How to evaluate an AI agent before and after go-live: a manager's guide
Evaluate an AI agent as a workflow service, not a one-off chat. An evidence loop for pre-launch testing and post-launch monitoring.
Written by the Problem Solver Team with AI assistance.
The short answer
How to evaluate an AI agent starts with the workflow, not a chatbot demo. Define users, decisions, permitted actions, and failure boundaries; test representative and misuse cases; set acceptance criteria; then monitor outcomes, exceptions, access, change, and the quality of human handoffs after go-live.
Key takeaways
- Evaluate the workflow: inputs, tools, approvals, handoffs, and recovery—not only an answer.
- Test representative and misuse scenarios that match the agent’s permissions.
- Set release criteria, then continue evaluating after launch because production changes.
- Use evidence from users and logs to improve the workflow without expanding autonomy by default.
01The short answer
How to evaluate an AI agent is not a question of whether its answers sound good. A manager needs evidence that the agent improves a defined workflow, respects decision rights, handles uncertainty safely, and can be monitored and changed responsibly after go-live.
The useful unit of evaluation is the service: inputs, instructions, tools, approvals, human handoffs, logs, and fallback. That shifts the question from “Is the model smart?” to “Can this workflow operate as intended?”
02Start with the workflow, not the agent
Write a one-page evaluation brief before testing:
- User and trigger: who uses the agent, and when?
- Decision or action: what does it prepare, recommend, or do?
- Boundaries: which information, systems, and tools can it use?
- Decision rights: which actions are read-only, proposed for approval, or permitted automatically?
- Failure boundary: what must it never do, and when must it escalate?
- Owner: who is accountable once it is live?
This prevents judging fluent output while ignoring inappropriate action. If permissions are unclear, resolve them before scoring answers. AI agent permissions and audit logs explains least-privilege access and reviewable records.
03Build an evaluation set the team recognizes
Collect normal work cases and label the expected action or escalation.
| Case type | What you are checking |
|---|---|
| Typical requests | Does it produce a useful, reviewable next step? |
| Incomplete input | Does it ask, flag uncertainty, or guess? |
| Conflicting information | Does it preserve the human decision? |
| Exceptions | Does it follow the escalation path? |
| Tool failure | Does it stop safely and offer a fallback? |
| Misuse | Does it resist prompt override and unauthorized tool use? |
For each case, record expected behavior—not just preferred prose. In many workflows, “ask for review” is correct. Keep the set under change control as new failure patterns emerge. Anthropic notes that agent outcomes can depend on a sequence of actions and changing tool state, so test the actual task path, not merely isolated prompts. Its guide to agent evals provides helpful context.
04Assess four dimensions before go-live
1. Workflow usefulness
Ask frontline users whether the result makes the next decision easier, clearer, or faster without creating hidden rework. Review samples together. Look for missing information, misleading certainty, and handoff friction. A well-labeled review set can reveal whether a narrow release is ready.
2. Reliability and safe uncertainty
Test whether the agent distinguishes what it knows from what it cannot establish, asks for needed information, and routes cases to people. Use a shared checklist of expected behavior, forbidden actions, escalation triggers, and release evidence. See AI agent acceptance criteria.
3. Security and action control
Evaluation must match actual access. A read-only research assistant and an agent that changes records have different risk profiles. Test prompt injection, instruction overrides, tool misuse, unauthorized access, data leakage, and runaway loops where relevant.
OWASP’s AI Agent Security Cheat Sheet recommends testing before production and after material changes. Pair it with a clear audit trail for consequential actions.
4. Operability
Ask: Who sees a problem? Who responds? What evidence is retained? What is the fallback? Which changes require retesting?
Evaluate before deployment and keep measurement in production. A credible launch is an operating decision, not a permanent declaration that the agent is done.
05Set the release decision in advance
| Decision | Evidence required | If not met |
|---|---|---|
| Limited release | Representative cases reviewed; owner and fallback named | Narrow scope or continue review |
| Action with approval | Approval boundary and audit record tested | Keep proposal mode |
| Expand use | Post-launch evidence supports it | Pause expansion and investigate |
A composite example: an operations manager may allow an agent to draft a service-request summary for a coordinator while keeping priority assignment and customer communication in review mode. If summaries omit a source-system warning, add that condition to the test set and correct the workflow before expanding autonomy.
06Continue after go-live
Review representative samples on a fixed rhythm. Look at reviewed outcomes and escalations; source, policy, prompt, model, or tool changes; access and audit events; integration failures and fallback use; and recurring user confusion.
A material change to a tool, model, permission, or high-impact workflow is a reason to retest. At a monthly checkpoint, confirm the workflow and owner remain right, review new exceptions, and ask whether the next increase in autonomy is justified by evidence. If not, hold scope steady.
For help designing an evaluation and rollout plan around a real workflow, talk with ProblemSolver.
07Frequently asked questions
What should managers measure when evaluating an AI agent?+
Measure whether the agent helps the intended workflow, how often it needs escalation, the quality of reviewed outputs, tool or access failures, and whether changed inputs or rules create new risks. Use measures that fit the decision, not a generic score alone, and review representative cases with the users doing the work.
Do AI agents need evaluation after launch?+
Yes. Production introduces changing source data, user behavior, integrations, policies, and model updates. Post-launch checks should review outcomes, exceptions, access events, and material changes to the agent or its tools. Update the evaluation set with relevant new failure patterns and reassess the affected workflow before scope expands.
Who should approve an AI agent for go-live?+
The accountable workflow owner should make the operating decision with input from technology, security, and the frontline work. The right group depends on the agent’s actions and consequences. The release record should name the owner, its decision boundaries, the approved scope, and the fallback when the agent cannot operate safely.
Sources and further reading
- OWASP AI Agent Security Cheat Sheet. OWASP Foundation, 2025.
- Demystifying evals for AI agents. Anthropic, 2025.