Building AI Agents with LangChain: A Practical Guide

Introduction

AI agents represent the next evolution in artificial intelligence applications. Unlike simple chatbots that respond to queries, AI agents can autonomously plan and execute multi-step tasks to achieve goals.

In this guide, I'll walk you through building a practical AI agent using LangChain, drawing from my experience developing production AI systems.

What Are AI Agents?

AI agents are systems that use LLMs as their "brain" to:

• Reason about problems and break them into steps
• Plan sequences of actions to achieve goals
• Execute those actions using available tools
• Learn from feedback and adjust their approach

Setting Up Your Environment

First, let's set up our development environment:

pip install langchain langchain-openai python-dotenv

Create a .env file with your API keys:

OPENAI_API_KEY=your_key_here

Building a Simple Agent

Here's a basic agent that can search the web and perform calculations:

from langchain.agents import initialize_agent, Tool
from langchain.llms import OpenAI
from langchain.tools import DuckDuckGoSearchRun

# Initialize the LLM
llm = OpenAI(temperature=0)

# Define tools
search = DuckDuckGoSearchRun()
tools = [
    Tool(
        name="Search",
        func=search.run,
        description="Useful for searching the internet"
    )
]

# Create the agent
agent = initialize_agent(
    tools,
    llm,
    agent="zero-shot-react-description",
    verbose=True
)

# Run the agent
result = agent.run("What is the current weather in New York?")

Adding Custom Tools

The power of agents comes from custom tools. Here's how to create one:

from langchain.tools import tool

@tool
def calculate_roi(investment: str, returns: str) -> str:
    """Calculate ROI given investment and returns amounts."""
    inv = float(investment)
    ret = float(returns)
    roi = ((ret - inv) / inv) * 100
    return f"ROI: {roi:.2f}%"

Best Practices

1. Clear Tool Descriptions: Agents rely on descriptions to choose tools
2. Error Handling: Always wrap tool execution in try-catch blocks
3. Rate Limiting: Implement delays to avoid API throttling
4. Logging: Track agent reasoning for debugging

Production Considerations

When deploying agents to production:

• Use streaming for better UX
• Implement caching for repeated queries
• Add human-in-the-loop for critical decisions
• Monitor token usage and costs

Conclusion

AI agents are transforming how we build intelligent applications. Start simple, iterate quickly, and always keep the user experience in mind.

Want to discuss AI agents? Book a call with me!