Demystifying AI Agents: Build One From Scratch in Google Colab

AI agents are often treated as inscrutable black boxes. This tutorial shatters that notion by building one from the ground up in Google Colab, exposing the inner workings that most frameworks obscure.

Inspired by nanobot architecture, but eschewing any pre-built agent framework, this guide recreates core components like tool registration, session memory, and an MCP-style tool server from scratch. The result? A lightweight AI agent that's fully runnable in Colab and, more importantly, fully comprehensible.

The journey begins with the critical 'provider' abstraction:

class Provider:
    """Base class. A provider turns (messages, tools) into an LLMResponse."""
    name = "base"
    async def complete(self, messages: list[dict], tools: list[dict]) -> LLMResponse:
        raise NotImplementedError

This seemingly simple class is the linchpin of the entire system. It normalizes interactions with language models, whether you're using OpenAI's API or a local mock LLM. This abstraction is what allows the rest of the agent architecture to remain blissfully ignorant of the underlying model details.

The tutorial then methodically builds up the agent's capabilities:

1. Tool Registration: Defining the agent's action space.
2. Session Memory: Maintaining conversational context.
3. Lifecycle Hooks: Customizing behavior at key decision points.
4. Skills: Packaging reusable capabilities.
5. MCP-Style Tool Server: Orchestrating tool execution.

A standout feature is the MockProvider. This isn't just a dummy placeholder, it's a deterministic, rule-based LLM simulator that can run the entire agent loop without an API key or network connection. It even includes clever touches like scanning previous messages to 'remember' user-provided information, demonstrating how session memory integrates with model context.

Here's where this tutorial truly shines: by rebuilding these components from scratch, it forces you to grapple with the actual mechanics of how messages flow, how tool calls are decided and executed, and how memory influences the entire process. This isn't just reading about agent architecture, it's experiencing it firsthand.

The choice of Google Colab as the development environment is a double-edged sword. It ensures accessibility and removes setup hurdles, but it also imposes constraints that shape the implementation. This isn't a weakness; it's a deliberate trade-off that keeps the focus squarely on core concepts rather than advanced optimizations.

Make no mistake: this isn't meant to replace production agent frameworks. What it does offer is something far more valuable, the deep understanding required to use those frameworks effectively, to debug them when they inevitably break, and to push their boundaries with custom extensions.

For developers who've worked with high-level agent APIs but felt a nagging uncertainty about what's really happening under the hood, this tutorial is the antidote. It transforms the abstract concept of an AI agent into concrete, manipulable code.

By the time you've worked through this guide, the once-opaque world of AI agents will be laid bare. You'll understand not just what these systems do, but how they do it, knowledge that's invaluable whether you're building proof-of-concept demos or architecting production-grade AI applications.