What makes an effective eval harness for coding agents
A useful eval harness for coding agents measures repeatable workflows, not only prompt outputs, and keeps results tied to concrete developer tasks.
By Agent Software
Start with real workflows
An eval harness becomes useless quickly if it measures tasks no one actually cares about. Coding-agent evaluation should start from recurring developer workflows: debug a failing test, implement a small feature under constraints, update docs after code changes, or recover after a bad first plan.
The closer the scenario is to real work, the more likely the harness will catch regressions that polished demos miss.
Define pass criteria before you run the test
Agent systems can look impressive while still failing the actual requirement. That is why pass criteria matter. A good harness knows what counts as success, partial success, and failure before execution begins.
These criteria can include behavioral boundaries such as respecting file constraints, asking for clarification at the right time, running validation steps, or avoiding unsupported claims.
Observe the full workflow
Prompt output alone is not enough. For coding agents, the important evidence often appears in the sequence of actions: what files were inspected, how the plan changed after errors, whether validation happened, and whether the system stopped when it should have stopped.
A good harness treats the workflow trace as part of the evaluation surface.
Make regression comparison easy
The point of a harness is not only to score today. It is to compare today against last week, last model version, or last prompt revision. That requires stable scenarios and result storage that is good enough for side-by-side review.
Without that comparison layer, teams collect test results but still struggle to answer whether the system is improving.
Preserve room for judgment
Not every meaningful behavior compresses cleanly into a single score. Some scenarios will still need review. That is normal. The goal is not to erase judgment. The goal is to make judgment operate on repeatable evidence instead of fuzzy recollection.
That is what an effective eval harness should do for coding agents. It should narrow ambiguity, surface regressions, and keep improvement grounded in real work.
