AI Agent Evaluation Harness: Test Real Workflows Before Users Do

Short summary

AI agent teams need evaluation harnesses — repeatable test systems that score entire workflows, not just final outputs, to catch production failures before users do. The pattern includes test fixtures with realistic data, sandbox tools, trace capture, and scorers checking correctness, grounding, safety, and cost. Start with 20-40 representative cases covering happy paths, edge cases, safety boundaries, and cost constraints; store traces for continuous improvement.

• •Demos differ from production: agents fail on messy data, unclear intent, and tool sequencing
• •Evaluate workflows, not just final answers, by capturing traces and checking every step
• •Concrete pattern: fixtures → runner → sandbox → trace → scorers → gate, starting with 20-40 representative test cases

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