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.
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As AI coding agents generate vast amounts of code, the most tedious part of a developer's job shifts from writing code to reviewing it. This creates a new product opportunity: building tools that help developers validate and build confidence in AI-written code, making the review process less of a chore.
For stubborn bugs, use an advanced prompting technique: instruct the AI to 'spin up specialized sub-agents,' such as a QA tester and a senior engineer. This forces the model to analyze the problem from multiple perspectives, leading to a more comprehensive diagnosis and solution.
As AI generates more code than humans can review, the validation bottleneck emerges. The solution is providing agents with dedicated, sandboxed environments to run tests and verify functionality before a human sees the code, shifting review from process to outcome.
Go beyond static AI code analysis. After an AI like Codex automatically flags a high-confidence issue in a GitHub pull request, developers can reply directly in a comment, "Hey, Codex, can you fix it?" The agent will then attempt to fix the issue it found.
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.
Simply deploying AI to write code faster doesn't increase end-to-end velocity. It creates a new bottleneck where human engineers are overwhelmed with reviewing a flood of AI-generated code. To truly benefit, companies must also automate verification and validation processes.
LLMs can both generate code analysis tools (measuring metrics like cognitive complexity) and then act on those results. This creates a powerful, objective feedback loop where you can instruct an LLM to refactor code specifically to improve a quantifiable metric, then validate the improvement afterward.
To improve the quality and accuracy of an AI agent's output, spawn multiple sub-agents with competing or adversarial roles. For example, a code review agent finds bugs, while several "auditor" agents check for false positives, resulting in a more reliable final analysis.
Define different agents (e.g., Designer, Engineer, Executive) with unique instructions and perspectives, then task them with reviewing a document in parallel. This generates diverse, structured feedback that mimics a real-world team review, surfacing potential issues from multiple viewpoints simultaneously.
Using an LLM to grade another's output is more reliable when the evaluation process is fundamentally different from the task itself. For agentic tasks, the performer uses tools like code interpreters, while the grader analyzes static outputs against criteria, reducing self-preference bias.
