AI Agent: What Is It and How Does It Work?

An AI agent is a program that uses a language model to decide what to do next, not just what to say next. Instead of returning a single response to a prompt, an agent can plan a sequence of steps, call external tools or APIs, look at the results, and decide whether to continue, retry, or stop. The defining trait is a loop: the model observes some state, takes an action, gets a new observation, and repeats until the task is done.

This is different from a regular chatbot. A chatbot takes your message and produces a reply. An AI agent takes a goal — "find the cheapest flight to Berlin next week and draft an email with three options" — and works toward it across multiple steps, potentially calling a flight search API, a calendar API, and an email draft tool along the way, without a human writing out each intermediate instruction.

The core components of an AI agent

Every AI agent, regardless of framework or vendor, is built from a small set of pieces:

• A language model that reasons about the task and decides what action to take next
• Tools the model can call — functions, APIs, databases, file systems, browsers
• A loop or controller that manages the cycle of "think, act, observe, repeat"
• Memory or context that keeps track of what has happened so far in the task
• A stopping condition so the agent knows when it's done or should hand off to a human

None of these pieces is exotic on its own. What makes something an "agent" rather than just a script that calls an LLM once is the loop: the model's output at one step influences what happens at the next step, and the model itself decides that path rather than a developer hardcoding it.

A simple example of the agent loop

Here's a minimal, framework-free illustration of what's happening under the hood when an agent runs:

async function runAgent(goal, tools) {
  let history = [{ role: "user", content: goal }];

  while (true) {
    const response = await callModel(history);

    if (response.type === "tool_call") {
      const result = await tools[response.tool_name](response.arguments);
      history.push({ role: "assistant", content: response });
      history.push({ role: "tool", content: result });
      continue; // loop again with the new observation
    }

    if (response.type === "final_answer") {
      return response.content;
    }
  }
}

The model isn't just answering a question — it's choosing between "call a tool" and "give a final answer" at every step, based on what it has learned so far. That decision-making, repeated over multiple turns, is what people mean when they say "AI agent."

Tool use is what makes agents useful

A language model on its own only knows what was in its training data (plus whatever you paste into the prompt). An agent becomes useful in the real world when it can call tools: search the web, query a database, hit a payment API, read a file, send an email. Most modern LLM APIs support this natively through a feature usually called tool use or function calling — the model returns a structured request to call a specific function with specific arguments, your code executes it, and you feed the result back in.

This is the mechanism that turns "a model that writes text" into "a system that does things." If you're building this yourself, you need an API that supports structured tool calls, streaming for responsiveness, and reliable message handling across multi-turn conversations. SubToAPI exposes this through a standard HTTPS API — see the tool use docs for how tool calling, streaming, and message threading work in practice.

Agents vs. workflows vs. plain LLM calls

It helps to place agents on a spectrum:

1. Single LLM call — one prompt in, one response out. No loop, no tools.
2. Workflow / pipeline — a fixed sequence of LLM calls and tool calls defined by a developer. The steps are hardcoded, even if each step uses a model.
3. Agent — the model itself decides the sequence of steps, which tools to call, and when to stop, based on the goal and what it observes along the way.

Most production systems today are somewhere between 2 and 3 — a workflow with agentic decision points, rather than a fully open-ended agent. This is usually the right call: fully autonomous agents are harder to debug, more expensive to run (more model calls per task), and riskier when a tool call has real-world side effects like sending money or deleting data.

Why "what is an AI agent" gets murky in practice

Part of the confusion around the term is that "agent" gets used for very different levels of autonomy:

• A single-tool assistant that can only call one API (e.g., "search the docs") is often marketed as an agent.
• A multi-step planner that breaks a goal into subtasks and executes them in order is closer to what researchers mean by the term.
• A multi-agent system has several agents with different roles (a planner, a researcher, a writer) coordinating on a shared task.

None of these definitions is wrong — they're just different points on the same spectrum. If you're evaluating a product or deciding what to build, the more useful question isn't "is this an agent" but "how much of the decision-making is delegated to the model, and what happens when it's wrong."

What you need to build one

Practically, building an agent requires:

• An LLM API with reliable tool/function calling and streaming, so the agent can respond incrementally rather than blocking on long tool calls
• A way to define and register tools with clear input/output schemas
• Guardrails: timeouts, retry limits, and a maximum number of loop iterations so a confused agent doesn't run forever
• Logging of every tool call and model decision, since debugging an agent means reading its trace, not guessing at its reasoning

If your agent runs on top of a Claude subscription, SubToAPI turns that access into a standard HTTPS API with application keys, streaming, and tool use support, so you can build the agent loop above without managing separate API billing. Check the quickstart to see the request format, or pricing for plan details.

questions

Is an AI agent the same as a chatbot? No. A chatbot answers a single message. An AI agent works toward a goal across multiple steps, deciding which tools to call and when to stop, based on the results of its own previous actions.

Do I need a special framework to build an AI agent? No — the core pattern is just a loop that calls a model, executes any tool it requests, feeds the result back, and repeats. Frameworks add convenience (tool registries, memory, tracing) but the underlying mechanism is a handful of API calls.

What's the difference between an agent and a workflow? A workflow has a fixed, developer-defined sequence of steps. An agent lets the model decide the sequence itself — which step to take next, which tool to call, and when the task is finished.