Agent Harness Testing: What a Harness Can't Verify About Itself

An agent harness comprises the model’s surrounding runtime components, including tool-calling loops, context and memory management, permissions, retries, approvals, and logs, which control agent behavior but may not establish whether actions produced correct final outcomes in connected systems. The discussion argues that model replacements, prompt changes, context policies, new tools, and retry adjustments can introduce workflow-level regressions, such as duplicate refunds after a timed-out payment request, even when individual steps are authorized and logged. It recommends testing complete agent workflows externally by executing realistic scenarios and checking resulting records, data, and downstream effects against deterministic requirements on every harness change. The text presents TestMu AI’s Agent Assurance as one such independent verification layer, intended to derive test scenarios from specifications, run workflows through real systems, and act as a CI/CD release gate alongside conventional software testing and runtime safeguards.

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