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LinkedIn has developed a custom multi-agent AI system designed to function as production-grade code review infrastructure rather than a simple wrapper around generic models. This approach specifically targets the reduction of hallucinations and low-signal feedback by grounding reviews in organizational context, coding standards, and tribal knowledge that off-the-shelf tools miss. The primary value lies in shifting from trivial comment generation to ensuring high-fidelity, actionable insights before human reviewers engage with pull requests.

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AI Code Review at Scale: LinkedIn's Multi-Agent Approach

InfoQ22 august 20263 min de cititArticolul și perspectiva sunt în engleză.

At LinkedIn's scale, relying solely on human reviewers or simply putting an off-the-shelf AI reviewer in front of GitHub is not an effective way to manage PRs. To address this, LinkedIn engineers built a multi-agent AI code review platform that understands the organization's coding context, treats code review as production infrastructure, and minimizes hallucinations and low-signal feedback. By Sergio De Simone

At LinkedIn's scale, relying solely on human reviewers or simply putting an off-the-shelf AI reviewer in front of GitHub is not an effective way to manage PRs. To address this, LinkedIn engineers built a multi-agent AI code review platform that understands the organization’s coding context, treats code review as production infrastructure, and minimizes hallucinations and low-signal…

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