handoffs, human authority, side effects, budgets, and incident recovery.
is part of: AgentOps vs LLMOps: operating models for systems that can act

AgentOps vs LLMOps: operating models for systems that can act
type: TechArticle
description: Compare AgentOps and LLMOps across model calls, trajectories, tools, state, identity, evaluation, authority, incidents, and business outcomes.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-07-31
date modified: 2026-08-01
keywords: AgentOps vs LLMOps
keywords: what is AgentOps
keywords: AI agent operations
keywords: LLMOps for agents
about: AgentOps
about: LLMOps
about: agent observability
about: agent evaluation
author: LongTermIntelligence.com
main entity: What is the difference between AgentOps and LLMOps?

Should an enterprise buy an agent platform or build custom agent architecture?
type: Question
accepted answer: Answer
  text: Buy or adopt a platform when its control model, integrations, evidence, economics, and roadmap fit the workload. Build or assemble custom architecture when differentiated workflow logic, security boundaries, deployment constraints, or portability requirements justify the additional engineering and operating burden. Many enterprises use a composable hybrid.
is part of: Agent platform vs custom architecture: a buyer-side comparison

Agent platform vs custom architecture: a buyer-side comparison
type: TechArticle
description: Compare agent platforms and custom agent architecture across speed, control, integration, portability, evidence, operating burden, and exit design.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-07-31
date modified: 2026-08-01
keywords: AI agent platform vs custom
keywords: build vs buy agentic AI
keywords: agent platform evaluation
keywords: vendor neutral AI architecture
about: agent platforms
about: custom architecture
about: vendor evaluation
about: AI procurement
author: LongTermIntelligence.com
main entity: Should an enterprise buy an agent platform or build custom agent architecture?

What is the LongTermIntelligence.com method library?
type: Question
accepted answer: Answer
  text: It is a collection of editable, vendor-neutral records for documenting agentic-system purpose, architecture, dependencies, authority, evaluation, access, incidents, and decisions. The templates support disciplined work; they do not certify a system or replace legal, security, or regulatory review.
is part of: Machine intelligence operating methods and editable records

Machine intelligence operating methods and editable records
type: CollectionPage
description: Practical methods for agentic system cards, AI bills of materials, incident reviews, continuous evaluation, control-plane decisions, and least-agency access.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-07-31
date modified: 2026-08-01
keywords: agentic AI methods
keywords: AI governance templates
keywords: AI system card
keywords: AI bill of materials
keywords: agent incident review
about: methods
about: agent governance
about: evidence
about: machine intelligence operations
main entity: What is the LongTermIntelligence.com method library?

What should an agentic system card contain?
type: Question
accepted answer: Answer
  text: An agentic system card should identify the workflow, purpose, accountable owners, users, agents, models, tools, data, state, permissions, authority boundaries, evaluation evidence, operating controls, known limitations, incidents, and change history. It should link to detailed artifacts rather than trying to replace them.
is part of: Agentic system card: a concise operating record

Agentic system card: a concise operating record
type: TechArticle
description: Create an agentic system card covering purpose, owners, scope, components, authority, evaluation, evidence, operations, and change history.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-07-31
date modified: 2026-08-01
keywords: agentic system card
keywords: AI system card template
keywords: AI agent documentation
keywords: agent governance record
about: agentic system card
about: AI documentation
about: governance
about: evidence
author: LongTermIntelligence.com
main entity: What should an agentic system card contain?

What belongs in an AI Bill of Materials for an agentic system?
type: Question
accepted answer: Answer
  text: An AI Bill of Materials should list the models, prompts, system instructions, datasets, retrieval sources, tools, connectors, libraries, runtimes, policies, identities, memory stores, external services, vendors, versions, owners, licenses, data flows, and integrity evidence used by the system.
is part of: AI Bill of Materials for agentic systems

AI Bill of Materials for agentic systems
type: TechArticle
description: Build an AI Bill of Materials covering models, prompts, data, tools, connectors, libraries, services, policies, identities, and suppliers.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-07-31
date modified: 2026-08-01
keywords: AI bill of materials
keywords: AIBOM template
keywords: AI supply chain inventory
keywords: agent dependency inventory
about: AI Bill of Materials
about: AI supply chain
about: security
about: governance
author: LongTermIntelligence.com
main entity: What belongs in an AI Bill of Materials for an agentic system?

How should an organization review an AI agent incident?
type: Question
accepted answer: Answer
  text: Review an agent incident by reconstructing the intended task, active versions, inputs, selected context, plan, messages, state transitions, tool calls, policy decisions, human interventions, side effects, detection, containment, recovery, and evidence gaps. Identify where the failure crossed a semantic or authority boundary and which system change will prevent recurrence.
is part of: Agent incident review for semantic and operational failures

Agent incident review for semantic and operational failures
type: TechArticle
description: Use a structured incident review to trace agent failures through state, messages, tools, authority decisions, side effects, containment, and corrective actions.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-07-31
date modified: 2026-08-01
keywords: AI agent incident review
keywords: agent postmortem template
keywords: multi-agent failure analysis
keywords: AI incident response
about: agent incidents
about: cascading failure
about: observability
about: recovery
author: LongTermIntelligence.com
main entity: How should an organization review an AI agent incident?

