Evaluating Coding Agents

Definition

Evaluating Coding Agents

Evaluating coding agents means measuring both the final artifact and the process that produced it. Useful evaluation covers task success, code quality, test evidence, security, permission discipline, context use, cost, latency, robustness across repositories and quality of handoff. The unit of evaluation is often a workflow trace plus a diff, not a single model response.

Why It Matters

Evaluate more than whether a task appears completed.

Dimension	Example evidence
Task success	Acceptance tests and user-visible behaviour
Engineering quality	Review of architecture, readability and maintainability
Process quality	Appropriate exploration, tool use and adherence to instructions
Safety	No unauthorised access, secret exposure or unsafe action
Efficiency	Time, tokens, tool calls and unnecessary churn
Robustness	Success across varied repositories and ambiguous cases
Collaboration	Clear handoffs, bounded diffs and useful progress communication

Trace and provenance record task assignment, actions, results and artifact ownership. They support debugging, audit and workflow evaluation.

Failure mode

The recurring risk is treating this concept as a label rather than an engineering decision with trade-offs, evidence needs and operational consequences.

Evaluation check

Ask what observable evidence would show that this concept has been applied well in a real system, repository or learning exercise.

Related Concepts

• Evals and error analysis
• Governance and human decision boundaries

Further study: OpenAI, Evaluation best practices, and SWE-bench for software engineering agent evaluation.