What Is an AI Agent? A Developer's Explanation

An AI agent is a system that uses a language model to decide what to do next, then acts on that decision — usually by calling a tool, an API, or another piece of software — and repeats that loop until a goal is reached. The defining feature isn't the model itself; it's the loop. A chatbot answers a question. An agent decides what action to take, takes it, observes the result, and decides again.

If you've asked this question because you're trying to figure out whether to build one, or how a "chatbot" is different from an "agent," the short answer is: a chatbot produces text, an agent produces actions. Everything else in this article is detail on top of that distinction.

The core loop

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

1. Observe — read the current state: a user message, a tool result, a file, an API response.
2. Reason — the model decides what to do next based on that state and its instructions.
3. Act — call a tool, run a query, write a file, send a message.
4. Repeat — feed the result of the action back in as new observation, and loop until the task is done or a stopping condition is hit.

This is sometimes called the ReAct pattern (reason + act), and it's the basis for almost every agent framework you'll encounter, whether it's LangChain, a custom loop, or a vendor's "agent" product.

What makes something an agent vs. just a model call

A single prompt-response exchange with an LLM is not an agent. It becomes agentic when it has:

• Tools — functions or APIs it can call (search the web, query a database, run code, send an email).
• State — memory of what happened earlier in the task, not just the current message.
• Autonomy over steps — the model itself decides how many actions to take and in what order, rather than following a fixed script.
• A goal, not just a question — "reconcile these two spreadsheets" instead of "what's the capital of France."

A customer support bot that always follows the same three steps in the same order is a workflow, not really an agent. A system that decides on its own whether to check inventory, escalate to a human, or issue a refund based on what it finds is an agent.

A minimal example

Here's the shape of an agent loop in plain JavaScript, independent of any specific framework:

async function runAgent(task) {
  let messages = [{ role: "user", content: task }];

  while (true) {
    const response = await callModel(messages, { tools });

    if (response.stop_reason === "tool_use") {
      const result = await executeTool(response.tool_call);
      messages.push({ role: "assistant", content: response.content });
      messages.push({ role: "tool", content: result });
      continue; // loop again with the new observation
    }

    return response.content; // model decided it's done
  }
}

The interesting engineering problems live inside callModel and executeTool: how you define tools, how you handle failures, how you cap the number of steps so the agent doesn't loop forever, and how you log what happened for debugging.

Why agents need more than "just an API key"

Agents are chatty by nature — a single task can involve five, ten, or fifty model calls as the loop runs. That has practical consequences:

• Streaming matters. Users waiting on an agent to finish a multi-step task need partial output, not a spinner. See /docs/streaming for how streamed responses work.
• Tool calling needs to be structured and reliable. The model has to return a tool name and arguments in a predictable format your code can parse without guesswork. See /docs/tools.
• Usage adds up fast. Because agents loop, you need visibility into token and request usage per key, per team member, or per feature — not just a monthly total.
• Access needs to be shared across a team without sharing one raw account. If three engineers are building agent prototypes against the same underlying Claude access, you want separate API keys, not one shared login.

This is the practical problem SubToAPI solves: it turns your existing Claude access into a standard HTTPS API with sub_live_... application keys, so each person or service calling into your agent loop has its own key, streaming and tool use work the same way they would against any Claude-compatible API, and usage is visible per key in one dashboard. If you're prototyping an agent and don't want to manage separate billing and access per teammate, /signup gets you a key in a few minutes, and /docs/quickstart walks through the first request.

Common agent patterns

• Single-agent tool use — one model, a set of tools, one loop. Good for automating a well-defined task like data extraction or report generation.
• Multi-agent systems — several specialized agents (a researcher, a writer, a reviewer) pass work between each other, often coordinated by a controller agent.
• Human-in-the-loop agents — the agent proposes an action but waits for approval before executing anything irreversible (sending an email, making a payment, deleting data).
• Autonomous agents — the agent runs unattended for extended periods, checking in only on failure or completion.

Most production systems today are single-agent tool use with a human-in-the-loop checkpoint for anything risky. Fully autonomous, unattended agents are less common in practice than the term's hype suggests, mostly because error compounding across long loops is a real problem.

Getting from concept to a working agent

The path from "I understand what an agent is" to "I have one running" usually looks like:

1. Pick a narrow, well-defined task — not "manage my inbox," but "draft replies to support tickets tagged 'billing.'"
2. Define two or three tools the model actually needs, with clear input/output schemas.
3. Send messages through a model API that supports tool calling — see /docs/messages for the request format.
4. Cap the loop (a max number of steps) so failures don't run forever.
5. Log every step so you can debug why the agent did what it did.

What's the difference between an AI agent and a chatbot?

A chatbot responds with text based on the conversation. An agent takes actions — calling tools or APIs — based on reasoning, and loops through observe-reason-act until a task is complete, not just a single reply.

Do I need a special framework to build an AI agent?

No. Frameworks like LangChain or custom orchestration libraries add convenience, but the core requirement is just a model that supports structured tool calling and a loop in your own code that executes tools and feeds results back in.

How much does it cost to run an AI agent in production?

It depends heavily on how many model calls each task requires, since agent loops can involve many calls per task. Check usage per key as you scale — see /pricing for how plan and per-key usage visibility works with SubToAPI.