What should a continuous AI agent evaluation plan include?
type: Question
accepted answer: Answer
  text: A continuous agent evaluation plan should define the system and versions in scope, representative scenarios, expected outcomes, step and trajectory metrics, policy and authority checks, cost and latency measures, sampling, human review, thresholds, release and rollback rules, drift detection, incident feedback, and accountable owners.
is part of: Continuous agent evaluation plan

Continuous agent evaluation plan
type: TechArticle
description: Design a continuous evaluation plan for representative scenarios, trajectories, tool use, safety, cost, drift, sampling, release gates, and production feedback.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-07-31
date modified: 2026-08-01
keywords: continuous agent evaluation
keywords: AI agent evaluation plan
keywords: LLM agent monitoring
keywords: agent release gates
about: agent evaluation
about: continuous evaluation
about: AgentOps
about: observability
author: LongTermIntelligence.com
main entity: What should a continuous AI agent evaluation plan include?

What should an agentic control plane architecture decision record contain?
type: Question
accepted answer: Answer
  text: The record should describe the workflow and organizational context, required control capabilities, considered alternatives, selected architecture, decision drivers, identity and policy model, state and evidence design, operating ownership, trade-offs, risks, migration and exit approach, validation evidence, and conditions that require reconsideration.
is part of: Agentic control plane architecture decision record

Agentic control plane architecture decision record
type: TechArticle
description: Document the context, alternatives, decision, controls, consequences, evidence, ownership, and review triggers for an agentic control plane.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-07-31
date modified: 2026-08-01
keywords: agentic control plane ADR
keywords: AI architecture decision record
keywords: agent control plane design
keywords: AI governance architecture
about: agentic control plane
about: architecture decision record
about: AI governance
about: enterprise architecture
author: LongTermIntelligence.com
main entity: What should an agentic control plane architecture decision record contain?

What is a least-agency access review?
type: Question
accepted answer: Answer
  text: A least-agency access review determines the minimum objective, context, data, tools, permissions, duration, budget, delegation rights, and side effects an agent needs for one bounded task. It also defines prohibited actions, authority checkpoints, evidence, expiration, and revocation.
is part of: Least-agency access review for AI agents

Least-agency access review for AI agents
type: TechArticle
description: Apply least-agency principles to agent data, tools, credentials, duration, actions, side effects, delegation, and human authority.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-07-31
date modified: 2026-08-01
keywords: least agency AI agents
keywords: AI agent access review
keywords: non-human identity governance
keywords: agent least privilege
about: least agency
about: AI agent security
about: identity
about: human authority
author: LongTermIntelligence.com
main entity: What is a least-agency access review?

How does a cascading failure happen in a multi-agent system?
type: Question
accepted answer: Answer
  text: A cascade begins when one agent creates an unsupported interpretation, state, or instruction that another component accepts as valid. The error crosses handoffs, shared memory, tools, or approvals, becomes increasingly difficult to distinguish from trusted state, and produces a downstream decision or side effect.
is part of: Anatomy of a multi-agent cascade

Anatomy of a multi-agent cascade
type: BlogPosting
description: How a plausible but unsupported state can cross agent handoffs, tool calls, memory, and human review—and how semantic circuit breakers contain it.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-07-31
date modified: 2026-08-01
keywords: multi-agent cascading failure
keywords: semantic error propagation AI agents
keywords: agent circuit breaker
keywords: multi-agent failure analysis
about: multi-agent systems
about: cascading failure
about: semantic boundaries
about: agent incidents
author: LongTermIntelligence.com
main entity: How does a cascading failure happen in a multi-agent system?

What should an OpenTelemetry trace for an AI agent capture?
type: Question
accepted answer: Answer
  text: An agent trace should connect the task, active agent and workflow versions, model calls, retrieval, messages, state transitions, tool requests, authorization decisions, human interventions, costs, errors, side effects, and final business outcome under one durable correlation context.
is part of: OpenTelemetry for AI agents: trace the whole trajectory

OpenTelemetry for AI agents: trace the whole trajectory
type: BlogPosting
description: An architecture-first guide to tracing agent runs, model calls, retrieval, tool use, handoffs, policy decisions, approvals, cost, and outcomes.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-07-31
date modified: 2026-08-01
keywords: OpenTelemetry AI agents
keywords: AI agent tracing
keywords: agent observability OpenTelemetry
keywords: LLM agent telemetry
about: OpenTelemetry
about: agent observability
about: AgentOps
about: multi-agent tracing
author: LongTermIntelligence.com
main entity: What should an OpenTelemetry trace for an AI agent capture?

What are the core layers of an agentic control plane?
type: Question
accepted answer: Answer
  text: A practical agentic control plane separates experience and workflow, orchestration, policy and identity, execution and tools, state and memory, evaluation, evidence, and operations. The exact components may be centralized or federated, but their responsibilities and owners should be explicit.
is part of: The agentic control plane architecture

The agentic control plane architecture
type: BlogPosting
description: A reference architecture for governing agent identity, orchestration, tools, state, policy, evaluation, authority, evidence, budgets, incidents, and shutdown.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-07-31
date modified: 2026-08-01
keywords: agentic control plane architecture
keywords: AI agent control plane diagram
keywords: enterprise AI control plane
keywords: multi-agent governance architecture
about: agentic control plane
about: enterprise architecture
about: AI governance
about: agent operations
author: LongTermIntelligence.com
main entity: What are the core layers of an agentic control plane?

