What Are Agents in AI? A Clear, Practical Definition

An AI agent is a system built around a large language model that can take actions, not just generate text. Instead of producing a single reply to a single prompt, an agent decides what to do next, calls tools or APIs to gather information or make changes, evaluates the results, and repeats this loop until it reaches a goal. The "agent" part refers to this ability to act with some autonomy inside a defined set of tools and rules.

This is different from a plain chatbot. A chatbot takes your message and returns text. An agent can look up a customer record, call a payment API, search the web, write a file, or trigger another process — then use the output of that action to decide its next step. The model is still the reasoning engine, but the agent wraps it with tools, memory, and a control loop that lets it operate over multiple steps instead of one.

The Core Components of an AI Agent

Most working definitions of an AI agent converge on the same four pieces:

• A model — the LLM that reasons about the task and decides what to do
• Tools — functions or APIs the model can call (search, database queries, code execution, third-party services)
• A loop — the logic that sends the model's tool requests out, gets results back, and feeds them back in
• State/memory — the running context of what's happened so far in the task

Remove any one of these and you don't really have an agent anymore. A model with no tools is just a chat interface. Tools with no loop is a one-shot function call. A loop with no memory can't handle multi-step tasks that depend on earlier results.

How the Agent Loop Actually Works

A typical agent cycle looks like this:

1. The user (or a system) gives the agent a goal, e.g. "find the three cheapest flights to Lisbon next week and summarize them"
2. The model decides it needs external data and requests a tool call — say, a flight-search API
3. The application executes that tool call and returns the raw result to the model
4. The model reads the result, decides if it has enough information, and either calls another tool or produces a final answer
5. This repeats until the model stops requesting tools and returns a response

The key detail is that the model itself decides when to call a tool and which one — the application doesn't hardcode the sequence. That's what separates an agent from a traditional script that calls APIs in a fixed order.

// Simplified agent loop
let messages = [{ role: "user", content: "Find cheap flights to Lisbon next week" }];

while (true) {
  const response = await callModel(messages, { tools });
  if (response.tool_calls) {
    for (const call of response.tool_calls) {
      const result = await runTool(call.name, call.arguments);
      messages.push({ role: "tool", content: result });
    }
  } else {
    console.log(response.content); // final answer
    break;
  }
}

Agents vs. Workflows vs. Assistants

These terms get used loosely, so it's worth separating them:

• Assistant / chatbot: single-turn or multi-turn conversation, no tool use, no autonomous action
• Workflow / pipeline: a fixed sequence of steps, some of which may call an LLM, but the order is predetermined by code, not decided by the model
• Agent: the model itself decides the sequence of actions, which tools to call, and when the task is complete

Many production systems are actually hybrids — a workflow with an agent embedded at one step, or an agent with hard limits on which tools it's allowed to call and in what order. That's usually the right call. Fully open-ended agents are harder to test and more expensive to run than a well-scoped agent with a small, well-defined toolset.

What Agents Are Used For

Common real-world uses of AI agents include:

• Customer support — looking up order status, issuing refunds, updating account details
• Research and summarization — searching multiple sources, cross-referencing, producing a synthesized report
• Code assistance — reading a codebase, running tests, making edits, checking the result
• Data extraction and processing — pulling structured data from documents or APIs and transforming it
• Internal ops automation — triaging tickets, routing requests, updating internal systems

What these have in common: the task requires more than one step, and the right next step depends on the outcome of the previous one. If a task can be solved with a single prompt and no external data, you don't need an agent — a direct API call is simpler, cheaper, and easier to debug.

Building the Underlying Model Access

Whatever framework or loop you build on top, an agent needs reliable programmatic access to a model: predictable request/response format, streaming for long-running steps, structured tool-calling, and usage data so you can track cost per task. If you're already using Claude through a subscription and want to wire it into an agent loop, SubToAPI turns that access into a standard HTTPS API — issue an application key (sub_live_...), call the Messages endpoint, and get streaming and tool use support without setting up separate billing. It's not an agent framework itself — it's the model-access layer an agent loop calls into. Plans start at €9/month with a free trial, see pricing.

questions

Is an AI agent the same as a chatbot? No. A chatbot returns text in response to a message. An agent can call tools or APIs, use the results to decide its next step, and keep going until a goal is reached, without a human directing each step.

Do I need an agent framework to build an agent? Not necessarily. A basic agent loop is just a while-loop that sends messages to a model, executes any requested tool calls, and feeds the results back — frameworks add convenience (memory, retries, multi-agent orchestration) but aren't required to get started. See a quickstart for the model-call basics.

What's the difference between an agent and a workflow? A workflow follows a fixed, predetermined sequence of steps written in code. An agent has the model decide, at runtime, which tools to call and in what order based on the task and intermediate results.