Building AI Agents from Scratch: Lessons Learned

After spending months building AI agents for production use cases, I want to share the patterns that worked, the mistakes I made, and the architecture that emerged.

What Is an AI Agent?

An AI agent is more than a chatbot. It’s a system that can:

• Reason about a problem and break it into steps
• Act by calling tools and APIs
• Observe results and adjust its plan
• Loop until the task is complete

The core loop is deceptively simple:

def agent_loop(task, tools, max_steps=10):
    messages = [{"role": "user", "content": task}]

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

        if response.is_final:
            return response.content

        # Execute tool calls
        for tool_call in response.tool_calls:
            result = execute_tool(tool_call)
            messages.append(tool_result(result))

    return "Max steps reached"

Architecture Patterns

1. Tool Design Matters Most

The number one factor in agent performance isn’t the model — it’s how you design your tools. Good tools are:

• Atomic — Do one thing well
• Descriptive — Clear names and parameter descriptions
• Forgiving — Return helpful errors, not stack traces
• Observable — Log what they do for debugging

2. Structured Output is Non-Negotiable

Never let the agent return free-form text when you need structured data. Use schemas:

class SearchResult:
    query: str
    filters: dict
    max_results: int = 10

3. Memory is Hard

Short-term memory (conversation context) is easy. Long-term memory is where things get tricky:

• Vector stores work for semantic search but miss exact matches
• Structured databases work for facts but miss semantic similarity
• Hybrid approaches are usually necessary

The best memory system is the simplest one that solves your actual use case.

Mistakes I Made

1. Over-engineering the prompt — Started with 2000-word system prompts. The best prompts are concise and specific.
2. Not testing tool calls — Unit test your tools independently from the agent.
3. Ignoring cost — A poorly designed agent loop can burn through API credits fast. Add token budgets early.
4. Skipping observability — You need to see every step the agent takes. Log everything.

What’s Next

I’m now exploring multi-agent systems where specialized agents collaborate on complex tasks. Think of it as microservices, but for AI reasoning.

The field is moving incredibly fast. If you’re building agents, my advice is: start simple, measure everything, and iterate.

---

Have questions about AI agents? Find me on GitHub.