How do AI agents work?
An AI agent works in a loop: it reads a goal, plans a step, uses a tool (search, code, an app), observes the real result, then decides the next step, repeating until the task is done. The language model is the brain; tools are its hands; the loop is what makes it act instead of just chat.
Why — the first-principles explanation
A plain chatbot does one thing: you send text, it sends text back, and it stops. An agent adds three ingredients on top of that model, planning, tools, and a loop, so it can take actions in the world rather than just describe them.
The engine is a large language model. On its own it only predicts text, so builders give it tools: functions it can call, like a web search, a code runner, a calendar, or a database. The model is told what tools exist and how to ask for them. When it decides a tool is needed, it outputs a structured request; the surrounding software runs that tool and hands the result back.
The magic is the loop. As Anthropic puts it, agents are 'LLMs dynamically directing their own processes and tool usage.' Each cycle is: look at the goal and everything learned so far, pick one action, run it, then observe the real outcome, what Anthropic calls getting 'ground truth from the environment.' That observation feeds the next decision. Because the agent sees actual results (a file that failed to save, a search that returned nothing), it can correct course instead of guessing.
The loop keeps going until the model judges the goal is met or a safety limit (max steps, time, or budget) stops it. Memory ties it together: the running history of steps and results is fed back in so the agent doesn't forget what it already tried. That's the whole trick, model plus tools plus a feedback loop equals something that behaves like it's working, not just talking.
An example that makes it click
Picture asking a friend to plan a birthday dinner. A chatbot friend would just say 'try an Italian place.' An agent friend actually does it: they check a maps app (tool), see the first restaurant is fully booked (observation), so they try a second one, call to confirm a table for six (another tool), notice it's confirmed, then text you the address. Each little action, check, react, act again, is one turn of the loop.
The key detail: the agent friend keeps adjusting based on what really happened. When the booking failed, they didn't pretend it worked, they saw the failure and picked a new plan. That's why agents can finish multi-step jobs, while a chatbot just hands you advice.
How to do it
- Give the agent a clear goal or instruction (for example, 'find three flights under $400 and email me the options').
- The agent (an LLM) breaks the goal into a plan and decides the first action.
- It calls a tool, such as a web search, an API, code execution, or an app, to take that action.
- It observes the real result from the environment, the actual data, success, or error.
- It updates its plan based on that result and chooses the next action, repeating the loop.
- It stops when the goal is met or a guardrail (max steps, time, budget, or human approval) is reached, then reports back.
Key facts
- An AI agent = a language model + tools + a repeating perceive-plan-act-observe loop.
- Anthropic defines agents as LLMs that 'dynamically direct their own processes and tool usage,' unlike fixed workflows.
- At each step the agent gets 'ground truth' from the environment (tool results or code output) to check progress and self-correct.
- Tools are just functions the model can request, such as search, code execution, databases, or third-party apps.
- Guardrails like step limits, sandboxes, and human approval are standard because agents can compound errors across steps.
▶ The 60-second explainer (script)
How do AI agents work? Think of it as a loop, not a chat. A normal chatbot takes your message and replies once, then stops. An agent does more. First, it reads your goal. Then it makes a plan and picks one action. Instead of just talking, it uses a tool, maybe a web search, a calculator, or an app. Here's the important part: it looks at what actually happened. If the search came back empty or a file failed to save, the agent sees that real result and adjusts. Then it loops again: plan, act, observe, decide. It keeps cycling until the job is done or it hits a safety limit like a step cap. So the language model is the brain, the tools are its hands, and the loop is what turns talking into doing. That's the entire secret behind AI agents.
What authoritative sources say
People also ask
What's the difference between an agent and a chatbot?
A chatbot replies to messages and stops. An agent runs a loop, using tools and observing real results, so it can take multi-step actions to finish a task.
What are 'tools' in an AI agent?
Tools are functions the model can call, like web search, code execution, a calendar, or a database. They let the agent act in the world instead of only generating text.
Do AI agents actually think?
Not in a human sense. They predict useful next steps and tool calls based on patterns, then correct themselves using real feedback from each action.
What stops an agent from going haywire?
Guardrails: step and time limits, sandboxed environments, budget caps, and human approval on risky actions, because errors can compound across a long loop.