Taming AI Code Agents: Strategies for Precise Refactoring with LLMs
Agentic coding tools, while promising for automating mechanical tasks, often frustrate developers by introducing unwanted changes, reordering code, or even generating incorrect arguments despite explicit instructions to stay on task. This loss of coherence and scope creep can lead to wasted time and erode trust in the generated code.
The Power of Planning and Small Steps
The most consistent and valuable advice for taming AI code agents is to enforce a structured workflow centered around planning and granular execution. Instead of giving a single, broad command like "refactor the codebase," developers should compel the agent to first create a detailed, step-by-step plan. This plan should identify all locations requiring changes and, ideally, note any anticipated difficulties in transitioning to a new API.
Once the plan is generated, it's crucial to review and approve (or tweak) it. The execution then proceeds one small, atomic step at a time. This approach prevents the agent from going down rabbit holes, keeps the context focused, and allows for easier course correction. For a large number of similar refactorings, scripting these individual steps can create atomic, reviewable commits, such as refactoring one job type at a time.
Precise Prompting: What to Do, Not What Not To Do
Another critical insight is the style of instruction. Negative constraints, such as "Do NOT modify unrelated code" or "STOP RENAMING VARIABLES," are often misinterpreted by language models and can paradoxically trigger the very behaviors they are meant to prevent. Instead, prompts should focus on positive instructions, clearly stating what to do, focusing on desired outcomes and the exact scope of work.
For example, "Refactor ONLY [SpecificJobName] class to match the new signature" is far more effective than a broad directive. Prompts should also be highly specific, providing explicit before-and-after states for code changes, detailing parameter shifts, return values, and specifying target files or classes. Concrete examples of desired transformations can significantly improve an agent's performance. A clever technique is to use the agent itself to help refine and improve a prompt for a given task.
Iterative Review and Course Correction
Effective agentic programming requires continuous oversight and a willingness to provide feedback. Developers should thoroughly review every proposed change and not allow modifications they disagree with. When an agent deviates, providing specific feedback about the preferred approach helps it learn and improve its behavior within the current session. This iterative feedback loop can sometimes be codified into project-specific guidelines, such as a CLAUDE.md file, for consistent application.
It's also important to manage expectations. Language models don't possess the ability to say "I don't know"; they will simply produce code, even if it's incorrect. Therefore, accepting a 95% solution that requires minor manual cleanup might still represent a significant time-saving over fully manual refactoring. While some users note perceived degradations in model performance or debate the impact of 'polite' prompting, the consensus points to a structured, precise, and iterative approach as the key to successful agentic refactoring.