How do you control your AI Coding Agents?

Have you ever asked an AI to fix a small bug, only to watch it hallucinate ten new files, rewrite your configuration, and completely break your build? We've all been there.

In this rapid AI-based development era, AI coding agents have become indispensable tools for developers. These agents can assist in various tasks, from code generation and inline suggestions, to debugging and testing. However, to harness their full potential (at least until AGI arrives), it is essential to control them effectively. If we look at AI agents from just a year ago compared to now, they have improved drastically. But they are still not perfect. They can produce irrelevant or incorrect code, or maybe provide correct code that uses the simplest, most inefficient algorithms.

So, how do you steer these agents to build full-scale applications from scratch without losing your mind? Here are some strategies to consider:

1. Break Down the Problem: Instead of asking the AI agent to generate an entire application, break down the problem into smaller, manageable tasks. Just like you do with traditional agile development, divide the project into modules and modules into tasks. This allows you to control the output more effectively and ensures that the generated code is relevant to the specific task at hand.

2. Do not ask to scaffold the entire application: Instead of asking the AI agent to scaffold the entire application, get started with the basic setup by yourself. This way, you can control the structure and design of the application while still benefiting from the AI's code generation capabilities for further development.

3. Provide Clear Instructions: When interacting with AI coding agents, be specific and clear in your instructions. The more detailed your prompts are, the better the agent can understand your requirements and generate code that meets your expectations. For example, instead of asking "Generate a login API," you could say "Add a login API endpoint to the users module that accepts email and password, validates email format, matches the password with the stored hash using bcrypt, and returns a JWT token pair with a 1-hour expiration time."

4. Review and Refine: Always review the code generated by the AI agent. While these agents can produce impressive results, they may not always generate optimal or secure code. Take the time to review the generated code, test it thoroughly, and make necessary refinements to ensure it meets your standards.

5. Plan mode: Nowadays, some AI coding agents have a "plan mode" where they can outline a plan based on your request, you can review the plan, make changes to it and then ask the agent to execute the plan. This way, you can control the flow of the development process and ensure that the agent is working towards your desired outcome.

6. Working with complex logic: If you have a complex logic to implement, and you got the high-level plan on how it should unfold, what kind of algorithms to use, what efficiency you want, then you can ask the agent to generate the code with this specifications outlined. This way, you can control the quality of the code and ensure that it meets your performance requirements.

7. Equip Your Agent with Rules and Tools: A real-world best practice many developers now use is creating repository-level agent rules. Files like CLAUDE.md, .cursorrules, or .github/copilot-instructions.md act as a persistent memory and system prompt for your workspace. You can define architectural conventions, tech stacks, and formatting rules here so you don't have to repeat them in every prompt. Additionally, some AI agents allow you to integrate specific tools, libraries, or "skills." By controlling which tools the agent has access to, you ensure it consistently writes code that aligns with your project's standards and tech stack.

8. Iterative Development: Use an iterative approach when working with AI coding agents. Start with a small piece of functionality, review the generated code, and then build upon it incrementally. This allows you to maintain control over the development process and ensures that the final product aligns with your vision.

9. Context and Memory Management: For long coding sessions with agents like Cursor or Copilot, context is king. If your AI starts forgetting earlier instructions or hallucinating variables, its context window might be saturated. Learn to manage this by clearing the chat, systematically starting fresh sessions for new tasks, or explicitly controlling which files the agent can "see".

10. Version Control Discipline: This is wildly underappreciated—always commit your code before handing the reins over to an AI agent for a major task or refactor. By maintaining strict version control discipline, you create a safe save state. If the agent goes off the rails and hallucinates a mess, you can cleanly review the diffs and simply discard the unwanted changes.

11. Systematic Prompt Templating: Power-users don't write prompts from scratch every time. Create a personal library of prompt templates for recurring tasks like writing unit tests, generating boilerplate, or structuring pull requests. Reusing systematic prompts guarantees more consistent, predictable outputs from the AI.

Wrapping Up

AI coding agents are like high-performance sports cars—they can get you to your destination incredibly fast, but only if you know how to steer properly. By breaking down tasks, setting persistent rules, maintaining strict version control, and managing their context, you can transition from simply generating unmaintainable snippets to architecting robust systems.

How do you control and tame your AI coding agents? Share your best prompt engineering secrets and workflows.