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# HubSpot’s Sidekick: Multi-Model AI Code Review with 90% Faster Feedback and 80% Engineer Approval

**[InfoQ](https://daily.dev/sources/infoq)** · 3 min read · 0 upvotes · 0 comments

## Summary

HubSpot's engineering team built Sidekick, an internal AI-powered code review agent that analyzes pull requests using LLMs and filters feedback through a secondary 'judge agent' before posting comments to GitHub. The system migrated from a Kubernetes-based setup to a Java framework called Aviator, which integrates with HubSpot's dev platform and supports multiple model providers (Anthropic, OpenAI, Google). The judge agent pattern significantly reduced low-value comments, resulting in a 90% reduction in time-to-first-feedback and an 80% thumbs-up rate from engineers. Future plans include persistent memory for agents and expanded cross-repository context retrieval.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://www.infoq.com/news/2026/03/hubspot-ai-code-review-agent/>

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Tags: [#github](https://daily.dev/tags/github), [#java](https://daily.dev/tags/java), [#hubspot](https://daily.dev/tags/hubspot)

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