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arXiv AI By Celal Ziftci, Spencer Greene, Ray Liu, Livio Dalloro, Lorenzo Dini

Catching Developers in the Flow: Low-Latency Agentic Program Repair at Google Scale

Read the original on arXiv AI →

The paper introduces FlowAgent, an AI agent deployed at Google to automatically repair test failures in the pre-submit continuous integration workflow. FlowAgent uses a ReAct-style generate-and-validate loop with strict latency and quality filters, and was evaluated on 195 real-world failures with a 67.18% accuracy rate. After deployment, it suggested fixes on 295,508 changes, with developers previewing 65,069 and applying 28,554, and received positive feedback from interviews.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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