The agent that writes code is biased and may miss its own errors. Kun Chen uses a tool spawning a new agent with a fresh context. This 'reviewer' agent analyzes the original intent and scrutinizes the code for bugs, catching far more edge cases than self-review.

Relying on a single model family for generation and review is suboptimal. Blitzy found that using models from different developers (e.g., OpenAI, Anthropic) to check each other's work produces tremendously better results, as each family has distinct strengths and reasoning patterns.

To get an objective critique of AI-generated content, use a dedicated 'reviewer' sub-agent. This separates the drafting and evaluation processes, preventing the original agent from being biased by its own creation and ensuring a higher quality output.

Instead of relying on a single AI model, Josh Pigford's workflow uses Opus for initial code generation and then runs a review pass with a different powerful model like GPT. This adversarial, multi-model process consistently uncovers 3-5 bugs that the primary model overlooks.

An AI agent reviewing its own code is prone to confirmation bias, as it operates from the same context that created an error. To achieve genuine quality assurance, use a different AI model, preferably from another vendor, for review. This introduces diverse training and uncovers blind spots.

To overcome the challenge of reviewing AI-generated code, have different LLMs like Claude and Codex review the code. Then, use a "peer review" prompt that forces the primary LLM to defend its choices or fix the issues raised by its "peers." This adversarial process catches more bugs and improves overall code quality.

An external AI reviewer provides more than just high-level feedback; it can identify specific, critical technical flaws. In one case, a reviewer AI caught a TOCTOU race condition vulnerability, suboptimal message ordering for LLM processing, and incorrect file type classifications—all of which were integrated and fixed by the primary AI.

Prompting a different LLM model to review code generated by the first one provides a powerful, non-defensive critique. This "second opinion" can rapidly identify architectural issues, bugs, and alternative approaches without the human ego involved in traditional code reviews.

To improve code quality, use a secondary AI model from a different provider (e.g., Moonshot AI's Kimi) to review plans generated by a primary model (e.g., Anthropic's Claude). This introduces cognitive diversity and avoids the shared biases inherent in a single model family, leading to a more robust and enriching review process.