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How to Build a Free Test Harness for Benchmarking Coding AI Agents

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Developers can evaluate coding agents more reliably by running a structured audit that tracks five key signals: exit code, elapsed time, modified files, agent output, and test suite results after a task. Rather than relying on model cards or demos, the approach uses a disposable Git directory and a realistic, under-specified task to measure whether an agent makes contained changes without causing collateral damage. A Python script seeds a fixture, injects the task via an environment variable, runs the agent, and returns results as JSON. The same fixture should be run both locally and on a remote server to isolate how much the environment — not the model — influences outcomes. The guide was produced in partnership with MonkeyCode, a platform offering free model access and server-side execution to support this kind of cross-runtime comparison.

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