What an AI Agent Actually Is
Everyone says 'agent' like it's magic. It isn't; it's a loop. Here's the whole idea in plain English.
"Agent" might be the most overused word in AI right now, and one of the least explained. It gets thrown around like it means something autonomous and a little magical. It doesn't. Once you see the shape of it, an agent is one of the simplest ideas in the whole field.
In plain English
A plain language model only does one thing: you give it text, it gives you text back. That's a chatbot. Useful, but it can't actually do anything in the world; it can't check the weather, read a file, or send an email. It just talks.
An agent is what you get when you let that model take actions and see what happens. You hand it a goal, and instead of answering in one shot, it works in a loop: decide a step, take it, look at the result, decide the next step. It keeps going until the goal is met.
That's the whole trick. An agent is a model running in a loop with access to tools. If you want to see one wired up end to end on real infrastructure, that's exactly what Building an AI System walks through.
Chatbot vs workflow vs agent
These three get mixed up constantly. The real difference is who decides what happens next:
- A chatbot answers, then stops. You drive every turn.
- A workflow runs fixed steps you wrote in advance: step one, then two, then three. You decided the path ahead of time.
- An agent decides the path itself, at runtime, based on what it sees. You set the goal; it figures out the steps.
So the dividing line isn't intelligence; it's who's choosing the next move.
The loop, in one line
You give it a goal, it picks a step, it uses a tool, it reads the result, it picks the next step, and it repeats until done.
Everything fancy you've heard about agents (planning, tool use, memory, self-correction) is just a richer version of that loop.
Why it matters
Reach for an agent when the steps aren't knowable in advance. If you can write the steps down ahead of time, you want a workflow; it's cheaper, faster, and more predictable. If the path depends on what the system finds along the way (search results, a file's contents, a user's messy request), that's when letting the model decide each step earns its keep.
The mistake most people make is reaching for an agent when a workflow would do. Start with the simplest thing that works, and add the loop only when the problem actually needs it.