AI Agents 101: From Concept to Code (No Frameworks Required)

Most tutorials about AI agents rely heavily on frameworks.

LangChain.

AutoGen.

CrewAI.

They’re powerful — but they abstract away the most important part:

How an AI agent actually works under the hood.

So I decided to build one from scratch.

No frameworks.

Just Python, an LLM, and a clear decision loop.

What This Article Covers

In this write-up, I walk through:

• What makes an AI agent different from a plain LLM


• The core “Think → Act → Observe” loop


• How tool calling works conceptually


• How to structure a simple agent controller


• How to let an agent browse and retrieve information


• A fully working example at the end

The goal wasn’t to build something production-ready.

The goal was to understand the mechanics deeply.

Why Build Without Frameworks?

Frameworks are great for speed.

But if you:

• Want to customize behavior


• Need fine-grained control


• Are debugging weird agent decisions


• Or simply want to understand what's happening

Then building one manually is incredibly valuable.

Once you understand the loop, frameworks make much more sense.

If you're interested in how AI agents really work and want a practical, code-first explanation, you can read the full article here:

👉 https://medium.com/@kamil.tustanowski/ai-agents-101-from-concept-to-code-no-frameworks-required-2dfdaf66b6c1

Would love feedback — especially from people building agent systems in real-world projects.