Agent Harness vs Eval Harness: What Each Does and How to Test Both

Agent performance depends not only on model weights but also on the agent harness that manages prompts, tools, permissions, context, execution environments, and control loops, and on the separate eval harness that sets up tasks, records trials, grades outcomes, and aggregates scores. Public Terminal-Bench 2.0 results cited in the text show substantial score variation for the same model across different harnesses, while examples of flawed graders illustrate how evaluation infrastructure can dramatically misstate capability. Agent harnesses should be tested as production software through component, permission, context, ablation, and regression testing, whereas eval harnesses require calibration through transcript review, comparison with human labels, isolated and pinned environments, and explicit reporting of unverified criteria. The text recommends recording harness versions with evaluation results, evaluating models in the intended production setup, and changing one variable at a time to identify regressions. It also presents TestMu AI Agent Assurance as a pre-alpha evaluation product that generates scenarios from agent code or requirements, runs agents through their real interfaces, and grades observable effects such as file changes, artifacts, and tool use rather than relying solely on claimed actions.

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