Building an AI Coding Agent: 6 Lessons From Real Development

An AI coding agent is a program that uses a large language model (LLM) to read code, decide what to do next, and act on it through tools like file editing and shell commands. I built one to see how far a small setup could go. It taught me more about software design than about AI.

Here are the six lessons that mattered most.

1. Start With a Tiny Loop

My first version was about 40 lines. Every coding agent, from a weekend project to Claude Code or Cursor, is built on the same loop:

while True:
    response = llm(messages, tools)
    if response.tool_call:
        result = run_tool(response.tool_call)
        messages.append(result)
    else:
        break  # the model is done

The model proposes an action, your code runs it, and the result goes back into the conversation. I kept the first version of these AI Agents small, which made every later bug easier to find. Add features only after the core loop works.

2. Tools Matter More Than the Prompt

I spent days polishing the system prompt. The biggest improvement came from fixing the tools instead.

A coding agent needs only a few: read a file, edit a file, list a directory, and run a command. What matters is the quality of each tool:

• Clear names and descriptions. The model chooses tools based on how you describe them.
• Helpful error messages. "File not found. Did you mean src/app.py?" lets the agent recover on its own.
• Narrow scope. One tool that does one thing gets called correctly far more often than a tool with ten options.

3. Context Is Your Scarcest Resource

Every file you read and every command output you return fills the context window. Once it is full or cluttered, the agent gets slower, more expensive, and less accurate.

What worked for me:

• Return line ranges instead of whole files.
• Truncate long command output and keep the start and end.
• Summarize old steps instead of keeping the full history.
• Let the agent search the codebase instead of loading everything upfront.

Deciding what to leave out turned out to matter as much as what to include.

4. Make Every Edit Reversible

Early on, my agent rewrote an entire file to change two lines and quietly removed code it did not understand. That was my wake-up call.

I fixed it with three rules:

1. Edit with targeted diffs or find-and-replace, never full rewrites.
2. Work on a separate Git branch.
3. Commit before and after each task so any change can be rolled back in seconds.

Version control is the best safety net for an AI agent. It costs nothing, and developers already know how to use it.

5. Tests Are the Agent's Feedback Loop

Without feedback, an agent cannot tell whether its code works. It will confidently report success on broken code.

Once I let the agent run the test suite after each change, quality improved noticeably. It could see a failing test, read the error, and fix its own mistake. A project with good tests and clear error output is where a coding agent performs best. A project with no tests is where it is most likely to fail without anyone noticing.

6. Keep a Human in the Loop

Agents still get things wrong. Mine struggled with vague requirements, large refactors that touched many files, and code that depended on unwritten business rules.

My rule now is simple: the agent can read freely, but anything risky needs my approval. If you're wondering what is JEV AI, this same principle applies to AI coding agents anything involving deleting files, running unfamiliar commands, or pushing code should require human approval. Review the diff the way you would review a pull request from a new teammate: smart and fast, but new to your codebase.

Final Thoughts

The main lesson from building a coding agent is that the model is only one part of the system. Tools, context, safety, and feedback decide whether it is useful or a liability.