An AI agent is software that uses artificial intelligence to work towards a goal, choose actions and use tools to complete a task. A language model supplies part of the reasoning, while connected tools let the system retrieve information or act on it. The useful question is what the software can do, what access it has and when it must return control to a person.
Google Cloud's explanation of AI agents describes systems that pursue goals for users and combine capabilities such as planning, observation and action. That definition covers a range of software. It does not mean every application labelled an agent has the same abilities, permissions or reliability.
| Part | What it contributes |
|---|---|
| Goal | The result the user wants |
| Model | Interprets instructions and helps decide the next step |
| Tools | Read information or carry out permitted actions |
| Feedback | Shows what happened after an action |
| Human checkpoint | Lets a person review a decision or resolve uncertainty |
How does an AI agent work?
Cisco's overview describes a repeating process of planning, execution and feedback. A system receives an objective, breaks it into smaller tasks and interacts with available software. The information returned by an action can change what it does next. This is why a working connection to a tool matters as much as a persuasive answer in the chat window.
Consider this invented example: a college society wants a list of application deadlines from three university notices. An agent with permission to read those documents could identify the relevant sections, extract the dates and prepare a table. If one notice refers to a later correction, it could look for that correction before completing the table. This illustrates a possible workflow, not a test result for a particular product.
The boundary is equally important. Permission to read a notice does not, in this example, include permission to submit an application. A useful instruction would specify the institutions, the documents to consult, the output format and what to do if two notices disagree. The desired result is a checkable table with sources, not an unsupported claim that every deadline has been found.
AI agent vs chatbot: what is the difference?
A conversation interface alone tells you little. Google describes assistants that interact with a person through different stages of a task, and agents that can handle multiple actions with greater autonomy. A chatbot may also have tools. Looking for a chat box, or counting how long an answer is, therefore does not establish whether the underlying system can carry out a goal.
A more precise distinction comes from Anthropic's guide to building effective agents. It separates workflows with predetermined paths from agents whose language models direct their processes and tool choices dynamically. The distinction concerns how the work is organised. Neither label, by itself, is evidence that an answer is correct.
For another illustrative example, a fixed workflow might always read a form, extract its fields and place them into a template. An agent handling an incomplete form might decide which supporting document to inspect next. If the missing information is unavailable, the appropriate result could be a question for the user. Inventing the missing field would not complete the task properly.
What are AI agents used for?
Cisco describes agents interacting with data, applications and other agents to complete tasks involving several steps. Anthropic discusses customer-support systems that can consult account information and update support records, as well as coding systems that use test results as feedback. These are examples of software capabilities and design patterns, not promises about every service sold under the same label.
Readers following workplace applications can see our coverage of Google's Gemini agent for work. The terminology is easier to assess when connected to a specific permitted action: searching a document, preparing an output or updating a record.
What should you check before using an AI agent?
Anthropic warns that errors can compound as agents operate and recommends testing, suitable safeguards and stopping conditions. Its guide also recommends the simplest system that meets the need. A short, well-defined task may not need an agent at all. Adding autonomy creates more decisions to evaluate, rather than removing the need to check the result.
For the university-notice example, a practical review would compare every extracted deadline with its source, flag unclear dates and confirm that nothing was submitted. That is an editorial checklist for the example, not an industry certification. The same habit of asking who controls an action is relevant to our coverage of AI oversight in banking and AI use in legal education.
