To build evaluation harness for AI agents, start by defining the outcomes that matter most to your use case, such as task completion rate, correctness, latency, token usage, and monetary cost, then design measurable prompts and ground truth references for each scenario you want to assess. A robust evaluation harness should treat each agent run as an experiment, logging inputs, raw outputs, parsed actions, and intermediate states so you can replay decisions, attribute failures to specific steps, and compare configuration variants in a controlled way rather than relying on anecdotal observations. From the stream of Show HN projects like Perstack and Autocrit, the common pattern is a containerized harness that spins up the agent against a fixed test suite, captures full logs and API cost details, and exposes a simple API so you can integrate runs into CI pipelines without rebuilding the measurement layer each time. Practical steps include instrumenting your agent code to emit structured events, storing those events in a queryable log, normalizing metrics across models, and automating the run schedule so you can track regressions and improvements over time instead of one-off manual tests. Common mistakes to watch for are vague success criteria, missing cost and latency telemetry, leaking test data between runs, and overfitting your harness to a narrow benchmark that does not reflect real user behavior, so validate your harness against actual usage traces and iterate on scenarios as you discover edge cases. When to act or escalate depends on whether your metrics show consistent failure patterns, cost spikes, or safety concerns, at which point you should tighten guardrails, add human review checkpoints, and update your evaluation harness with new scenarios that reflect the observed failure modes before rolling out changes more broadly. Alignment with policy and governance considerations means documenting your evaluation methodology, versioning test suites, and ensuring traceability from metric to configuration change so stakeholders can trust the numbers and regulators can audit the process, as suggested in frameworks from UN UNU and industry analyses of runtime layer governance for agentic AI. Over time, treat your evaluation harness as a first class product component, iterate on metrics, visualization, and alerting, and leverage insights from community efforts like EleutherAI lm-evaluation-harness and engineering blogs on agent testing automation to refine your approach as standards and tooling evolve in 2026.
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