The theory is clean. The reality is messier. And honestly more interesting.
Everyone's talking about AI agents. Few people are showing you what it actually looks like to build one. So here's my honest account — what worked, what broke, and what surprised me.
A note before you start copying code: LangChain's agent APIs move fast, and some of what I used when I built this is now deprecated. I've flagged that inline below rather than pretending it didn't happen, that's kind of the point of this post.
What actually makes something an "agent"?
Before building anything, it's worth being precise about what we're building. An AI agent needs four things:
- An LLM as the brain — the reasoning engine
- Memory — context that persists across steps
- Tools — the ability to actually take actions
- A loop — plan → act → observe → repeat
Miss any one of the four and you don't have an agent. You have a fancy prompt.
The stack I used
- Python 3.11
- LangChain (agent framework)
- OpenAI GPT-4o (the brain)
- Tavily (web search tool)
- Python REPL (code execution tool)
Building a research agent, step by step
Step 1 — Install dependencies
pip install langchain langchain-openai tavily-python python-dotenv
Step 2 — Set up your tools
from langchain_community.tools.tavily_search import TavilySearchResults
from langchain_experimental.tools import PythonREPLTool
tools = [
TavilySearchResults(max_results=3),
PythonREPLTool()
]
Step 3 — Initialize the agent
This is the part that's changed the most since I first built this. Here's what I originally used:
# what I originally used — this pattern is now deprecated
from langchain_openai import ChatOpenAI
from langchain.agents import create_react_agent
from langchain import hub
llm = ChatOpenAI(model="gpt-4o", temperature=0)
# ReAct = Reason + Act, the classic agent loop pattern
prompt = hub.pull("hwchase17/react")
agent = create_react_agent(llm, tools, prompt)
Heads up: create_react_agent and AgentExecutor have since been deprecated in favor of create_agent from langchain.agents. The underlying concept — model, tools, and a loop wrapping them — hasn't changed. The constructor has. If you're starting fresh, check docs.langchain.com for the current signature before you build on top of this; LangChain's API surface has genuinely moved twice since I wrote this walkthrough.
Step 4 — Add the execution loop
from langchain.agents import AgentExecutor
agent_executor = AgentExecutor(
agent=agent,
tools=tools,
verbose=True, # see the agent's thinking
max_iterations=10,
handle_parsing_errors=True
)
Step 5 — Give it a goal
result = agent_executor.invoke({
"input": """
Research the current state of AI agents in enterprise software.
Find 3 specific companies using them, what they're doing,
and the results. Then write a structured summary.
"""
})
print(result["output"])
What actually happened when I ran this
Here's the agent's internal monologue, straight from verbose=True:
Thought: I need to search for information about AI agents
in enterprise software.
Action: tavily_search
Action Input: "AI agents enterprise software companies"
Observation: [search results...]
Thought: I found information about Klarna and Salesforce.
I need a third, more specific example.
Action: tavily_search
Action Input: "enterprise AI agent deployment results case study"
[continues for 6 more steps...]
Final Answer: [structured, sourced summary]
Watching this play out in real time is genuinely the best way to understand what an agent is doing differently from a single prompt. It's not magic, it's a loop, made visible.
What I got wrong (and how I fixed it)
Mistake 1 — too vague with the goal
Bad prompt: "Research AI agents"
Good prompt:
Research AI agents in enterprise software. Find 3 companies,
what they built, specific results they reported, and format
as: Company | Use Case | Result
Lesson: agents need precise goals just as much as any other system. Garbage in, garbage out — just smarter-sounding garbage.
Mistake 2 — not handling tool failures
Tavily search failed twice on bad queries. My first version just... stopped. Fix: handle_parsing_errors=True, plus retry logic around tool calls. Agents will hit walls — plan for it up front rather than discovering it in production.
Mistake 3 — infinite loops
An early version got stuck re-running the same search fifteen times. max_iterations=10 was the blunt fix; better prompt engineering telling the agent explicitly when to stop was the actual fix.
Mistake 4 — trusting output blindly
The agent confidently cited a statistic from a source that didn't exist. This is the most important lesson in the whole post: agents hallucinate with total confidence. Always validate anything that matters, and build source-checking into the prompt itself rather than trusting the output at face value.
What agents are actually good at (and where they struggle)
After building a few of these, here's my honest read:
Good at:
- Repetitive multi-step research
- Information gathering and synthesis
- Tasks with clear, checkable success criteria
- Structured data processing
Struggle with:
- Genuinely novel problem-solving
- Anything requiring real-world judgment
- Information beyond what their tools can reach
- Long tasks without checkpoints — they drift from the original goal
- Knowing when they're wrong
That last one is the one to internalize. An agent that's confidently wrong is more dangerous than one that visibly fails, because nothing in its own output tells you to double-check it.
Frameworks worth knowing
If you want to go deeper than a single research agent:
- LangChain / LangGraph — most mature ecosystem, best docs, largest community. Start here.
- CrewAI — multi-agent orchestration, built around "agent teams," a more intuitive API for that specific use case.
- AutoGen (Microsoft) — strong for human-in-the-loop agents, research-heavy focus.
- LlamaIndex — best fit for document- and data-heavy agents.
A multi-agent setup, with CrewAI
Once one agent works, the natural next step is wiring a few together. Here's a minimal researcher-and-writer crew:
from crewai import Agent, Task, Crew
researcher = Agent(
role="Research Analyst",
goal="Find accurate information about {topic}",
backstory="You're an expert researcher who finds and validates information.",
tools=[search_tool],
verbose=True
)
writer = Agent(
role="Content Writer",
goal="Write clear, engaging content",
backstory="You take research and turn it into compelling, accurate writing.",
verbose=True
)
research_task = Task(
description="Research {topic} thoroughly",
agent=researcher,
expected_output="Detailed research notes with sources"
)
writing_task = Task(
description="Write an article based on the research",
agent=writer,
expected_output="800-word article"
)
crew = Crew(
agents=[researcher, writer],
tasks=[research_task, writing_task]
)
result = crew.kickoff(inputs={"topic": "AI agents in production"})
My honest verdict
AI agents are real and genuinely useful. They're also overhyped in some circles. The truth sits in the middle:
- They work well for specific, well-defined tasks.
- They're not magic, they need real engineering around them.
- They fail in interesting, hard-to-predict ways.
- Building one teaches you more in an afternoon than reading about them for a week.
Start simple. One agent, one tool, one task. Get that working before you reach for the autonomous AI company.
What are you building with agents? Drop it in the comments — genuinely curious what's working for people right now.
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