Are state machines or role-based agent teams better for production?
type: Question
accepted answer: Answer
  text: Explicit state machines are usually easier to test, trace, resume, and govern when a workflow has known stages, consequential actions, or regulatory evidence needs. Role-based teams can be useful when exploration and dynamic delegation create value. Production systems often combine explicit outer state with bounded role-based reasoning inside a step.
is part of: State machines vs role-based agent teams

State machines vs role-based agent teams
type: BlogPosting
description: Compare explicit graph and state-machine orchestration with role-based agent teams across determinism, adaptability, debugging, governance, and workflow fit.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-07-31
date modified: 2026-08-01
keywords: state machines vs role based agents
keywords: LangGraph vs agent teams architecture
keywords: AI agent state machine
keywords: multi-agent orchestration patterns
about: state machines
about: agent orchestration
about: multi-agent systems
about: architecture
author: LongTermIntelligence.com
main entity: Are state machines or role-based agent teams better for production?

What is a shadow agent and how should an enterprise govern it?
type: Question
accepted answer: Answer
  text: A shadow agent is an AI agent or agentic workflow created or operated outside the organization’s approved inventory, ownership, security, evaluation, or governance process. Govern it by discovering the identity and dependencies, containing risky access, assigning an owner, classifying the workflow, evaluating actual behavior, and migrating useful work into a sanctioned path or retiring it.
is part of: Governing shadow agents without stopping responsible experimentation

Governing shadow agents without stopping responsible experimentation
type: BlogPosting
description: Discover, inventory, classify, contain, and migrate unowned AI agents and employee-built workflows into governed enterprise pathways.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-07-31
date modified: 2026-08-01
keywords: shadow AI agents
keywords: shadow agent governance
keywords: discover AI agents enterprise
keywords: unauthorized agentic workflows
about: shadow agents
about: AI governance
about: non-human identity
about: AI portfolio
author: LongTermIntelligence.com
main entity: What is a shadow agent and how should an enterprise govern it?

How should machines use the LongTermIntelligence.com knowledge resources?
type: Question
accepted answer: Answer
  text: Use canonical HTML pages as the primary source. Use reviewed answers, definitions, official references, corrections, entities, claims, methods, and comparisons for structured retrieval. Use the UAI package only as a project-specific aid, and never treat raw strategy-report chunks as independently verified facts. Google states that AI text files and special AI markup are not required for its AI search features.
is part of: Machine-readable discovery and citation guide

Machine-readable discovery and citation guide
type: CollectionPage
description: Human-readable guide to LongTermIntelligence.com canonical pages, answer records, entity graph, claims registry, UAI memory, feeds, and source-provenance boundaries.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-07-31
date modified: 2026-08-01
keywords: LongTermIntelligence API
keywords: AI discovery files
keywords: llms.txt UAI
keywords: machine-readable AI knowledge
keywords: AI citation guide
about: discovery
about: AEO
about: GEO
about: knowledge graph
about: source provenance
main entity: How should machines use the LongTermIntelligence.com knowledge resources?

How should an enterprise use AI standards and protocols?
type: Question
accepted answer: Answer
  text: Use standards and protocols as distinct inputs to an operating system: frameworks organize risk outcomes, management-system standards define organizational disciplines, security taxonomies identify threat classes, protocols define interoperability, telemetry conventions define observability fields, and policy sources create role-specific obligations. None of them replaces architecture, ownership, testing, evidence, or qualified review.
is part of: Standards, protocols, and policy for governed machine intelligence

Standards, protocols, and policy for governed machine intelligence
type: CollectionPage
description: Official-source guides to NIST AI RMF, ISO/IEC 42001, OWASP Agentic Top 10, MCP, OpenTelemetry, the EU AI Act, and current U.S. federal AI policy.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-08-01
date modified: 2026-08-01
keywords: AI standards
keywords: NIST AI RMF
keywords: ISO 42001
keywords: OWASP agentic AI
keywords: Model Context Protocol
keywords: OpenTelemetry GenAI
keywords: EU AI Act
keywords: federal AI policy
about: standards
about: AI governance
about: agentic protocols
about: machine intelligence assurance
main entity: How should an enterprise use AI standards and protocols?

What is the NIST AI Risk Management Framework?
type: Question
accepted answer: Answer
  text: The NIST AI RMF is a voluntary framework intended to help organizations incorporate trustworthiness considerations into the design, development, use, and evaluation of AI systems. Its Core is organized around four functions—Govern, Map, Measure, and Manage—that should operate continuously rather than as a one-time checklist.
is part of: NIST AI RMF for agentic and machine-intelligence systems

NIST AI RMF for agentic and machine-intelligence systems
type: TechArticle
description: A source-aware guide to applying NIST AI RMF Govern, Map, Measure, and Manage to enterprise agentic systems without implying certification.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-08-01
date modified: 2026-08-01
keywords: NIST AI RMF agentic AI
keywords: Govern Map Measure Manage
keywords: AI risk management framework
keywords: agent governance
about: NIST AI RMF
about: AI risk
about: governance
about: agentic systems
author: LongTermIntelligence.com
main entity: What is the NIST AI Risk Management Framework?

