Practical Tutorial

AI Coding Agent Benchmarking Guide

Use this workflow to compare coding agents against approved benchmark evidence, not toy examples. ClawBench ties each result to patch quality, trace review, reruns, and repeatable scoring context.

Use Approved Coding Benchmarks

Start with approved public benchmark families that exercise coding-agent behavior and produce comparable evidence.

Step 1: Define the Task Slice

Use a mixed set: bug fixes, feature additions, refactors, and test-writing tasks. Keep prompts realistic and compare each agent inside the same approved benchmark family.

Step 2: Pick Metrics Before Running

Step 3: Run Baseline in ClawBench

Start from the live competition surface, run a clean baseline, and save the leaderboard result before tuning prompts, tools, or model settings.

Trace Review

Inspect traces to see commands, file edits, errors, retries, and recovery steps behind the score. Trace evidence separates robust coding behavior from lucky one-off patches.

Rerun Before Ranking

Rerun close results before making leaderboard or promotion decisions. A coding agent should keep patch quality and task outcomes stable under comparable benchmark conditions.

Step 4: Perform Failure Analysis

Logic errors

Correct-looking code with wrong edge-case handling.

Spec drift

Agent solves adjacent problem, not requested behavior.

Flaky fixes

Patch passes once and breaks on rerun.

Unsafe actions

Commands that mutate unrelated files or expose secrets.

Common Mistakes

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