Harness Engineering for AI Code Review: Controlling Agent-to-Agent Review
As AI agents write code 10x faster than humans, the article discusses the challenge of keeping up with code review. It introduces the concept of 'harness engineering' - designing the environment to prevent AI agents from making the same mistakes.
Why it matters
As AI systems become more capable of generating code, the ability to effectively review and validate that code is critical to ensure quality and safety.
Key Points
- 1AI agents are generating code much faster than humans can review it
- 2OpenAI and Anthropic have developed approaches to enable agent-to-agent code review
- 3Harness engineering involves designing the configuration to manage an agent's context window
- 4OpenAI uses a table of contents approach and educational linters to guide agents
- 5Anthropic uses a two-phase approach with an initializer agent and coding agent
Details
The article discusses the problem of code review not keeping up with the rapid pace of AI-generated code. OpenAI's Codex team generated over 1 million lines of code in 5 months, with engineers merging an average of 3.5 PRs per day. Anthropic's long-running agents code continuously for 6+ hours. To address this, the concept of 'harness engineering' is introduced - designing the environment to prevent AI agents from making the same mistakes. OpenAI's approach includes a concise 'AGENTS.md' table of contents, an agent-to-agent review loop, and educational linters. Anthropic uses a two-phase approach with an initializer agent and a coding agent, as well as JSON over Markdown for feature lists to prevent tampering. The goal is to create a control system that allows AI-to-AI code review to scale effectively.
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