A chatbot answers one message at a time. An agent takes a goal, breaks it into steps, acts, looks at what happened and decides what to do next. Most agents today are built on a large language model connected to tools, with limits on what they may do without your approval.

How it works

Most agents run a loop. First, a large language model reads the goal plus any instructions, memory and previous results. Second, it decides on the next action, usually by producing a structured tool call such as 'search the web for X', 'read this file' or 'send this email draft'. Third, the surrounding software actually runs that tool and feeds the result back into the model's context window. The loop repeats until the model decides the goal is met, hits a step limit or asks a human to confirm. Key components are the model (the reasoning engine), the tools it can call (often exposed through APIs or the Model Context Protocol), memory (short-term context plus sometimes a database of past notes) and guardrails (permissions, spending limits, approval steps). Some systems split work across several agents, for example a planner and a checker. Because every step adds a chance of error, reliability drops on long tasks, which is why well-designed agents check their own work and keep humans in the loop for risky actions.

Why it matters

Agents move AI from drafting to doing. A creator can hand off repetitive chores such as renaming files, compiling research or posting to several platforms. A small business can let an agent triage support emails or update a spreadsheet from invoices. The value is time saved on tasks with clear goals and checkable results. The risk is that an agent acts with your accounts and permissions, so a mistake is a real action, not just a bad sentence. Knowing how agents work helps you decide what to delegate and what to keep manual.

Where you will see it

  • Researching a topic across many web pages and returning a sourced summary
  • Triaging an inbox and drafting replies for human approval
  • Fixing a bug by reading code, running tests and editing files
  • Turning a published video into captions, a blog post and social posts

Limits and common misconceptions

  • Errors compound: a small mistake early in a long task can derail every later step.
  • Agents can be tricked by malicious text on web pages or in files, known as prompt injection.
  • They are not autonomous minds; they follow a programmed loop and can only use the tools they are given.

How it differs from related terms

  • AI agent vs Workflow automation Workflow automation follows fixed rules you define in advance, while an agent decides its own next step at run time.
  • AI agent vs Chatbot A chatbot replies to messages; an agent takes actions in other software to complete a goal.

Tools that use it

Frequently asked questions

What is the difference between an AI agent and a chatbot?

A chatbot answers in conversation and waits for your next message. An agent is given a goal and works through several steps on its own, calling tools such as search, files or apps, and checking results between steps. Many chatbots now include agent features, so the line is about behaviour rather than brand.

Are AI agents safe to use?

They can be, with sensible limits. Give an agent only the access it needs, require approval for actions like payments, sending emails or deleting files, and review its output. The main risks are mistakes carried out with your permissions and prompt injection, where hidden instructions in content mislead the agent.

Do I need to code to build an AI agent?

No. No-code platforms let you connect a language model to apps with visual builders, and several assistants offer built-in agent modes. Coding gives more control over tools, memory and error handling, which matters for business-critical tasks.

Related terms

All AI terms