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.

Enhance pull requests by using Playwright to automatically screen-record a demonstration of the new feature. This video is then attached to the PR, giving code reviewers immediate visual context of the changes, far beyond what static code can show.

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.

Despite sophisticated AI debugging tools that monitor logs and browsers, the most efficient solution is often the simplest. Highlighting an error message, copying it, and pasting it directly into an AI agent's chat window is a fast and reliable way to get a fix without over-engineering your workflow.

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.

It's tempting to ask an AI to fix any bug, but for visual UI issues, this can lead to a frustrating loop of incorrect suggestions. Using the browser's inspector allows you to directly identify the problematic CSS property and test a fix in seconds, which is far more efficient than prompting an LLM.

Use 'stop hooks' in Claude Code to create an automated quality gate. After code generation, the hook runs checks like type checking or linting. If errors exist, the output is fed back to the AI with a prompt to fix them, creating a self-correcting workflow.

Agentic IDEs like Google's Anti-gravity will revolutionize development by eliminating tedious debugging. Its Chrome extension can programmatically access the DOM and console, allowing the AI to diagnose front-end issues automatically without requiring developers to manually copy and paste error data.

To get the best results from an AI agent, provide it with a mechanism to verify its own output. For coding, this means letting it run tests or see a rendered webpage. This feedback loop is crucial, like allowing a painter to see their canvas instead of working blindfolded.