The word agent gets thrown around a lot. Let’s make it concrete.
An AI agent is a language model placed inside a loop, given some tools, and pointed at a goal. That’s it. The magic isn’t a secret architecture — it’s the loop.
The loop
Every agent, no matter how fancy, runs some version of this:
1. THINK — given the goal and what I know, what should I do next?
2. ACT — call a tool (search, run code, read a file, send a request)
3. OBSERVE — read the result of that action
4. REPEAT — until the goal is met (or I give up)A plain chatbot stops after step 1. An agent keeps going — it can act on the world, see what happened, and adjust. That feedback loop is the whole difference.
Why tools matter
A model on its own only knows how to produce text. Tools let it do things:
- Read — fetch a web page, query a database, open a file
- Write — edit code, create a file, send a message
- Compute — run a script, call an API, do math it can’t reliably do in its head
An agent is only as capable as the tools you hand it. Good tools, focused goal, tight loop — that’s a useful agent.
A tiny example
Say the goal is “find the latest version of Astro and tell me what changed.” A capable agent might:
- Think: I need current info — I should search.
- Act:
web_search("latest Astro release notes") - Observe: reads the results, finds the version and changelog.
- Think: I have what I need.
- Answer the user.
No single step is clever. The combination is what feels intelligent.
Where it goes from here
Once you have the loop, everything else is refinement:
- Memory so the agent remembers across steps and sessions
- Planning so it breaks big goals into smaller ones
- MCP so it can plug into tools in a standard way
- Skills so it reliably gets better at specific tasks
Master the loop first. The rest is detail.