How to Build an Eval Harness for AI Agents

Reliable evaluations for AI agents require a harness that defines tasks with realistic starting fixtures and unambiguous, evidence-based success criteria, runs each trial in a fresh and resource-controlled environment, invokes the actual agent, and grades the resulting system state rather than relying on its textual reply. The guide presents a compact Node.js implementation that records agent claims, workspace artifacts, and tool traces, with graders returning pass, fail, or unverified when required evidence is unavailable; unverified results are excluded from pass-rate calculations rather than treated as successes. Repeated trials are emphasized as essential for measuring consistency through metrics such as pass@k, which measures whether at least one run succeeds, and pass^k, which measures whether all runs succeed, illustrated by a demo whose pass@1 of 0.43 contrasts with pass^3 of 0.03. It also recommends separating harness execution from CI policy enforcement, reporting infrastructure and observability problems independently, treating small score changes cautiously because resource conditions can significantly affect results, and continuously expanding task suites from real incidents. TestMu AI Agent Assurance is presented as a pre-alpha alternative for teams seeking managed scenario generation, execution, evidence checks, and CI reporting, while custom harnesses remain useful for specialized domain-specific scoring.

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