Building Your First AI Agent: A Realistic Step-by-Step Guide

What “agent” actually means

A loop where an LLM picks a tool, runs it, reads the output, and decides what to do next. That’s it. The complexity comes from making that loop reliable.

Step 1: Define the tools first, prompt second

Mistake everyone makes: prompt-engineer first, add tools later. Do the opposite. Pick 2-4 tools that cover your domain. For a research agent: web_search, fetch_url, final_answer.

Step 2: Write tools as plain functions with type hints

def web_search(query: str, num_results: int = 5) -> list[dict]:
    """Search the web for current information."""
    ...

Frameworks (OpenAI tool use, Anthropic tool use) convert these to JSON schemas automatically.

Step 3: Build the loop, not a chain

A chain is N → N+1. An agent is "while not done: pick tool". Use a state machine:

state = {"messages": [...], "iterations": 0}
while state["iterations"] < 15:
    response = llm.call(state["messages"])
    if response.tool_calls:
        result = run_tool(response.tool_calls[0])
        state["messages"].append(response)
        state["messages"].append(result)
        state["iterations"] += 1
    else:
        return response.content

Step 4: The 5 things that will break first

1. Infinite loops — agent calls the same tool 20 times. Add max iterations + repeated-call detection.
2. Tool argument hallucinations — agent passes country="USA" to a search tool that wants country="us". Strict JSON schemas help.
3. Lost context — long conversations push relevant info out of the window. Summarize older messages.
4. Silent tool failures — tool errors but agent thinks it succeeded. Return errors as observations, not exceptions.
5. Cost explosions — one bad query → 50 tool calls → $30 spent. Add a hard token budget.

Step 5: When to graduate to LangGraph

If your agent has branches (route to specialist), cycles (retry until valid), or human approval steps — LangGraph’s state-machine model pays off. Otherwise plain Python is fine.

Step 6: Eval before scaling

Build a 30-task eval set BEFORE you ship. Track: task completion rate, average tool calls per task, cost per task. You can’t improve what you don’t measure.