Remember when AI was just about chatbots answering questions? Those days feel ancient now. We've entered the era of agentic AI — systems that don't just respond, they act. They book meetings, analyze contracts, orchestrate workflows, and make decisions autonomously.
In this guide, I'll walk you through building your first AI agent from scratch. No fluff, just practical code you can run today.
What Makes an Agent Different from a Chatbot?
A chatbot responds. An agent reasons, plans, and executes. The key difference lies in the agent's ability to:
- Break down complex tasks into smaller steps
- Use external tools (APIs, databases, web search)
- Maintain memory across interactions
- Self-correct when things go wrong
Setting Up Your Environment
Let's start with LangChain, one of the most battle-tested frameworks for building agents. First, install the dependencies:
pip install langchain langchain-openai langchain-community python-dotenv
Now let's build a simple research agent that can search the web and summarize findings:
from langchain_openai import ChatOpenAI
from langchain.agents import AgentExecutor, create_react_agent
from langchain.tools import DuckDuckGoSearchRun
from langchain import hub
import os
# Initialize the LLM
llm = ChatOpenAI(model="gpt-4-turbo-preview", temperature=0)
# Define tools the agent can use
search_tool = DuckDuckGoSearchRun()
tools = [search_tool]
# Get a pre-built ReAct prompt
prompt = hub.pull("hwchase17/react")
# Create the agent
agent = create_react_agent(llm, tools, prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
# Run the agent
result = agent_executor.invoke({
"input": "What are the latest developments in AI agents in 2024?"
})
print(result["output"])The ReAct Pattern: Think Before You Act
The code above uses the ReAct (Reasoning + Acting) pattern. It's elegantly simple: the agent thinks about what to do, takes an action, observes the result, and repeats until the task is complete.
"The ReAct pattern transforms LLMs from pattern-matching systems into deliberate problem solvers."
Multi-Agent Systems with CrewAI
Single agents are powerful, but teams of specialized agents can tackle complex problems more effectively. CrewAI makes this surprisingly easy:
from crewai import Agent, Task, Crew
from langchain_openai import ChatOpenAI
llm = ChatOpenAI(model="gpt-4-turbo-preview")
# Define specialized agents
researcher = Agent(
role="Senior Research Analyst",
goal="Uncover cutting-edge developments in AI",
backstory="You're a veteran tech researcher with a keen eye for emerging trends.",
llm=llm,
verbose=True
)
writer = Agent(
role="Tech Content Writer",
goal="Create engaging content about AI developments",
backstory="You're a skilled writer who makes complex topics accessible.",
llm=llm,
verbose=True
)
# Define tasks
research_task = Task(
description="Research the latest AI agent frameworks and their capabilities.",
agent=researcher,
expected_output="A comprehensive report on AI agent frameworks"
)
writing_task = Task(
description="Write a blog post based on the research findings.",
agent=writer,
expected_output="An engaging blog post about AI agents"
)
# Create and run the crew
crew = Crew(
agents=[researcher, writer],
tasks=[research_task, writing_task],
verbose=True
)
result = crew.kickoff()
print(result)Pro Tips for Production Agents
1. Always implement guardrails — Agents can go off the rails. Set token limits, timeout thresholds, and content filters.
2. Log everything — When an agent fails, you need to understand why. Use structured logging for every decision point.
3. Start simple — Don't build a 10-agent system on day one. Start with a single agent, prove it works, then scale.
The agentic AI space is evolving rapidly. The frameworks I've shown here are just the beginning. The real magic happens when you start combining these patterns with your domain expertise. What will you build?