What Are AI Agents? A Plain-English Guide for Builders
An AI agent is a system that uses a language model to decide what actions to take, then actually takes them — calling APIs, running code, querying a database, or invoking other tools — and uses the results to decide what to do next. The key difference from a regular chatbot is the loop: a chatbot answers a question and stops, while an agent can act, observe the outcome, and act again until it reaches a goal.
If you've asked "what are AI agents" because you're evaluating whether to build one for a product, the short answer is: they're software that combines an LLM's reasoning with the ability to call external tools in a repeated cycle, and they're useful whenever a task requires multiple steps, real-world data, or side effects that a single text response can't provide.
The Core Loop
Every AI agent, regardless of framework or vendor, follows roughly the same cycle:
- Receive a goal — a user request, a scheduled trigger, or an event.
- Reason — the model decides what to do next based on the goal and any information it already has.
- Act — it calls a tool (an API, a function, a search, a database query).
- Observe — the result of that action is fed back into the model's context.
- Repeat or finish — the model either takes another action or produces a final answer.
This is sometimes called the "think-act-observe" loop, or ReAct (reason + act) in academic papers. The important part isn't the terminology — it's that the model isn't just generating text once. It's making a series of decisions, and each decision can change what happens next.
Agent vs. Chatbot vs. Workflow
These three terms get mixed up constantly, so it's worth being precise:
- A chatbot takes input, generates output, done. No tool use, no multi-step planning. Good for Q&A, summarization, drafting.
- A fixed workflow (sometimes called a "pipeline" or "chain") runs a predetermined sequence of steps — always step A, then B, then C. It might call APIs, but the order and logic are hardcoded by a developer, not decided by the model.
- An agent decides the sequence itself. Given a goal, it figures out which tools to call, in what order, and when it has enough information to stop.
In practice, most production systems sit somewhere between a workflow and a full agent. A rigid pipeline is more predictable and cheaper to run; a fully autonomous agent is more flexible but harder to test and can burn a lot of tokens exploring dead ends. Many teams build "semi-agentic" systems: a workflow with a few points where the model chooses between a small set of predefined branches.
What Makes Something an Agent, Technically
Three ingredients turn a plain LLM call into an agent:
- Tools — functions or APIs the model can invoke, usually described with a name, a description, and a JSON schema for arguments. See /docs/tools for how tool definitions typically look.
- Memory / state — the running conversation, plus any intermediate results, so the model has context across multiple steps.
- A control loop — code that takes the model's tool call, executes it, feeds the result back, and asks the model what to do next, until it returns a final answer instead of another tool call.
Here's a minimal version of that loop in JavaScript, using a Claude-compatible messages API:
async function runAgent(userGoal, tools) {
let messages = [{ role: "user", content: userGoal }];
while (true) {
const response = await fetch("https://api.subtoapi.app/v1/messages", {
method: "POST",
headers: {
"Authorization": `Bearer ${process.env.SUBTOAPI_KEY}`,
"Content-Type": "application/json"
},
body: JSON.stringify({
model: "claude-sonnet-4-5",
max_tokens: 1024,
messages,
tools
})
}).then(r => r.json());
const toolCall = response.content.find(c => c.type === "tool_use");
if (!toolCall) {
return response.content; // final answer, loop ends
}
const result = await executeTool(toolCall.name, toolCall.input);
messages.push({ role: "assistant", content: response.content });
messages.push({
role: "user",
content: [{ type: "tool_result", tool_use_id: toolCall.id, content: result }]
});
}
}That's the whole pattern. Everything else — planning strategies, sub-agents, retries, guardrails — is built on top of this basic act-observe cycle.
Common Types of AI Agents
- Single-tool agents — call one API repeatedly (a coding agent that only edits files, a research agent that only searches the web).
- Multi-tool agents — choose from a toolbox of several functions per task (a support agent that can look up orders, issue refunds, and search docs).
- Multi-agent systems — several specialized agents coordinate, often with one acting as an orchestrator that delegates subtasks to others.
- Autonomous agents — run with minimal human checkpoints, deciding when they're done; riskier, and usually need strict tool permissions and budget limits.
Where This Gets Practical
If you're building an agent, the model call itself needs to be reliable, fast, and metered like any other API dependency — because an agent might make dozens of calls per task. SubToAPI turns your existing Claude access into a standard HTTPS API with application keys (sub_live_...), streaming responses, tool-use support, and per-key usage metadata — useful when you're running an agent loop in production and need to track cost per feature or per customer. See /docs/quickstart for setup and /docs/streaming for handling incremental output during long agent runs.
When You Actually Need an Agent
Not every feature needs one. Use a plain LLM call when the task is a single transformation — summarize, translate, classify. Use a fixed workflow when the steps are known in advance and always run in the same order. Reach for an agent when:
- The number and order of steps genuinely depends on what happens at each step.
- The task requires querying external systems whose results change the next action.
- You need the system to keep working toward a goal across an unknown number of tool calls.
Building an agent adds real complexity — error handling, loop limits, cost control — so it's worth confirming a simpler approach won't do the job first.
questions
Is an AI agent the same as a chatbot with plugins? Close, but not identical. A chatbot with plugins can call a tool once per turn; a true agent loops — it can call multiple tools across multiple steps, reasoning about each result before deciding the next action, without waiting for a new user message each time.
Do AI agents need a specific framework to work? No. The core pattern — model call, tool execution, feed result back, repeat — can be built with plain API calls, as shown above. Frameworks (like agent SDKs) add convenience for memory, retries, and multi-agent coordination, but they're not required to get started.
What's the biggest risk with autonomous AI agents? Runaway loops and unintended actions — an agent that keeps calling tools without reaching a stopping condition, or takes an irreversible action (like sending an email or making a payment) based on a misreading of the task. Most production agents cap the number of steps and require confirmation for high-impact tool calls.