What is NIST AI 600-1?
type: Question
accepted answer: Answer
  text: NIST AI 600-1 is the Generative Artificial Intelligence Profile for the AI RMF. It is a cross-sectoral companion resource that helps organizations identify risks that are distinctive to or intensified by generative AI and select actions aligned with their context and priorities. It is not a complete implementation plan or certification.
is part of: NIST Generative AI Profile for production agentic systems

NIST Generative AI Profile for production agentic systems
type: TechArticle
description: How NIST AI 600-1 complements the AI RMF for generative and agentic systems, with practical evidence boundaries for production use.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-08-01
date modified: 2026-08-01
keywords: NIST AI 600-1
keywords: Generative AI Profile
keywords: GenAI risk management
keywords: agentic AI risk
about: generative AI
about: NIST profile
about: risk management
about: agentic systems
author: LongTermIntelligence.com
main entity: What is NIST AI 600-1?

What is ISO/IEC 42001?
type: Question
accepted answer: Answer
  text: ISO/IEC 42001 specifies requirements and provides guidance for establishing, implementing, maintaining, and continually improving an AI management system within an organization. It addresses the management system around responsible AI development, provision, and use; it does not certify an individual model or guarantee that every output is correct or safe.
is part of: ISO/IEC 42001 and the enterprise AI management system

ISO/IEC 42001 and the enterprise AI management system
type: TechArticle
description: A practical guide to ISO/IEC 42001 AI management systems, evidence, continual improvement, and the boundary between alignment and certification.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-08-01
date modified: 2026-08-01
keywords: ISO IEC 42001 agentic AI
keywords: AI management system
keywords: AIMS
keywords: AI governance certification
about: ISO 42001
about: AI management system
about: governance
about: continual improvement
author: LongTermIntelligence.com
main entity: What is ISO/IEC 42001?

What is the OWASP Top 10 for Agentic Applications?
type: Question
accepted answer: Answer
  text: It is an OWASP GenAI Security Project taxonomy of ten major risk areas for applications in which AI agents plan, use tools, communicate, retain context, and act. The list creates a shared threat vocabulary; secure deployment still requires a scoped threat model, architecture, testing, monitoring, incident response, and evidence.
is part of: OWASP Top 10 for Agentic Applications: operating translation

OWASP Top 10 for Agentic Applications: operating translation
type: TechArticle
description: A defender-oriented translation of the OWASP Top 10 for Agentic Applications into architecture, evaluation, identity, memory, and recovery controls.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-08-01
date modified: 2026-08-01
keywords: OWASP Top 10 agentic applications
keywords: agentic AI security risks
keywords: ASI01 ASI10
keywords: AI agent threat model
about: OWASP
about: agentic security
about: AI agent security
about: threat modeling
author: LongTermIntelligence.com
main entity: What is the OWASP Top 10 for Agentic Applications?

What is the Model Context Protocol?
type: Question
accepted answer: Answer
  text: MCP is an open protocol for connecting language-model applications with external contextual capabilities. In the reviewed 2026-07-28 specification, a host manages one or more clients that communicate with servers exposing capabilities such as resources, prompts, and tools. MCP standardizes communication; it does not provide complete governance, authorization, evaluation, budgeting, recovery, or human approval.
is part of: Model Context Protocol: scope, controls, and enterprise use

Model Context Protocol: scope, controls, and enterprise use
type: TechArticle
description: A source-aware guide to the 2026-07-28 Model Context Protocol specification, host-client-server architecture, security boundaries, and what MCP does not provide.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-08-01
date modified: 2026-08-01
keywords: Model Context Protocol 2026
keywords: MCP specification
keywords: MCP security
keywords: MCP agent control plane
about: MCP
about: interoperability
about: agentic control plane
about: protocol security
author: LongTermIntelligence.com
main entity: What is the Model Context Protocol?

What does OpenTelemetry provide for GenAI systems?
type: Question
accepted answer: Answer
  text: OpenTelemetry provides vendor-neutral mechanisms and evolving semantic conventions for representing operations such as model calls, token usage, tool calls, and tool results. It helps teams collect and move telemetry across systems. It does not determine whether an answer is correct, an action is allowed, a trace may retain sensitive content, or an incident has been resolved.
is part of: OpenTelemetry for GenAI and AI agents: traces are evidence, not judgment

OpenTelemetry for GenAI and AI agents: traces are evidence, not judgment
type: TechArticle
description: How OpenTelemetry GenAI semantic conventions support model and tool observability, and where evaluation, policy, privacy, and incident design remain separate.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-08-01
date modified: 2026-08-01
keywords: OpenTelemetry GenAI
keywords: AI agent tracing
keywords: LLM observability
keywords: GenAI semantic conventions
about: OpenTelemetry
about: agent observability
about: telemetry
about: evaluation
author: LongTermIntelligence.com
main entity: What does OpenTelemetry provide for GenAI systems?

When does the EU AI Act apply to an agentic system?
type: Question
accepted answer: Answer
  text: That determination depends on the specific system, role, use, market, affected people, risk classification, and current timetable. The Commission’s overview and July 2026 AI Omnibus update should be used instead of older one-date summaries. Engineering teams should create a clear system and role record for qualified legal reviewers rather than declaring compliance from architecture alone.
is part of: EU AI Act: current timing, scope questions, and evidence boundaries

EU AI Act: current timing, scope questions, and evidence boundaries
type: TechArticle
description: A reviewed August 2026 orientation to the EU AI Act and AI Omnibus timetable for agentic systems, with clear legal-advice and certification boundaries.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-08-01
date modified: 2026-08-01
keywords: EU AI Act agentic AI
keywords: AI Omnibus 2026
keywords: high-risk AI timeline
keywords: AI Act evidence
about: EU AI Act
about: AI Omnibus
about: regulation
about: agentic systems
author: LongTermIntelligence.com
main entity: When does the EU AI Act apply to an agentic system?

