AI Agent Evaluation Template for AI Products

Run a full evaluation before every major release, after any model swap, and after significant prompt or tool changes. For high-stakes agents (those handling financial data, healthcare, or legal queries), run evaluations weekly. For lower-stakes agents, monthly is sufficient. Always run an ad-hoc evaluation after any safety incident.

For statistically meaningful results, aim for 100+ test cases per dimension. For safety testing, use at least 50 adversarial prompts. If you have production logs, sample from real user interactions to ensure your test suite reflects actual usage patterns, not just the scenarios you anticipated.

Evaluate the agent as a complete system, not individual models. If the agent routes between a fast model for simple tasks and a large model for complex ones, your test suite should include both task types. Track cost and accuracy separately for each model path so you can tune the routing logic.

It depends on the domain. For factual lookup agents (customer support, documentation search), target below 1%. For creative or brainstorming agents, 5% may be acceptable if users understand the outputs are suggestions. For any agent that takes real-world actions (sending emails, modifying data), the hallucination rate for action parameters should be 0%.

Yes, but the depth varies. For pre-launch evaluations, run structured user testing with 10-20 participants. For ongoing evaluations, rely on production satisfaction metrics (thumbs up/down, escalation rates, task completion logs). Supplement with periodic qualitative interviews every quarter.