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

Source: Dev.to

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

• 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 building manually is valuable when you:

• Want to customize behavior
• Need fine‑grained control
• Are debugging weird agent decisions
• Simply want to understand what’s happening under the hood

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:

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