Build Your First AI Agent in Python — No Frameworks (AI Agents from Scratch — Episode 2)

This episode demonstrates how to build a functional AI agent in Python using only the Anthropic API without relying on frameworks like LangChain or AutoGen. The tutorial covers the core concepts of agent architecture, tool integration, and agentic loops in approximately 80 lines of code. Viewers will learn how to implement a working agent that can reason, plan, and execute tasks by directly interfacing with Claude's API.

Key Points

• •Build agents from first principles using only the Anthropic API—no framework dependencies required
• •Implement a core agentic loop: receive input → call Claude → parse response → execute tools → repeat
• •Define tools/functions that the agent can call and integrate them into the agent's decision-making process
• •Use Claude's function calling capability to enable the agent to decide which tools to use and when
• •Handle tool results and feed them back into the conversation context for multi-step reasoning
• •Keep implementation minimal (~80 lines) to understand the fundamental mechanics of agent behavior
• •Understand the difference between agent frameworks and the underlying patterns they abstract
• •Test agent behavior with real-world tasks to validate tool selection and execution logic

Workflow Diagram

Concepts