New product line extends CodeRabbit’s purpose-built, high-performance context engine from AI code review into Slack, where engineering teams already plan, debug, and ship. One agent for your entire SDLC.
CodeRabbit, the pioneer in AI code review, announced CodeRabbit Agent, a second brain for engineering teams. CodeRabbit Agent is a single agent for the entire software development lifecycle. Built on the context engine that already runs two million code reviews a week across 15,000 engineering teams, the Agent helps teams move from individual productivity to team-level productivity, right inside the workspace where they already plan, debug, and ship.
CodeRabbit, the pioneer in AI code review, announced CodeRabbit Agent, a second brain for engineering teams. CodeRabbit Agent is a single agent for the entire software development lifecycle.
One Agent for the Agentic SDLC
AI has accelerated individual software development. Writing code, fixing bugs, generating tests: developers feel the speedup every day. Software engineering, the work of moving an idea through planning, requirements, design, coding, testing, deployment, and maintenance, hasn’t moved at the same pace. Each phase runs on a different tool and uses a different agent. None of them talks to each other. What one engineer figures out in coding doesn’t show up in testing. What the team decided in design gets lost by the time deployment rolls around.
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CodeRabbit Agent is one agent across all seven phases of the SDLC. It carries context and decisions from one phase to the next, so the team’s collective knowledge compounds across the lifecycle instead of resetting at every handoff.
“Every engineering team is adopting AI, and individual software development is faster than ever. What leaders keep telling us is that software engineering the full SDLC is still slow, because three things are missing from today’s tools: placement inside the workspace where engineering collaboration already happens, an explainable record of what the agent actually did, and cost attribution that matches how teams are organized. CodeRabbit Agent brings all three into Slack, as one agent for the entire SDLC,” said Harjot Gill, Co-founder and CEO of CodeRabbit.
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An Engineering Team’s Second Brain
CodeRabbit Agent operates across the full SDLC, turning the decisions, patterns, and know-how that usually stay locked in one engineer’s head into durable knowledge the whole team can draw on at any phase.
Built for Agentic SDLC Workflows
CodeRabbit Agent is organized around four capabilities that map to how engineering teams actually operate across the SDLC: context, memory, team collaboration, and governance.
Context: The agent connects to the tools engineering teams already run on: code (GitHub, GitLab, Bitbucket, Azure DevOps), tickets (Jira, Linear), documentation (Notion, Confluence, internal runbooks), monitoring (Datadog, PostHog, Sentry), and cloud (AWS, GCP). That context shapes every agent run, and the decisions the agent makes along the way feed back into team knowledge.
Memory: The agent builds a continuously updated knowledge base from everything happening across Slack and your systems. Decisions, fixes, patterns, and conversations are captured as they happen, and refined through everyday use, so the agent always reflects how your team actually works.
Team collaboration: The agent works in a shared thread alongside your team. Anyone can guide, contribute, and move tasks forward. It learns from team conversations and stays aligned as work evolves.
Governance: Access, knowledge, and spend, scoped to the channel and user. Every run is explainable and attributed, so you see what the agent did, for whom, and what it cost.
