Evaluation & Safety
How to Test an AI Agent Before You Trust It
Before agents are deployed into real workflows, they need testing for task success, hallucinations, tool-use accuracy, permission failures, regression risk, and human review.
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.
Build Your First Reliable AI Agent System
Move beyond AI experiments. Microcorem helps organisations design agentic workflows, retrieval systems, evaluation pipelines, and production-ready LLM applications.
