Why AI Coding Agents Need a Frozen Evaluation Protocol Before Testing Begins

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A DEV Community article argues that coding-agent performance metrics are only meaningful when a formal protocol file is written and locked before any agent task is executed. Without a pre-registered protocol, benchmarks risk quiet manipulation — such as adding tasks after a model performs poorly — which distorts reported scores without appearing fraudulent. The author recommends storing evaluation protocols in a version-controlled YAML file that defines dataset splits, task identifiers, timeouts, and metrics, ensuring nothing changes post-run. Tasks used during agent development must be kept in a separate split and excluded from reported scores to prevent data contamination. The article emphasizes that reproducibility depends on treating the protocol document as the primary artifact of the evaluation, not the percentage it eventually produces.

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