Is OMB M-24-10 still the current federal agency AI memorandum?
type: Question
accepted answer: Answer
  text: No. OMB M-25-21, issued April 3, 2025, explicitly rescinds and replaces M-24-10. Public guidance for covered federal agency AI use should cite M-25-21, define the relevant scope, and avoid presenting older M-24-10 language as current policy.
is part of: Current U.S. federal agency AI policy: OMB M-25-21

Current U.S. federal agency AI policy: OMB M-25-21
type: TechArticle
description: A current source-aware guide to OMB M-25-21, which replaced M-24-10, and the evidence implications for covered federal agency AI use.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-08-01
date modified: 2026-08-01
keywords: OMB M-25-21 AI
keywords: federal AI governance
keywords: high-impact AI
keywords: M-24-10 replaced
about: federal AI policy
about: OMB M-25-21
about: government AI
about: high-impact AI
author: LongTermIntelligence.com
main entity: Is OMB M-24-10 still the current federal agency AI memorandum?

What is the LongTermIntelligence.com official source ledger?
type: Question
accepted answer: Answer
  text: It is a reviewed registry of primary sources used to support factual orientation on AI risk management, management systems, agentic security, interoperability, telemetry, law and policy, search guidance, and crawler discovery. It records source status and use boundaries so retrieval systems and human reviewers can distinguish official facts from interpretation.
is part of: Official source ledger for machine intelligence and agentic AI

Official source ledger for machine intelligence and agentic AI
type: WebPage
description: A reviewed ledger of primary NIST, ISO, OWASP, MCP, OpenTelemetry, EU, OMB, Google, OpenAI, Bing, and IndexNow sources used by LongTermIntelligence.com.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-08-01
date modified: 2026-08-01
keywords: AI source ledger
keywords: official AI standards sources
keywords: agentic AI references
keywords: machine intelligence citations
about: sources
about: provenance
about: standards
about: freshness
main entity: What is the LongTermIntelligence.com official source ledger?

Why publish a corrections register?
type: Question
accepted answer: Answer
  text: Machine-intelligence guidance ages quickly. A corrections register shows which earlier wording is superseded, clarified, or bounded; what the current language should be; when the change was recorded; and which official source supports it. This prevents retrieval systems and human readers from treating preserved strategy reports as current authoritative guidance.
is part of: Corrections and freshness register

Corrections and freshness register
type: WebPage
description: A public corrections register for superseded, clarified, and bounded machine-intelligence guidance, linked to current official sources and affected pages.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-08-01
date modified: 2026-08-01
keywords: AI corrections register
keywords: AI policy updates 2026
keywords: M-25-21 replaces M-24-10
keywords: EU AI Act Omnibus dates
about: corrections
about: freshness
about: policy
about: provenance
main entity: Why publish a corrections register?

What should an agentic AI RFP require?
type: Question
accepted answer: Answer
  text: It should define the business process and measurable outcome, system and data boundaries, agent and human roles, authority limits, identity and tool controls, model and supplier transparency, evaluation scenarios, telemetry, security, privacy, incident and recovery obligations, service ownership, cost reporting, portability, change control, and evidence-based acceptance criteria.
is part of: Agentic AI RFP requirements and acceptance evidence

Agentic AI RFP requirements and acceptance evidence
type: TechArticle
description: A buyer-side RFP method for agentic AI covering outcomes, architecture, identity, authority, evaluation, observability, acceptance, portability, and exit terms.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-08-01
date modified: 2026-08-01
keywords: agentic AI RFP template
keywords: AI procurement requirements
keywords: AI agent vendor evaluation
keywords: MCP RFP security
about: procurement
about: agentic AI
about: vendor evaluation
about: acceptance evidence
author: LongTermIntelligence.com
main entity: What should an agentic AI RFP require?

What is an AI governance evidence register?
type: Question
accepted answer: Answer
  text: It is a versioned index that links a requirement, objective, risk, or policy statement to its scope, owner, control, evidence artifact, storage location, test or review method, finding, exception, approval, and next review date. It supports inspection and change management; it does not itself establish legal compliance, certification, or control effectiveness.
is part of: AI governance evidence register

AI governance evidence register
type: TechArticle
description: An editable register linking AI governance obligations and objectives to owners, controls, artifacts, tests, findings, approvals, and review dates.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-08-01
date modified: 2026-08-01
keywords: AI governance evidence register
keywords: AI compliance evidence template
keywords: NIST AI RMF evidence
keywords: ISO 42001 evidence mapping
about: governance
about: evidence
about: NIST AI RMF
about: ISO 42001
author: LongTermIntelligence.com
main entity: What is an AI governance evidence register?

What should a source verification register contain?
type: Question
accepted answer: Answer
  text: For each claim, record the exact public wording, claim type, source URL and publisher, source authority, publication and review dates, supporting passage or summary, whether the source directly supports the claim, qualifications and conflicts, allowed use, reviewer, status, and a trigger for re-verification or correction.
is part of: Source verification register for AI content and claims

Source verification register for AI content and claims
type: TechArticle
description: An editable source-verification method for AI content, standards, policy, vendor, market, security, and outcome claims.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-08-01
date modified: 2026-08-01
keywords: AI source verification template
keywords: fact checking AI content
keywords: AI claims register
keywords: citation provenance
about: source verification
about: editorial governance
about: claims
about: provenance
author: LongTermIntelligence.com
main entity: What should a source verification register contain?

