GridArray Editorial
AI agent evaluation checklist
A checklist for evaluating multi-step agent workflows before release, covering task success, context quality, tool safety, recovery, and operator visibility.
Direct answer
Evaluate the workflow, not only the model
Agent evaluation fails when teams score isolated answers and ignore the path that produced them. For production systems, evaluation has to cover task completion, context trustworthiness, tool behavior, recovery, and whether operators can see and contain failures before customers do.
Framework
The production evaluation checklist
Use these checks as release gates for one workflow. If a critical item is unmeasured, you do not yet have evidence that the system is ready.
1. Task success
Define what a successful run means in business terms. Measure completion, correctness, and the cost of near-misses that still require human cleanup.
- Success criteria written for the workflow
- Golden scenarios covering the critical path
- Failure cases included, not only happy paths
2. Context quality
Check whether the agent retrieved or received the right context, with provenance an operator can inspect. A fluent answer from the wrong source is still a failure.
- Retrieval or context fixtures for known decisions
- Provenance present on evaluated outputs
- Missing-context behavior tested explicitly
3. Tool and action safety
Evaluate what the agent can do, not only what it can say. High-risk tools need contract tests, permission checks, and validation before irreversible effects.
- Tool contract tests for required and forbidden actions
- Permission denials verified under realistic identities
- Validation blocks incomplete or unsafe payloads
4. Recovery behavior
Inject timeouts, partial failures, and conflicting state. Confirm the system retries safely, stops when it should, and leaves durable evidence of what happened.
- Chaos or fault cases for critical integrations
- Bounded retry behavior verified
- Partial runs leave inspectable state
5. Operator visibility
If nobody can replay or explain a bad run, evaluation is incomplete. Operators need traces, statuses, and containment actions that work under pressure.
- Traces link steps, tools, and outcomes
- Replay or reconstruction is possible for reviewed runs
- Containment actions are documented and tested
6. Release gates
Wire the checklist into the release process. Prompt changes, retrieval changes, and tool changes should all face regression evaluation before they reach production traffic.
- Regression suite runs before release
- Ownership is clear for pass and fail decisions
- Known gaps are tracked as production risk, not forgotten notes
Diagnosis
Symptoms versus root causes
Symptoms
- Spot checks look fine while customer escalations keep rising.
- Prompt edits ship with no regression suite.
- Incidents turn into debates because runs cannot be reconstructed.
Root causes
- Answer-only evaluation that ignores tools and recovery.
- No release gate for retrieval or workflow changes.
- Observability that logs noise without replayable structure.
Implementation
How to implement the checklist
Build a small golden set around the workflow that matters, then expand with the failure modes you already fear. Keep evaluation close to release decisions so it changes engineering behavior instead of becoming a report nobody uses.
- Write success and failure definitions with the workflow owners.
- Create fixtures for context, tools, and recovery paths.
- Block releases when critical gates fail.
- Review evaluation gaps in the same forum as operational risk.
Boundaries
Boundaries
- This checklist does not invent benchmark scores or industry averages.
- It does not replace security review, cost control, or data readiness work.
- It assumes there is already a real multi-step workflow to evaluate.