AI Agent Evaluation: How to Test Agents Before and After Production
AI agents need more than output checks. This guide covers task completion, tool use, traces, repeated runs and production monitoring.
By Maya Chen, Women in AI Editorial Fellow ยท 31 August 2026
AI agent evaluation is harder than evaluating a chatbot because the system does more than generate an answer.
An agent may select tools, plan a sequence of actions, retrieve information, modify state and decide when a task is complete. A final answer can look correct even when the process behind it was unreliable.
That changes what engineers need to test.
Start with task success
The most important question is whether the agent completed the task the user actually requested.
Define success in operational terms. Did it create the right ticket? Retrieve the correct record? Produce the requested analysis? Stop when the task required approval?
Avoid metrics that are easy to score but disconnected from the user outcome.
Evaluate tool selection
Agents fail when they choose the wrong tool, call tools in the wrong order or invent information instead of calling a tool at all.
Evaluation should therefore inspect the execution trace.
AWS guidance on agent evaluation emphasises that output-level testing can miss failures below the surface. A well-written response may still be unsupported by the tool results that preceded it.
Run scenarios more than once
Large language models are non-deterministic.
One successful run demonstrates that an agent can succeed. It does not tell you how often it succeeds.
Important scenarios should be repeated enough times to reveal variability, especially where the agent has multiple possible tools or action paths.
Test edge cases deliberately
Build evaluation sets around likely failure modes.
What happens when the retrieval tool returns nothing? When two customer records have similar names? When the user changes the objective halfway through? When an API times out? When a tool returns malformed data?
Production failures often live outside the happy path.
Evaluate process constraints
Some workflows require specific steps even when another route might produce the same answer.
A regulated process may require identity verification before an action. A support workflow may require checking account status before offering a refund.
The evaluator should test whether the agent follows those process rules.
Monitor after deployment
Evaluation does not stop at release.
Live traffic exposes new user behaviour and tool combinations that the test set did not anticipate. Production monitoring can sample agent traces and measure task success, policy violations and tool errors over time.
AWS now supports online evaluation patterns for deployed agents, reflecting the broader move toward continuous agent quality monitoring.
Build evaluation into development
Every prompt, model or tool change can alter behaviour.
A repeatable evaluation suite gives engineers evidence that the new version is better rather than simply different.
The mature approach treats agent evaluation like an engineering discipline: versioned datasets, defined metrics, trace inspection and production feedback loops.
Agents are powerful precisely because they can make decisions. The same property makes rigorous evaluation non-negotiable.