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What Is an AI Agent? (And How It's Different From a Chatbot)

An AI agent is a model in a loop: it decides on an action, uses a tool, looks at the result, and repeats until the job is done. How agents work, real examples, what makes them useful and risky, and how to tell a real agent from a renamed chatbot.

"Agent" has become one of the most overused words in tech. Underneath the marketing, the idea is simple and genuinely different from a chatbot:

  • A chatbot answers. You ask, it replies, done.
  • An agent acts. You give it a goal, and it decides what to do, does it, checks the result, and keeps going until the goal is reached — or it gets stuck.

The agent loop

Every AI agent, from coding tools to customer-service bots, runs the same basic loop:

  1. Think: given the goal and everything so far, what's the next step?
  2. Act: call a tool — search the web, read a file, run a command, query a database, send an email.
  3. Observe: look at what the tool returned.
  4. Repeat until done.

The model itself can only produce text. Tools are what let it affect the world: your code describes the tools, the model asks to use one, your code runs it and hands back the result. (What is function calling?)

A concrete example

Goal: "Find out why the signup page is broken and fix it."

A coding agent might:

  1. Read the signup page's code.
  2. Run the app and load the page → sees an error.
  3. Read the error, open the file it points to.
  4. Edit the code.
  5. Run the tests → one fails.
  6. Fix that too, run the tests again → all pass.
  7. Report what it changed.

Nobody told it those seven steps. It chose each one based on what it saw. That's what makes it an agent. (What is an AI coding agent?)

Kinds of agents you'll meet

  • Coding agents — Claude Code, Codex, Cursor's agent mode. Read, write and run code. (What is Claude Code?)
  • Browser agents — click through websites to complete tasks.
  • Research agents — search, read and summarise many sources.
  • Workflow agents — triage emails, update a CRM, process documents.
  • Agents inside apps — a support bot that can actually look up your order and issue a return.

What makes agents work well

  • Good tools with clear descriptions and useful error messages.
  • Clear goals and a way to check success — tests for code, a checklist for tasks.
  • Context — the right information, without drowning in irrelevant information. (Context engineering)
  • Somewhere to work — a real environment with files, a terminal and the right access.

What makes them risky

An agent that can act can also act wrongly:

  • Deleting data, sending the wrong email, spending money.
  • Being tricked by text it reads — a web page or document saying "ignore your instructions and…" (Prompt injection)
  • Looping, getting stuck, or confidently declaring success when it hasn't succeeded.

So sensible setups:

  • Give agents only the permissions they need.
  • Require human approval for anything destructive or irreversible.
  • Run them in isolated environments, away from production data. (Stop an AI agent deleting your production database)
  • Keep logs of what they did.

Is it really an agent?

A quick test for product claims: does it choose and take actions on its own, in a loop? If it only answers questions — even cleverly, even with your documents — it's a chatbot or an assistant. Nothing wrong with that; it's just not an agent.

The summary

  • An agent is a model in a loop: think, act with a tool, observe, repeat.
  • Tools are what let it do real things.
  • Agents need good tools, clear goals, context and a workspace.
  • Limit permissions, require approval for risky actions, and isolate them from production.

EasySpawn gives AI agents like Claude Code a persistent server of their own — files, terminal, database and running app — isolated from your other work and still there tomorrow. See how it works or join the waitlist.

Related: What Is an AI Coding Agent? · What Is Function Calling? · What Is MCP? · How Coding Agents Work

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