Why is MCP not a complete agentic control plane?
type: Question
accepted answer: Answer
  text: MCP standardizes communication between model applications and contextual servers. A control plane must additionally govern identity, authorization, tool policy, budgets, model routing, state, evaluation, human authority, telemetry, incidents, recovery, and change. MCP can carry some relevant interactions, but it does not make those organizational and architectural decisions.
is part of: MCP Is Not an Agentic Control Plane

MCP Is Not an Agentic Control Plane
type: BlogPosting
description: A Machine Intelligence Field Note separating Model Context Protocol interoperability from agentic governance, evaluation, authority, security, cost, and recovery.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-08-01
date modified: 2026-08-01
keywords: MCP control plane
keywords: Model Context Protocol architecture
keywords: MCP vs agentic control plane
keywords: MCP governance
about: MCP
about: agentic control plane
about: interoperability
about: governance
author: LongTermIntelligence.com
main entity: Why is MCP not a complete agentic control plane?

Why are standards not runtime controls?
type: Question
accepted answer: Answer
  text: A standard, framework, taxonomy, protocol, or policy source can define outcomes, requirements, risks, or interfaces. A runtime control is implemented behavior that permits, denies, limits, observes, escalates, recovers, or records an operation. The two should be traceable, but citing the source does not prove the control exists or works.
is part of: Standards Are Not Runtime Controls

Standards Are Not Runtime Controls
type: BlogPosting
description: Why AI standards and policies must be translated into owners, enforceable controls, evidence, evaluation gates, monitoring, exceptions, and incident response.
is part of: LongTermIntelligence.com
publisher: LongTermIntelligence.com
date published: 2026-08-01
date modified: 2026-08-01
keywords: AI standards runtime controls
keywords: NIST AI RMF implementation
keywords: ISO 42001 agentic controls
keywords: AI governance evidence
about: standards
about: runtime controls
about: AI governance
about: evidence
author: LongTermIntelligence.com
main entity: Why are standards not runtime controls?

Agent evaluation
type: DefinedTerm
description: Repeatable testing and review of task quality, tools, coordination, policy, recovery, cost, and human calibration.

Agent Evaluation Scorecard
type: CreativeWork
description: A weighted, evidence-linked release decision framework.

Agent incident review
type: CreativeWork
description: A structured review of semantic and operational agent failures.

Agent memory
type: DefinedTerm
description: Governed task state and durable context with provenance, isolation, retention, correction, and access rules.

Agent observability
type: DefinedTerm
description: Evidence that makes agent state, handoffs, tool calls, policy decisions, cost, latency, and outcomes inspectable.

Agent Swarm Control Maturity Model
type: CreativeWork
description: A four-level maturity model across six control domains.

Agentic AI
type: DefinedTerm
description: AI systems with bounded ability to plan, use tools, maintain state, and pursue goals through multiple steps.
name: AI agent
description: A bounded software actor that uses models, context, state, and tools to perform a task.
name: Artificial intelligence
description: A broad field of computational systems performing tasks associated with intelligent behavior.

Agentic AI RFP Requirements
type: CreativeWork
description: Buyer-side requirements for evaluating agentic AI architecture, evidence, authority, security, operations, portability, and exit design.

Agentic control plane
type: DefinedTerm
description: The durable layer that manages identity, policy, authority, routing, budgets, context, evidence, and shutdown across agents.

Agentic Readiness Assessment
type: CreativeWork
description: A local 20-question assessment across business, architecture, authority, evaluation, and operations.

Agentic swarm management
type: DefinedTerm
description: The operating discipline for coordinating, governing, evaluating, observing, and recovering groups of AI agents.

Agentic system card
type: CreativeWork
description: A concise version-linked record of an agentic system and its evidence boundary.

AgentOps
type: DefinedTerm
description: Operations for agent trajectories, tools, state, authority, evidence, incidents, and outcomes.
name: LLMOps
description: Lifecycle engineering and operations for applications that use large language models.

AI agent security
type: DefinedTerm
description: Security architecture for agent identities, tools, memory, execution, boundaries, telemetry, containment, and recovery.

AI Bill of Materials
type: CreativeWork
description: A dependency inventory for models, prompts, data, tools, services, policies, identities, and suppliers.

AI Governance Evidence Register
type: CreativeWork
description: A working record connecting governance obligations and control claims to owners, artifacts, tests, dates, gaps, and decisions.

Architecture decision record
type: CreativeWork
description: A durable record of architecture context, alternatives, decision, trade-offs, and review conditions.

Multi-Agent Cascading Failure Taxonomy
type: CreativeWork
description: Six classes of boundary and propagation failure.

Claims and Evidence Policy
type: CreativeWork
description: The public claim classification, provenance, and evidence policy.

Continuous agent evaluation
type: DefinedTerm
description: An operating loop connecting release evidence, production traces, incidents, and improved tests.

Agentic Control Plane Reference Architecture
type: CreativeWork
description: Seven responsibility layers for governed agentic execution.

