GitHub · Open Source · AI Agent · Claude · ChatGPT · GitHub Blog
First, the agent assesses the task, then drafts a plan, implements, tests, and improves if necessary
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Assumptions and inefficiencies become dependencies, and by the time you notice, you may have to fix more than the small mistake at the start.
Key facts
- Claude Sonnet 4.6 paired with Rubber Duck running GPT-5.4 achieved a resolution rate approaching Claude Opus 4.6 running alone, closing 74.7% of the performance gap between Sonnet and Opus
- The team noticed that Rubber Duck tends to help more with difficult problems, ones that span 3+ files and would normally take 70+ steps
- On these problems, Sonnet + Rubber Duck scores 3.8% higher than the Sonnet baseline, and 4.8% higher on the hardest problems identified across three trials
- When you’ve selected a Claude model from the model picker to use as your orchestrator, Rubber Duck will be GPT-5.4
Summary
When you ask a coding agent to build a data pipeline, it may not use the best structure. Today, in GitHub Copilot CLI, they're introducing Rubber Duck in experimental mode. To catch different kinds of errors, a different perspective matters. Today’s coding agents follow a clear loop. Using self-reflection and having the agent review its own output before moving forward is a proven technique.