https://agent-zone.ai/skills/agentic-coding-assessment/Recent content in Agentic-Coding-Assessment on Agent ZoneHugoen-usMon, 25 May 2026 00:00:00 +0000https://agent-zone.ai/knowledge/agent-tooling/benchmarking-local-llms-for-agentic-coding/Mon, 25 May 2026 00:00:00 +0000https://agent-zone.ai/knowledge/agent-tooling/benchmarking-local-llms-for-agentic-coding/<blockquote class='book-hint '> <p><strong>Decision-first:</strong> Evaluate on the <strong>agent loop</strong> (read/edit/test/push), not one-shot patches. Use a <strong>multi-file execution-stamina</strong> task as your discriminator, tune <strong>OFAT at N≥3</strong>, and distinguish turn-ceiling vs token-ceiling vs capability-ceiling — only the last is unfixable by config.</p> </blockquote><blockquote class='book-hint '> <p><strong>Scope &amp; freshness:</strong> Methodology is durable; the named results are 2026-05 snapshots — re-run the harness for current models.</p> </blockquote><h2 id="why-public-leaderboard-scores-mislead">Why public leaderboard scores mislead<a class="anchor" href="#why-public-leaderboard-scores-mislead">#</a></h2> <p>SWE-bench-style and chat leaderboards measure something adjacent to, but not the same as, <strong>autonomous tool-using coding</strong>. A model can score well on one-shot patch generation and still fail as an agent because the agent loop demands sustained, multi-turn behavior: read files, edit several, run tests, react to failures, and <em>push</em> — without giving up, looping, or declaring &ldquo;done&rdquo; early. Evaluate on the loop you&rsquo;ll actually run.</p>