What is it about?

When a software bug occurs in a large production system, engineers often spend hours digging through issue tickets, monitoring logs, and multiple code repositories just to pinpoint the root cause before writing a fix. Our research introduces Sherlock, an end-to-end autonomous AI agent pipeline that automates this workflow. Starting from a bug report, Sherlock traces production telemetry and stack traces to identify the responsible service, diagnoses the underlying fault, writes a code patch, and opens a pull request for human engineers to review.

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Why is it important?

Most AI coding assistants focus on isolated benchmark tasks rather than live enterprise environments where errors span dozens of interconnected services. Evaluated on 100 real production tickets across more than 120 microservices and 20 engineering teams, Sherlock autonomously resolved 41% of issues in an average of just 9 minutes—compared to a 4.2-hour manual baseline—with 68% of its generated pull requests accepted after human review. By automating routine triage and initial patching while keeping human review boundaries intact, this approach significantly reduces operational toil and accelerates incident recovery.

Perspectives

"When a production issue hits a complex microservices platform, the hardest part isn't usually writing the fix—it's connecting the dots between a vague bug ticket, noisy telemetry logs, and the exact lines of code across more than a hundred repositories. We designed Sherlock to bridge that gap while keeping engineers firmly in control through human-in-the-loop pull request reviews. Seeing the pipeline take a real production bug from an initial report to a merge-ready pull request in under ten minutes showed us how autonomous agents can take on repetitive operational toil and free developers to focus on higher-level engineering."

Uday Bhanu Prasad Kanagala

Read the Original

This page is a summary of: From Bug Report to Pull Request: An Autonomous Agent Pipeline for Production Issue Resolution, May 2026, ACM (Association for Computing Machinery),
DOI: 10.1145/3786335.3813187.
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