Release evidence guide
How to evaluate an AI agent before granting more authority
Evaluate an AI agent against the workflow it will operate, not only generic model benchmarks. Build cases from ordinary work, exceptions, denied actions, missing evidence, stale records, and adversarial inputs; score both task quality and boundary behavior; then grant authority only through an explicit release gate with rollback.
- Good fit
- Use workflow-specific evaluation whenever an agent reads private context, calls tools, recommends consequential actions, or moves from drafting toward external execution.
- Pause when
- A single demonstration, aggregate accuracy score, or vendor benchmark cannot establish readiness when failures have different costs or when the agent’s permissions differ from the test environment.
Signals this guide applies
- Stakeholders describe the agent as working but cannot name the cases it passed.
- A new tool, prompt, model, policy, or data source can change behavior after launch.
- The team needs a repeatable reason to expand, hold, or reduce the agent’s authority.
A bounded way to proceed
- 01
Define outcome classes
Name pass, needs-human, blocked, insufficient-evidence, tool-failure, and unknown-action outcomes. Set quality criteria for the answer and safety criteria for the path taken, including whether the agent stopped at the right boundary.
- 02
Build representative fixtures
Sample real patterns with privacy controls, preserving difficult exceptions instead of only average cases. Version inputs and expected evidence so regressions can be compared across model, prompt, policy, and tool changes.
- 03
Run blind and inspect traces
Keep expected outcomes hidden from the executing agent, score with deterministic checks where possible, and have qualified reviewers examine disputed judgments. Inspect tool calls and stops, not merely final prose.
- 04
Gate promotion and monitor drift
Tie each release state to allowed actions, required scores, unresolved risks, owner approval, and rollback. Continue sampling live receipts because a passing fixture set does not freeze provider behavior or business context.
Artifacts an operator can inspect
Versioned evaluation set
Fixtures identify source pattern, expected outcome class, required evidence, prohibited actions, privacy treatment, and why the case matters.
Promotion decision
A signed record states the tested versions, scores by case class, known misses, granted authority, monitoring plan, owner, and rollback trigger.
Regression report
Each material system change is compared against prior behavior, with new failures routed to a named owner before the release can expand authority.
Questions operators ask
How many cases are enough for an AI agent evaluation?
No fixed count proves readiness. Cover every meaningful outcome and risk class, then add cases until new samples stop revealing major behavior gaps. High-consequence or diverse workflows require broader evidence than a narrow drafting task.
Should AI agent evaluations use production data?
Use representative patterns while applying privacy, access, and retention rules. De-identification or synthetic fixtures may be appropriate, but verify that they preserve the edge conditions and source relationships the agent will actually encounter.
What should happen when an agent fails one rare case?
Assess the consequence and whether the failure reveals a general boundary weakness. Keep or reduce authority, add the case to regression coverage, fix the relevant data, policy, tool, or judgment layer, and require a new promotion decision.