Agents fail in predictable ways: wrong tool selection, partial task completion, hallucinated parameters, and silent permission errors. Testing must target those failure modes, not only fluent language.
Building representative eval sets
Build eval sets from real tasks your operators perform — with expected tool calls, allowed data scopes, and clear success criteria. Run them on every material change to prompts, tools, or models.
Regression suites matter because small changes can break previously stable flows. Pair automated checks with periodic human review for high-impact actions.
Evidence over demos
Trust increases when teams can show evidence: logs, eval scores, and approval trails — not when a demo looks convincing once.