How CodeRabbit used Claude to build an agent orchestration system

2026-05-27 · Source: Claude Blog · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, medium

Summary

CodeRabbit, an AI code review platform founded in 2023, developed an agent orchestration system using the Claude Platform to address a common issue where AI-generated code compiles but fails to meet developer intent. This system, which reviews 2 million pull requests weekly for over 15,000 customers, inserts a structured planning phase before any code is generated. It coordinates various Claude models—Opus for strategic understanding, Sonnet for sequencing, and Haiku for specific tasks—to analyze requirements and surface assumptions. The output is a collaborative Product Requirements Document (PRD) that defines what should be built and its constraints, serving as a quality gate to catch errors early and reduce expensive rework. CodeRabbit also built an evaluation harness to measure plan quality, iterating to find the optimal level of detail.

Key takeaway

For AI Engineers or Architects building with large language models, if you are experiencing AI-generated code that compiles but misses intent, consider implementing a structured planning orchestration layer. This approach allows you to explicitly define requirements and surface assumptions before code generation, acting as a critical quality gate. By validating the plan upfront, you can significantly reduce expensive downstream rework and ensure your AI systems produce code that truly aligns with project goals.

Key insights

Planning quality directly dictates AI code output quality, making early validation crucial to avoid expensive rework.

Principles

• Planning quality determines AI code output quality.
• Explicit requirements prevent AI assumption-filling.
• Match LLM tier to task complexity for efficiency.

Method

Implement an orchestration layer before AI code generation to coordinate multiple LLMs for requirement analysis, assumption surfacing, and structured plan creation. Refine plan granularity using an evaluation harness.

In practice

• Define explicit specifications and desired outcomes.
• Ask LLMs to identify implicit assumptions.
• Create a chronicle of planning artifacts.

Topics

• Agent Orchestration
• AI Code Review
• Claude Platform
• LLM Planning
• Evaluation Harness
• Product Requirements Document

Best for: AI Product Manager, AI Engineer, Machine Learning Engineer, AI Architect

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Editorial summary, takeaway, and curation by AIssential. Original article published by Claude Blog.