Build Your Own AI Agent From Scratch

Every vendor in 2026 claims to have "AI agents." Almost none of them do.

Chatbots with system prompts. API wrappers with a for loop. Rigid workflows dressed up with a marketing budget. The word "agent" has been stretched so thin it's lost all meaning — one engineer recently said he'd switched to "bot-to-bot protocol" just to have a serious conversation about the technology.

The fastest way to cut through the noise is to build one yourself.

Build Your Own AI Agent From Scratch takes you through sixteen chapters of real Python code — not a wrapper around LangChain, not a tutorial for someone else's framework, but a complete autonomous agent built from the ground up. By the time you finish, you won't just understand what agents are. You'll understand exactly why they work, where they break, and how to make informed decisions about when to build versus when to buy.

What you'll build:

• A working agent in ~50 lines of Python — the foundation everything else grows from
• A tool registry with dynamic function calling and JSON Schema validation
• Real tools: web search, file operations, database queries, API calls, sandboxed code execution
• A semantic tool router that matches queries to tools using embeddings
• An MCP client and server from scratch — the universal tool interface
• Conversation memory with sliding windows, summarization, and token budgets
• A vector memory store built from scratch (no external database) with RAG retrieval
• Persistent state and episodic memory that survives across sessions
• A ReAct reasoning loop — the most important pattern in agent development
• A planning agent that decomposes complex tasks and adapts when plans fail
• Error handling, retries, cost caps, circuit breakers, and human-in-the-loop guardrails
• A multi-agent supervisor/worker system
• A complete autonomous research agent that ties everything together

Every code example is available in the companion GitHub repository. Clone it, run it, break it, learn from it.

This book is for you if you're a Python developer who uses AI tools daily, you want to understand agents below the framework level, or you learn best by building things rather than reading about them.

Prerequisites: Python proficiency and basic familiarity with LLM APIs. No machine learning background required.

Book 2 in The Agent Stack: LLMs, Agents, and Multi-Agent Systems. Companion repository at github.com/grizzlypeaksoftware/gps-ai-agent-fundamentals.