CodeRabbit bags $143M to help companies get a grip on the explosion of AI-generated code
CodeRabbit Inc., the creator of a popular tool that automatically reviews artificial intelligence-generated code, is becoming more ambitious after closing on its latest $143 million Series C round of funding.
Alongside the round, it announced the launch of a new Agentic Change Management layer that’s meant to help companies govern, evaluate and prioritize code changes made by both humans and AI agents.
Today’s round was led by Atomico and Smash Capital and saw the participation of a host of new backers, including BMW i Ventures, Datadog Inc., Hirtle Callaghan and SineWave Ventures. Existing investors such as CRV, Scale Venture Partners, Flex Capital and Pelion Venture Partners also piled into the round, as did a number of angel investors, including senior executives from Apple Inc. and Amazon.com Inc.
Until now, CodeRabbit has only offered a fairly straightforward, yet very effective, AI-enabled code review tool that lets developers check the code generated by the AI tools they’re using in real time. Its automated platform uses AI agents to catch syntax errors, logic bugs and security vulnerabilities in freshly generated code the moment it’s created, helping developers to ensure their code meets the highest standards while maintaining the productivity gains associated with “vibe coding.”
That’s all well and good, but CodeRabbit says AI-generated code has now become so pervasive across organizations that it’s no longer enough to just review the quality of new code. Before vibe coding emerged to change everything, software engineering was a fairly slow process. Because of this, most teams managed their backlogs using tools such as Jira, assigning work to individual developers manually.
But nowadays, AI-native development means that everyone, including product managers, designers, marketers, background agents and even things like support systems and observability platforms, can automatically generate code or create pull requests. Increasingly, code is being pushed out before organizations can even hope to establish alignment, assign priorities and determine if the code should be put into production.
Companies are simply overwhelmed by a tsunami of AI-generated code, explained CodeRabbit co-founder and Chief Executive Harjot Gill. “Code changes now originate from across the software organization, including from developers, nontechnical personnel and coding agents,” he said. “Every change creates a decision for the team.”
A simple code quality review is no longer enough for teams to keep up. That’s why CodeRabbit has unveiled Agentic Change Management, an expanded control layer that’s meant to help software engineering teams govern, understand and safely ship software at much greater scale than they’ve been able to do before. According to Gill, the platform works by validating incoming pull requests using repository-wide context and sandboxed test environments to assess their impact, while its traditional AI agents continue to iron out any flaws in that code.
Gill explained that Agentic Change Management has three core capabilities, all available starting today. The first is CodeRabbit Triage, which expands the standard code review process into prioritization and routing. Essentially, what it does is score each incoming pull request based on its value, dependencies, urgency, risk and reviewer fit. In this way, it can direct high-stakes requests to human developers for review, while automatically processing low-risk tasks.
There’s also CodeRabbit Change Stack, which improves explainability by replacing traditional alphabetic file views with a visual look at a pull request’s impact on domain behavior, system dependencies and integration impact. With this, developers can highlight the architectural risks of each proposed change before it’s actually merged.
Finally, CodeRabbit Security is designed to handle governance by performing full-repository scans and continuously monitoring new code after it’s shipped. It works by identifying logic vulnerabilities in production code, and from there it can generate recommended fixes and send them back through the pull request loop to ensure that additional problems won’t surface if they’re implemented.
By combining these capabilities into a single platform, CodeRabbit says, it’s making it much easier for software engineering teams to manage high-volume AI code changes from multiple sources without sacrificing control. “We pioneered AI code review to give teams an independent system that validates work before it ships,” Gill explained. “Agentic Change Management expands that layer to determine what deserves attention, explain each change’s impact and continue monitoring the codebase after it ships.”
Image: SiliconANGLE/Gemini
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