AI Agent, Explained: What It Actually Is

An AI agent is a program that uses a language model to decide what to do next, then actually does it — calling tools, running code, reading results, and looping until a goal is met. That's the core distinction from a regular chatbot: a chatbot answers a question, an agent pursues an outcome. It plans, acts, observes what happened, and adjusts, often across many steps without a human approving each one.

If you've used a customer support bot that can only answer from a script, that's not an agent. If you've used something that can check your order status by querying a real database, issue a refund through a payment API, and then email you a confirmation — all from one instruction — that's an agent. The model isn't just generating text; it's generating decisions that trigger real actions.

The Core Loop

Every AI agent, regardless of framework or vendor, runs some version of the same loop:

1. Perceive — receive a goal or task, plus current context (conversation history, tool outputs, files, state).
2. Reason — the language model decides what to do next: answer directly, or call a tool.
3. Act — if a tool is needed, the agent executes it (an API call, a database query, a code execution).
4. Observe — the tool's output is fed back into the model as new context.
5. Repeat — the loop continues until the model decides the task is complete, or a limit is hit.

This is fundamentally different from a single prompt-response exchange. A chatbot completes step 1 and 2 once and stops. An agent keeps looping, sometimes for dozens of steps, chaining tool calls together to accomplish something a single response never could — like researching a topic across five sources, comparing the findings, and writing a summary.

Why Language Models Alone Aren't Agents

A language model by itself has no memory beyond its context window, no ability to take real-world action, and no persistent state. It can write code, but it can't run it. It can draft an email, but it can't send it. It's a reasoning engine, not an actor.

What turns that reasoning engine into an agent is the scaffolding around it:

• Tools — functions the model can call (search, code execution, database access, external APIs).
• Memory — a way to persist information across steps or sessions, beyond the raw context window.
• Control flow — the code that decides when to keep looping, when to ask for human approval, and when to stop.
• State — tracking what's been done, what's pending, and what the current goal actually is.

The model provides judgment. The system around it provides the ability to act on that judgment. Neither piece alone makes an agent.

A Concrete Example

Say you ask an agent: "Find our top 3 churned customers this month and draft a win-back email for each."

Without agent capability, a language model can only guess or ask you to paste in data. With tools, the loop looks like:

1. Model decides it needs customer data → calls a query_database tool.
2. Tool returns a list of churned accounts with revenue figures.
3. Model reasons about which three are highest priority → calls the same tool again with a filter, or does the ranking itself.
4. Model drafts three emails, one per account, using details from the query results.
5. Model reports back with the drafts, or calls a send_email tool if authorized to act autonomously.

Every arrow between those steps is a real function call with real output, not simulated text. That's what "agent" means in practice — the model's reasoning is wired directly into things that actually happen.

Levels of Autonomy

Not every agent is fully autonomous, and that's intentional. Most production systems sit somewhere on a spectrum:

• Suggest-only: the agent recommends an action, a human approves it (common in finance, healthcare, anything irreversible).
• Human-in-the-loop: the agent executes low-risk steps automatically and pauses for approval on higher-risk ones.
• Fully autonomous: the agent runs the entire loop unsupervised, common for research tasks, data processing, or internal tooling with low blast radius.

Choosing the right level matters more than making something maximally autonomous. A coding agent that can run shell commands unsupervised is powerful and also dangerous if it misreads a task. Good agent design usually means scoping tool permissions tightly and adding checkpoints, not removing all human oversight.

What You Need to Build One

Practically, building an agent requires three things:

• A capable model that can reason over multi-step tasks and reliably decide when to call a tool versus respond directly.
• A tool-calling interface — most modern APIs support this natively, letting you define functions with typed parameters that the model can invoke.
• An execution loop in your own code that handles the call, runs the actual function, and feeds the result back.

This is also where infrastructure choices matter. If you're building on top of Claude, for example, you need a stable API layer that handles authentication, streaming responses as the agent works, and returning structured tool-call output your loop can parse. SubToAPI turns an existing Claude subscription into that kind of HTTPS API — with dedicated application keys, streaming, and tool-use support — so you're not gluing together your own auth and proxy layer just to get an agent loop running. Details on tool calling are in the tools docs, and streaming behavior is covered in the streaming docs.

questions

Is an AI agent the same as a chatbot? No. A chatbot generates a response to a message. An agent takes a goal, decides on a sequence of actions using tools, executes them, and adjusts based on results — often across multiple steps without a new prompt from the user each time.

Does an AI agent need to be fully autonomous? No. Most real-world agents include human checkpoints for risky or irreversible actions. Autonomy is a design choice, not a requirement — you can build agents that suggest actions, ones that auto-execute low-risk steps, or ones that run entirely unsupervised.

What's the minimum needed to build an AI agent? A language model with tool-calling support, a defined set of tools (functions it can call), and a loop in your code that executes those calls and feeds results back to the model. See the quickstart for a working example.