Corrections and Freshness Register
type: Dataset
description: A public register of corrected, replaced, amended, or clarified factual language.

EU AI Act
type: Legislation
description: The European Union risk-based legal framework for artificial intelligence, as amended by the 2026 AI Omnibus.

Governed autonomy
type: DefinedTerm
description: Bounded machine action under executable policy, observation, evaluation, recovery, and explicit human accountability.

Human authority boundary
type: DefinedTerm
description: The explicit point where machine autonomy ends and named human authority begins.

Human Authority Boundary Framework
type: CreativeWork
description: A five-tier action-level authority model.

Independent AI Scale Gate
type: Service
description: A buyer-side, evidence-backed scale, remediate, replace, rebid, or stop decision for one process.

ISO/IEC 42001
type: CreativeWork
description: An international management-system standard for establishing, implementing, maintaining, and improving an AI management system.

Least agency
type: DefinedTerm
description: Limiting the objective, context, tools, duration, delegation, budget, and side effects of an agent task.

LongTermIntelligence.com
type: Organization
description: A machine-intelligence architecture and operations brand focused on governed agentic systems.

Machine intelligence
type: DefinedTerm
description: The operational system of models, deterministic software, data, tools, memory, policy, evaluation, and accountable people.

Model Context Protocol
type: DefinedTerm
description: An open protocol for connecting AI applications to contextual resources, prompts, and tools through defined client-host-server roles.

Multi-agent system
type: DefinedTerm
description: A system in which specialized agents coordinate through explicit roles, interfaces, state, and control.

NIST AI Risk Management Framework
type: CreativeWork
description: A voluntary AI risk-management framework organized around Govern, Map, Measure, and Manage.

NIST Generative AI Profile
type: CreativeWork
description: A companion NIST profile applying AI RMF considerations to generative AI risks.

Official Source Ledger
type: Dataset
description: A reviewed registry of primary official sources, dates, status, scope, and use boundaries.

OMB Memorandum M-25-21
type: CreativeWork
description: United States executive-branch agency policy for accelerating federal AI use with governance, inventory, and risk-management requirements.

OpenTelemetry agent tracing
type: DefinedTerm
description: Vendor-neutral correlation of agent, model, retrieval, tool, policy, authority, and outcome telemetry.

OpenTelemetry GenAI Semantic Conventions
type: CreativeWork
description: Vendor-neutral telemetry conventions for generative AI and agent operations.

OWASP Top 10 for Agentic Applications
type: CreativeWork
description: A community security risk taxonomy for agentic applications.

Production Agent Risk Register
type: CreativeWork
description: A scenario-control-signal-response register for production agents.

Semantic circuit breaker
type: DefinedTerm
description: A control that stops an unsupported meaning or state before it propagates to consequential action.

Shadow agent
type: DefinedTerm
description: An agentic workflow outside approved ownership, inventory, security, evaluation, or governance.

Source Verification Register
type: CreativeWork
description: A working method for recording source authority, freshness, claim scope, review status, and correction action.

Standards and Policy Library
type: CollectionPage
description: A scoped library of official frameworks, standards, protocols, observability conventions, legislation, and federal policy relevant to machine-intelligence systems.

Vendor and model evaluation
type: Service
description: Buyer-side comparison of AI vendors, models, architectures, operating cost, portability, risk, and evidence against a defined business process.

Workflow automation
type: DefinedTerm
description: Deterministic execution through explicit rules, states, and integrations.

relationship-agent-evaluation-dependson-agent-observability
type: Statement
subject of: agent-evaluation
predicate: dependsOn
object: agent-observability

relationship-agent-evaluation-scorecard-operationalizes-agent-evaluation
type: Statement
subject of: agent-evaluation-scorecard
predicate: operationalizes
object: agent-evaluation

relationship-agent-incident-review-improves-continuous-agent-evaluation
type: Statement
subject of: agent-incident-review
predicate: improves
object: continuous-agent-evaluation

relationship-agent-memory-isgovernedby-agentic-control-plane
type: Statement
subject of: agent-memory
predicate: isGovernedBy
object: agentic-control-plane

relationship-agentic-ai-uses-ai-agent
type: Statement
subject of: agentic-ai
predicate: uses
object: ai-agent

relationship-agentic-ai-rfp-requirements-supports-vendor-model-evaluation
type: Statement
subject of: agentic-ai-rfp-requirements
predicate: supports
object: vendor-model-evaluation

relationship-agentic-control-plane-governs-multi-agent-system
type: Statement
subject of: agentic-control-plane
predicate: governs
object: multi-agent-system

relationship-agentic-readiness-assessment-uses-agent-swarm-control-maturity-model
type: Statement
subject of: agentic-readiness-assessment
predicate: uses
object: agent-swarm-control-maturity-model

relationship-agentic-swarm-management-governs-multi-agent-system
type: Statement
subject of: agentic-swarm-management
predicate: governs
object: multi-agent-system

relationship-agentic-system-card-documents-agentic-ai
type: Statement
subject of: agentic-system-card
predicate: documents
object: agentic-ai

relationship-agentops-extends-llmops
type: Statement
subject of: agentops
predicate: extends
object: llmops

relationship-agentops-observes-agent-observability
type: Statement
subject of: agentops
predicate: observes
object: agent-observability

relationship-ai-bill-of-materials-documents-agentic-control-plane
type: Statement
subject of: ai-bill-of-materials
predicate: documents
object: agentic-control-plane

