How to Build an AI Agent from Scratch: Step-by-Step Tutorial

Building an AI agent might sound complex, but with today's frameworks and the MCP ecosystem, you can create a capable autonomous agent in under an hour.

What We're Building

An AI agent that can search the web, read/write files, query a database, and make tool-use decisions.

Step 1: Set Up

pip install langchain langchain-anthropic mcp python-dotenv

Step 2: Define Tools

Tools are the capabilities your agent can use. Each tool has a name, description, and function.

from langchain.tools import tool

@tool
def search_web(query: str) -> str:
    # Integration with Brave Search or similar
    pass

Step 3: Create the Agent

from langchain_anthropic import ChatAnthropic
from langchain.agents import create_tool_calling_agent

llm = ChatAnthropic(model="claude-sonnet-4-20250514")
agent = create_tool_calling_agent(llm, tools, prompt)

Step 4: Add Memory

For conversation continuity, add a memory system for cross-session context.

Step 5: Connect MCP Servers

Extend capabilities by connecting MCP servers for database access, file management, and more.

Best Practices

1. Start simple with 2-3 tools
2. Use MCP servers — our directory has 2,299+
3. Add guardrails for destructive actions
4. Monitor costs and token usage

Next Steps

Browse our AI Agents Directory for inspiration, connect MCP servers, and explore AI workflows.