If you are a developer, you should know how an AI Agent actually works under the hood. If you don’t yet, this is where you should start👇
I hope you know what an AI Agent is, it is basically an autonomous system that works on behalf of you. It has some incredible components that can reason over your prompt/query and critique over its own responses. It gets you the satisfied responses going through a loop (if it feels the generated response is not upto the mark). That’s what makes it more unique instead of just generating dumb responses.
**Let’s understanding this with a solid example/use case. **
A user says their order never arrived. The agent looks up the order, finds it’s lost in transit, issues a refund, and tells the user. That’s two tool calls and a final response, all handled without a human.
Remember, a basic agent has five components:
<ol>
<li>An LLM with tool calling support</li>
<li>A done tool and a max iteration limit as exit conditions</li>
<li>Tools for the actions your agent needs to take</li>
<li>A system prompt that defines behavior and constraints</li>
<li>A loop: call the LLM, check for tool use, run the tool, call the LLM again</li>
</ol>
Every agent framework you’ve seen, from LangChain to N8N to Zapier, is an abstraction on top of this loop. Knowing how the underlying loop works is what lets you understand why your agent is behaving the way it is, and fix it when it’s not.
**See the below image for more clarity. **
Understand more about building AI Agent through this article: <a href="https://blog.techforproduct.com/p/how-to-build-an-ai-agent-from-scratch" target="_blank" rel="noopener nofollow">https://blog.techforproduct.com/p/how-to-build-an-ai-agent-from-scratch</a>
Learn how to build AI Agents with LangGraph in minutes: <a href="https://youtu.be/3wbb0dpv_lE" target="_blank" rel="noopener nofollow">https://youtu.be/3wbb0dpv_lE</a>
Also, here is my guide to help you learn build an Agentic AI Travel App in minutes: <a href="https://youtu.be/lRUeJaZvJw8" target="_blank" rel="noopener nofollow">https://youtu.be/lRUeJaZvJw8</a>

Pavan Belagatti

A beginner-friendly breakdown of how AI agents work under the hood. Covers the five core components of a basic agent: an LLM with tool calling, exit conditions (done tool + max iterations), action tools, a system prompt, and a reasoning loop. Uses a customer support scenario to illustrate how an agent autonomously handles multi-step tasks. Notes that frameworks like LangChain, N8N, and Zapier are abstractions over this same fundamental loop.

Understanding AI Agents From Scratch!