AI Agents with Tool Use: Building Autonomous Coding Assistants

AI Agents with Tool Use: Building Autonomous Coding Assistants

AI agents that can read files, write code, run tests, and iterate on failures are transforming software development. Here is how to build one that actually works reliably.

The Agent Loop

Every AI agent follows the same core pattern: observe → think → act → observe results → repeat. The difference between a demo and production agent is error handling and safety.

async def agent_loop(task: str, max_steps: int = 20):
    messages = [{"role": "user", "content": task}]

    for step in range(max_steps):
        response = await llm.chat(messages, tools=TOOLS)

        if response.stop_reason == "end_turn":
            return response.content

        for tool_call in response.tool_calls:
            result = await execute_tool(tool_call)
            messages.append(tool_result(result))

    return "Max steps reached"

Tool Design Principles

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Atomic operations: Each tool does one thing well

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Rich error messages: Tell the agent WHY something failed, not just that it failed

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Sandboxing: Run code in containers, limit file system access

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Confirmation gates: Require human approval for destructive actions

The Hardest Problem: Knowing When to Stop

Agents that loop endlessly burn tokens and patience. Implement cost budgets, step limits, and teach the model to ask for help when stuck instead of retrying the same failing approach.