Direct answer: Evidence-based agent evaluation measures whether a coding agent produced work that can be accepted, not merely whether it produced plausible code or passed a narrow benchmark.
Evaluation dimensions
Intent satisfaction, evidence strength, shortcut risk, sensitive-area handling, and reviewer leverage.
These dimensions reveal whether an agent is safe to delegate to for real engineering work.
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Why evals need receipts
A raw pass/fail outcome hides why the agent succeeded or failed. Receipts make failures comparable and fixable.
They also help teams identify whether a model, tool, prompt, repo context, or acceptance policy needs improvement.
Operationalizing evals
Run similar tasks across agents, capture evidence reports, compare risk classes, and measure review burden. The result is a more realistic view of agent reliability.