Machine intelligence · Agentic AI · Governed swarm management

Machine-intelligence working framework

Agent Evaluation Scorecard

Evaluate the whole task system—outcome, tool use, handoffs, policy, recovery, cost, and human calibration—rather than relying on a model benchmark or a compelling demonstration.

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Published Updated Reviewed By LongTermIntelligence.com

Direct answer

How should an enterprise evaluate AI agents?

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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.

  • Use client-owned scenarios and expected outcomes.
  • Test normal, edge, adversarial, and degraded conditions.
  • Re-run the suite after model, prompt, tool, data, policy, or workflow changes.

Source basis: reviewed synthesis of the strategy corpus. Report-derived claims remain subject to the verification boundary in the source library.

Decision framework

Suggested scorecard dimensions

Weights are a starting point; a high-risk workflow may make authority or safety a hard gate rather than a weighted score.

DimensionSuggested weightQuestion
Task quality25%Did the system produce the accepted business outcome?
Tool correctness15%Were tool calls authorized, accurate, idempotent where required, and reconciled?
Coordination15%Were handoffs complete, state-consistent, and free of unresolved conflict?
Policy and authority15%Did the system obey limits and escalate at the correct boundary?
Reliability and recovery15%Could the system retry, degrade, contain, roll back, and resume safely?
Cost and latency10%Was cost and time per successful task within the operating envelope?
Human calibration5%Did reviewers agree with escalations and use overrides appropriately?

Release method

Turn evaluation into a release gate

The score is useful only when thresholds and consequences are predetermined.

  1. Define the unit of work

    State the task, outcome, side effects, time limit, and owner.

  2. Build representative scenarios

    Include typical, rare, conflicting, malicious, incomplete, and provider-failure cases.

  3. Set thresholds and hard stops

    Separate weighted quality tradeoffs from non-negotiable policy or safety failures.

  4. Run and preserve evidence

    Capture inputs, versions, traces, decisions, tool calls, approvals, costs, and outcomes.

  5. Decide and monitor

    Issue proceed, narrow, remediate, replace, or stop; then continue evaluation after release.

Downloadable working files

Download the evaluation worksheet

CSV template

Agent Evaluation Scorecard CSV

Customize weights, thresholds, evidence locations, and the release decision.

Download CSV

Templates are planning aids. They are not certifications, legal advice, security guarantees, or substitutes for client-specific validation.

Next decision

Build the test before expanding autonomy

Use one representative workflow to create the first replayable evaluation suite.

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