👥 Developers 🎯 Safer agentic development 📅 2026

Developed a practical guide for reviewing AI-generated code, combining technical explanation, hands-on testing, and a repeatable review workflow.

Impact

This guide helps developers turn AI-assisted coding from an act of trust into a reviewable workflow. It explains why agent-generated code needs scrutiny, identifies common failure modes, and walks readers through reviewing diffs, leaving inline feedback, sending that feedback back to the agent, and running automated checks before accepting the changes.

At a Glance

Audience

Developers using Warp, Claude Code, Codex, Gemini CLI, or other coding agents

Goal

Help developers review AI-generated code systematically before committing it

Collaboration

Product, Engineering

Tools

Git, Markdown, Warp, CLI coding agents

View the Work

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Behind the work (click to expand)

Context:

Coding agents can produce working code quickly, but their output can still contain subtle logic errors, architectural problems, security gaps, and other issues that are easy to miss. The goal was to create a practical guide that gave developers both the context for why review matters and a concrete workflow they could use to review and correct agent-generated changes.

Activities:

  • Researched and hands-on tested the workflow to understand the product behavior and identify the guidance readers would actually need
  • Structured the article to establish why review matters before introducing the procedural workflow
  • Identified common categories of problems in AI-generated code, including hallucinated imports, redundant logic, architectural decisions, security gaps, style drift, and incomplete error handling
  • Developed step-by-step guidance for reviewing diffs, leaving inline comments, submitting feedback to the agent, and validating the resulting changes
  • Created a concise review checklist covering both code quality and verification
  • Heavily edited and refined an AI-generated first draft for technical accuracy, clarity, flow, and audience fit