What is AI agent testing?
AI agent testing evaluates whether an AI agent reasons, uses tools and acts correctly and safely across realistic scenarios.
Agents plan steps, call tools and take actions, so testing them goes beyond checking a single response. Tests examine whether the agent chose the right tools, passed valid arguments, recovered from errors and stayed within policy.
Because outputs vary between runs, agent tests use scored evaluations over many samples and track scores across model and prompt versions.
Example
A support agent is asked to cancel an order. The test checks it looks up the order first, calls cancel_order with the right ID and does not issue a refund unless the policy allows it.
Best practices
- Build scenario sets from real user requests
- Score tool choice and arguments, not only final text
- Include adversarial and injection cases
- Compare scores before changing models or prompts
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