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Short answer: An AI agent is software that uses an AI model to pursue a goal on its own: it decides what to do next, uses tools to do it and keeps going until the job is done. A chatbot answers a message. An agent takes a task such as “fix this bug” or “research this market” and works through the steps. Anthropic defines agents as “systems where LLMs dynamically direct their own processes and tool usage.”
This guide gives you a definition you can repeat, shows how an agent works inside and walks through real examples from the Klyvoo directory, which sorts agents by the job they do. If you want to browse by job first, start with the best AI agents hub.
AI agents at a glance
| Definition | Software where an AI model decides its own steps and uses tools to finish a goal |
| What it needs | A model, instructions, tools and a way to remember what it has done |
| How it differs from a chatbot | A chatbot replies. An agent acts across several steps |
| Typical jobs | Writing code, researching, answering customers, booking meetings |
| Examples | Claude Code, Perplexity, Fin by Intercom |
| Main strength | Handles open-ended work where the steps are not known in advance |
| Main risk | Errors compound across steps, so it needs limits and review |
| Where to start | Pick one narrow job and one agent built for it |
What is an AI agent?
An AI agent is a system where a language model runs a loop. It looks at the goal, picks an action, uses a tool, reads the result and decides what to do next. It repeats until it thinks the task is finished or it needs a person.
OpenAI’s agent guide describes the same idea: agents can plan and complete tasks using tools, work with other agents and “maintain context across steps.” Three parts matter there:
- Planning. The agent chooses its own steps instead of following a script.
- Tools. It can search the web, read files, run code, call an API or send a message.
- Memory across steps. It keeps track of what it already tried, so step five builds on step four.
Take away the tools and you have a chatbot. Take away the loop and you have a single prompt. Both are useful, but neither is an agent.
How is an AI agent different from a chatbot?
A chatbot waits for your message and answers it. If you ask it to book a table, it can tell you how. An agent can go and book the table: it checks availability, picks a slot and confirms.
| Chatbot | AI agent | |
|---|---|---|
| Starts with | A question | A goal |
| Steps | One reply | Many, chosen by the model |
| Tools | Usually none | Search, files, code, apps and APIs |
| Ends when | It has answered | The task is done or it needs help |
| Can change things | No | Yes, within the access you give it |
| Cost and risk | Low | Higher, because each step can go wrong |
The line is blurry in practice. Many chat products now include agent features, such as web search or code execution. A useful test is simple: did the software decide its own next step and act on it? If yes, treat it as an agent.
How does an AI agent work?
Every agent follows the same cycle, whatever the product.
- You give it a goal. “Find the three biggest competitors and summarize their pricing.”
- It plans. The model breaks the goal into steps and picks the first one.
- It acts. It calls a tool, for example a web search.
- It reads the result. The output goes back into the model as new information.
- It decides again. Is the goal met? If not, it picks the next action.
- It hands back the result. You get a report, a code change, a reply or a booked meeting.
The tools are where most of the power comes from. Anthropic’s engineering team says tool definitions deserve “just as much prompt engineering attention as your overall prompts.” A well-described tool gets used correctly. A vague one gets misused.
Many agents connect to tools through the Model Context Protocol, an open standard for linking AI tools to outside data sources. Claude Code, for example, uses it to read design docs, update tickets and pull data from other apps. If that term is new, read What Are MCP Servers? and browse the MCP servers directory.
Workflows vs agents
Not everything called an agent is one. Anthropic separates two designs:
- Workflows are systems “where LLMs and tools are orchestrated through predefined code paths.” A developer writes the route and the model fills in steps along it.
- Agents are systems where the model chooses the route itself.
Anthropic describes five common workflow patterns: prompt chaining, routing, parallelization, orchestrator-workers and evaluator-optimizer. Each one fixes the structure in advance and lets the model work inside it.
This matters when you buy or build. A workflow is more predictable and cheaper to run. An agent copes better when you cannot predict the steps. Anthropic recommends starting with the simplest solution and adding complexity only when it is needed. Its guide says agents fit open-ended problems where “you can’t hardcode a fixed path.”
