Artificial intelligence has moved past simple question-and-answer chatbots. The newest wave of tools, often called AI agents, can plan a task, choose which tools to use, take multiple steps on their own, and check their own work before handing back a result. Instead of just answering a prompt, an AI agent tries to actually get something done.
This shift matters for anyone who uses AI tools for work, from developers automating code reviews to marketers scheduling social posts. Here’s what AI agents actually are, how they’re different from the chatbots most people are used to, and where they’re already being used.
What Are AI Agents?
An AI agent is a system built around a large language model (LLM) that can decide, on its own, what steps to take to complete a goal. Anthropic, the company behind Claude, draws a useful line between “workflows” and “agents” in its engineering guidance: workflows are systems “where LLMs and tools are orchestrated through predefined code paths,” while agents are systems “where LLMs dynamically direct their own processes and tool usage, maintaining control over how they accomplish tasks.”
In plain terms: a workflow follows a script a developer wrote in advance. An agent decides its own script as it goes, based on what it’s trying to achieve and what it learns along the way.
How AI Agents Differ From Chatbots
A standard chatbot responds to one message at a time. You ask a question, it answers, and the interaction ends unless you prompt it again. An AI agent, by contrast, can:
- Break a large goal into smaller steps without being told each one
- Call external tools, such as a web browser, a code interpreter, a calendar app, or a company database, to gather information or take action
- Check the results of its own actions and adjust course if something didn’t work
- Keep working across multiple steps until the goal is met or it needs human input
That autonomy is powerful, but it’s also the reason agents need careful design. Anthropic’s guidance stresses starting simple and only adding complexity when it demonstrably improves outcomes, since more autonomy also means more chances for a small error to compound into a bigger one.
How an AI Agent Actually Works
Most AI agents run on some version of a plan-act-observe loop:
- Plan: The model interprets the goal and decides what steps are needed.
- Act: It calls a tool, such as searching the web, running code, or querying a database, to move toward that goal.
- Observe: It looks at the result of that action and decides what to do next: keep going, try a different approach, or report back that the task is done.
This loop can repeat many times for a complex task, all without a human approving each individual step, though most production agent systems still include checkpoints where a person reviews or approves before anything high-stakes happens, like sending a payment or publishing content.
Real Examples of AI Agents in Use
Agent tools have moved from research demos to products people can actually use:
- OpenAI’s AgentKit, introduced at OpenAI’s DevDay, gives developers a visual builder and a set of APIs specifically for building, deploying, and evaluating agents that can browse, run code, and call outside tools.
- Anthropic has published detailed engineering guidance on building agents with Claude, including how to design tools an agent can use reliably and how to manage an agent’s context as tasks get longer.
- Microsoft has been extending Copilot with agent-style features across its 365 apps, moving from a tool that answers questions to one that can carry out multi-step tasks like drafting a series of emails or updating a spreadsheet based on new data.
You may have already used a simplified version of this: coding assistants like the ones covered in our guide to AI coding assistants use agent-style behavior to write, test, and fix code across multiple files without a developer manually approving every line.
Benefits and Limitations
The upside is real: agents can handle repetitive, multi-step busywork that used to require a human stitching several tools together, similar to how the automation tools many teams already rely on chain simple triggers, except an agent can reason about what to do next rather than following a fixed rule.
The limitations are just as real. Agents can misinterpret a goal, get stuck in a loop, or take an action that seemed reasonable to the model but wasn’t what the user actually wanted. Costs also add up, since an agent may call a model multiple times to complete a single task. That’s why most serious deployments keep a human in the loop for anything irreversible or high-stakes.
Getting Started With AI Agents
If you want to try agent-style AI without building anything from scratch, the easiest entry points are the tools already built into products you may use, such as Copilot’s agent features in Microsoft 365, or a custom agent built with OpenAI’s AgentKit or Anthropic’s Claude. For a lower-effort starting point, our guide on how to build an AI chatbot for your website covers the basics of connecting an LLM to tools and data, which is the same foundation agent systems build on.
FAQs
Is an AI agent the same thing as a chatbot?
No. A chatbot responds to prompts one at a time. An AI agent can take a broader goal, break it into steps, use outside tools, and keep working across multiple actions without a person prompting each step.
Do AI agents work without any human oversight?
Rarely, in practice. Most production agent systems include checkpoints where a person reviews or approves actions that are high-stakes or hard to reverse, such as sending money or publishing content publicly.
What can AI agents actually do today?
Current agent tools can browse the web, write and run code, manage files, and take multi-step actions inside apps like email or spreadsheets. Capabilities vary a lot by platform and are still evolving quickly.
Are AI agents only for developers?
No. While developers build custom agents with tools like AgentKit, many people already use agent-style features inside everyday products, such as Copilot in Microsoft 365 or agent-assisted coding tools.
What’s the risk of using AI agents?
The main risks are compounding errors, where a small mistake early in a multi-step task can throw off everything after it, and unpredictable costs, since an agent may call a model many times to finish one task. Careful design and human checkpoints reduce both risks.
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