How to Debug AI Coding Agents When They Change the Wrong Thing

A practical tutorial for recording model tool requests, real function results, patches, checks, screenshots, and a saved run log.

Artificial Intelligence (AI) coding agents are increasingly being used to automate various tasks in software development, including code completion, bug fixing, and code review. However, as with any complex system, AI coding agents can sometimes introduce errors or modify the wrong parts of the code. In this article, we will provide a step-by-step guide on how to debug AI coding agents when they change the wrong thing.

Understanding the Problem

Before we dive into the debugging process, it’s essential to understand the potential problems that can arise when using AI coding agents. Some common issues include:

• Incorrect code modifications: The AI agent may modify the wrong part of the code, leading to unexpected behavior or errors.
• Overwriting existing code: The AI agent may overwrite existing code, including manually written code or code generated by other tools.
• Introduction of new bugs: The AI agent may introduce new bugs or errors that were not present in the original code.

Preparing for Debugging

To debug AI coding agents effectively, you need to prepare by gathering the necessary information and tools. Here are some steps to follow:

1. Record model tool requests: Keep a record of the requests you send to the AI coding agent, including the input parameters and expected output.
2. Save real function results: Save the actual output of the AI coding agent, including any errors or warnings.
3. Patch and check: Apply patches or modifications to the code generated by the AI agent and check if the issues are resolved.
4. Capture screenshots: Take screenshots of the code, errors, and warnings to visualize the problem.
5. Save a run log: Save a log of the AI coding agent’s activity, including any errors or warnings, to track the issues.

Step-by-Step Debugging Process

Once you have prepared the necessary information and tools, follow these steps to debug the AI coding agent:

1. Review the code: Review the code generated by the AI coding agent to identify any potential issues or errors.
2. Compare with expected output: Compare the actual output of the AI coding agent with the expected output to identify any discrepancies.
3. Analyze the run log: Analyze the run log to identify any patterns or errors that may indicate the root cause of the problem.
4. Apply patches and modifications: Apply patches or modifications to the code generated by the AI agent to resolve the issues.
5. Test and iterate: Test the modified code and iterate on the debugging process until the issues are resolved.

Tools and Techniques for Debugging AI Coding Agents

Several tools and techniques can aid in debugging AI coding agents, including:

• Code review tools: Tools like GitHub Code Review or Bitbucket Code Review can help you review and analyze the code generated by the AI agent.
• Debugging frameworks: Frameworks like PyCharm or Visual Studio Code can provide debugging tools and features to help you identify and fix issues.
• AI-specific debugging tools: Tools like AI Debug or DebugDiag can provide AI-specific debugging features and insights.
• Log analysis tools: Tools like ELK Stack or Splunk can help you analyze the run log and identify patterns or errors.

Best Practices for Debugging AI Coding Agents

To ensure effective debugging of AI coding agents, follow these best practices:

• Use version control: Use version control systems like Git to track changes and collaborate with team members.
• Test thoroughly: Test the code generated by the AI agent thoroughly to identify any issues or errors.
• Use debugging frameworks and tools: Use debugging frameworks and tools to aid in the debugging process.
• Document the process: Document the debugging process, including the issues, solutions, and lessons learned.
• Continuously improve: Continuously improve the AI coding agent and the debugging process to ensure better results and efficiency.

Conclusion

Debugging AI coding agents can be a complex and challenging task, but with the right approach and tools, you can identify and fix issues effectively. By following the steps outlined in this article, you can ensure that your AI coding agents generate high-quality code that meets your requirements. Remember to use version control, test thoroughly, and document the debugging process to ensure continuous improvement.

This article was originally published on Towards Data Science and is reposted here with permission.