AI agent examples you can try
These three come from the Klyvoo directory. They do different jobs, and each one has the loop described above.
A coding agent: Claude Code. Anthropic describes it as an agentic coding tool that reads your codebase, edits files, runs commands and plugs into your development tools. You describe a change in plain language. It plans the approach, edits files across the project and checks that the result works. It runs in the terminal, in IDEs, in a desktop app and in the browser. Other tools in this group are on the best AI coding agents page.
A research agent: Perplexity. Perplexity says its Deep Research mode runs dozens of searches, reads hundreds of sources and reasons about what to do next before it writes a report. You get a cited report instead of a list of links. Compare it with similar tools on the best AI research agents page.
A customer support agent: Fin by Intercom. Intercom calls Fin “a single customer facing Agent that works across the customer journey.” It can take actions in other systems, such as updating an account or processing a refund, and it works across voice, chat, email, Slack and social channels. See the best AI customer support agents for alternatives.
All three have a clear job, access to the right tools and a defined finish line. That pattern shows up in every agent that works well.
What can AI agents not do?
Be clear about the limits before you hand over real work.
- They make mistakes, and mistakes stack. Anthropic notes that agents cost more than simple setups and can compound errors, which is why it recommends extensive testing in sandboxed environments.
- They are only as good as their tools. An agent with the wrong access or a poorly described tool will fail in odd ways.
- They need limits. Anything that can change data, send messages or spend money should have guardrails and a review step. OpenAI’s Agents SDK includes guardrails for validating agent inputs and outputs for this reason.
- They are not a replacement for judgment. Use them for the repeatable middle of a job and keep a person on the decisions that matter.
- Open-ended is not always better. If you know the steps, a plain workflow is often cheaper and more reliable.
How do you choose an AI agent?
Start from the job, not the technology.
- Name one job. “Answer customer questions” beats “improve my business.”
- Check the finish line. Can you tell when the work is done and whether it is right?
- List the access it needs. Files, a calendar, a help desk. Give the least it needs.
- Start with a ready-made agent. The Klyvoo directory groups agents by job so you can compare tools built for the same task.
- Test on low-stakes work first. Review the output until you trust the pattern.
If you would rather build your own, read How to Build an AI Agent. Frameworks such as the OpenAI Agents SDK give you the pieces: agents with instructions and tools, handoffs between agents and guardrails. The full list is on the best AI agent frameworks page. And if you are brand new, AI Agents for Beginners covers what to try first.
FAQ
What is an AI agent in simple terms?
It is software that takes a goal and works toward it on its own. It decides the steps, uses tools such as search or code and keeps going until the task is done. A chatbot only answers the question you ask.
What is the difference between an AI agent and ChatGPT?
ChatGPT in its basic form is a chatbot: you ask and it answers. It can act like an agent when it uses tools and works through several steps toward a goal. The difference is the loop. An agent decides its next action and carries it out.
What are examples of AI agents?
Claude Code writes and edits software. Perplexity’s Deep Research runs many searches and writes a cited report. Fin by Intercom answers customer questions and takes actions such as refunds. Each does one job end to end.
Are AI agents safe to use?
They can be, with limits. Give an agent only the access it needs, test it on low-stakes work and keep a person in the loop for anything that changes data or spends money. Errors can compound across steps, so review matters.
Do I need to code to use an AI agent?
No. Most agents in the Klyvoo directory are ready-made products you set up in a browser or an app. Coding only matters if you want to build a custom agent with a framework.
What is the difference between a workflow and an agent?
In a workflow, code decides the route and the model works inside it. In an agent, the model decides the route. Workflows are more predictable. Agents suit open-ended problems where the steps are not known in advance.
Ready to see agents sorted by job? Open the best AI agents hub to compare tools side by side. For the official thinking behind the term, read Anthropic’s Building effective agents and OpenAI’s agents guide.