relationship-ai-governance-evidence-register-documents-governed-autonomy
type: Statement
subject of: ai-governance-evidence-register
predicate: documents
object: governed-autonomy

relationship-architecture-decision-record-documents-control-plane-reference-architecture
type: Statement
subject of: architecture-decision-record
predicate: documents
object: control-plane-reference-architecture

relationship-cascading-failure-taxonomy-feeds-production-agent-risk-register
type: Statement
subject of: cascading-failure-taxonomy
predicate: feeds
object: production-agent-risk-register

relationship-claims-and-evidence-policy-governsclaimsfor-longtermintelligence
type: Statement
subject of: claims-and-evidence-policy
predicate: governsClaimsFor
object: longtermintelligence

relationship-continuous-agent-evaluation-supports-agent-evaluation
type: Statement
subject of: continuous-agent-evaluation
predicate: supports
object: agent-evaluation

relationship-control-plane-reference-architecture-implements-agentic-control-plane
type: Statement
subject of: control-plane-reference-architecture
predicate: implements
object: agentic-control-plane

relationship-corrections-register-updates-official-source-ledger
type: Statement
subject of: corrections-register
predicate: updates
object: official-source-ledger

relationship-governed-autonomy-requires-human-authority-boundary
type: Statement
subject of: governed-autonomy
predicate: requires
object: human-authority-boundary

relationship-human-authority-boundary-framework-defines-human-authority-boundary
type: Statement
subject of: human-authority-boundary-framework
predicate: defines
object: human-authority-boundary

relationship-independent-ai-scale-gate-uses-agent-evaluation-scorecard
type: Statement
subject of: independent-ai-scale-gate
predicate: uses
object: agent-evaluation-scorecard

relationship-least-agency-constrains-ai-agent
type: Statement
subject of: least-agency
predicate: constrains
object: ai-agent

relationship-longtermintelligence-focuseson-machine-intelligence
type: Statement
subject of: longtermintelligence
predicate: focusesOn
object: machine-intelligence

relationship-machine-intelligence-includes-agentic-ai
type: Statement
subject of: machine-intelligence
predicate: includes
object: agentic-ai

relationship-model-context-protocol-supports-agentic-ai
type: Statement
subject of: model-context-protocol
predicate: supports
object: agentic-ai

relationship-multi-agent-system-contains-ai-agent
type: Statement
subject of: multi-agent-system
predicate: contains
object: ai-agent

relationship-nist-generative-ai-profile-extends-nist-ai-rmf
type: Statement
subject of: nist-generative-ai-profile
predicate: extends
object: nist-ai-rmf

relationship-official-source-ledger-supports-standards-and-policy
type: Statement
subject of: official-source-ledger
predicate: supports
object: standards-and-policy

relationship-opentelemetry-agent-tracing-supports-agent-observability
type: Statement
subject of: opentelemetry-agent-tracing
predicate: supports
object: agent-observability

relationship-opentelemetry-genai-observes-agentic-ai
type: Statement
subject of: opentelemetry-genai
predicate: observes
object: agentic-ai

relationship-owasp-agentic-top-10-informs-ai-agent-security
type: Statement
subject of: owasp-agentic-top-10
predicate: informs
object: ai-agent-security

relationship-production-agent-risk-register-feeds-agent-evaluation-scorecard
type: Statement
subject of: production-agent-risk-register
predicate: feeds
object: agent-evaluation-scorecard

relationship-semantic-circuit-breaker-contains-cascading-failure-taxonomy
type: Statement
subject of: semantic-circuit-breaker
predicate: contains
object: cascading-failure-taxonomy

relationship-shadow-agent-requiresgovernancefrom-agentic-control-plane
type: Statement
subject of: shadow-agent
predicate: requiresGovernanceFrom
object: agentic-control-plane

relationship-source-verification-register-documents-official-source-ledger
type: Statement
subject of: source-verification-register
predicate: documents
object: official-source-ledger

relationship-standards-and-policy-contains-eu-ai-act
type: Statement
subject of: standards-and-policy
predicate: contains
object: eu-ai-act

relationship-standards-and-policy-contains-iso-iec-42001
type: Statement
subject of: standards-and-policy
predicate: contains
object: iso-iec-42001

relationship-standards-and-policy-contains-model-context-protocol
type: Statement
subject of: standards-and-policy
predicate: contains
object: model-context-protocol

relationship-standards-and-policy-contains-nist-ai-rmf
type: Statement
subject of: standards-and-policy
predicate: contains
object: nist-ai-rmf

relationship-standards-and-policy-contains-nist-generative-ai-profile
type: Statement
subject of: standards-and-policy
predicate: contains
object: nist-generative-ai-profile

relationship-standards-and-policy-contains-omb-m25-21
type: Statement
subject of: standards-and-policy
predicate: contains
object: omb-m25-21

relationship-standards-and-policy-contains-opentelemetry-genai
type: Statement
subject of: standards-and-policy
predicate: contains
object: opentelemetry-genai

relationship-standards-and-policy-contains-owasp-agentic-top-10
type: Statement
subject of: standards-and-policy
predicate: contains
object: owasp-agentic-top-10

relationship-workflow-automation-maycombinewith-ai-agent
type: Statement
subject of: workflow-automation
predicate: mayCombineWith
object: ai-agent