INSIGHTS · A PRACTICAL INTRODUCTION
What are AI agents?
How they work, where they help, and why their limits matter.
An AI agent is a software system that uses artificial intelligence to work toward a goal, choosing actions and responding to what happens along the way. Depending on its permissions, it might search documents, use an application, write code, or ask a person for a decision.
The useful question is not simply whether software is called an agent. It is what the system can do, what it can access, and how you can tell whether it completed the task correctly.
How do AI agents work?
A typical language-model agent combines a model, instructions, access to tools, and information about its task. Some systems also retain information between sessions. Implementations vary, and the word “agent” does not describe a single standard architecture.
- Understand the goal. Identify the requested outcome and any constraints.
- Choose a next step. Decide what information or action could move the task forward.
- Use an available tool. Search, read a file, run a permitted operation, or interact with software.
- Check the result. Use the returned information to decide whether to continue, correct course, or ask for help.
- Stop and report. Return the result or explain what prevented completion.
This feedback loop matters. A successful tool call is not automatically a successful task: saving a document, for example, does not establish that its numbers are correct.
AI agents, chatbots, and automation
| System | Main behavior | Illustrative task |
|---|---|---|
| Basic chatbot | Responds within a conversation | Explain how to prepare a customer report |
| Fixed automation | Follows predetermined rules or steps | Email a report every Friday |
| AI agent | Selects steps using the goal and feedback | Find the relevant records, investigate gaps, and prepare a report for review |
These categories overlap. A chat interface can connect to an agent, and an agent can run inside a fixed workflow. A conversational interface alone does not reveal how much autonomy a system has.
Practical examples of AI agents
The following are illustrative tasks, not promises about a particular product.
Research and document preparation
A research agent could gather permitted sources, compare their claims, identify missing information, and prepare a referenced brief. A reviewer still needs to check whether the sources support its conclusions.
Software development
A coding agent could inspect a project, propose a change, edit files, and run checks. Passing checks provides useful evidence, but the change still needs to meet the actual requirements.
Customer support
A support agent could look up an order, explain a policy, and prepare a resolution. Access to customer records and permission to issue a refund are separate decisions.
Everyday administration
An administrative agent could compare available appointment times and draft an invitation. Whether it may send that invitation should be explicit before it acts.
What are the limitations?
Agents can misunderstand instructions, rely on incomplete information, or produce plausible but incorrect answers. In a multi-step task, an early mistake can affect later decisions. More autonomy does not remove the need to verify outcomes.
- Access is not judgment. Being able to open a file does not mean the system should share it.
- External content can mislead. Instructions embedded in a webpage or document can attempt to redirect an agent away from the user's goal.
- Tools can fail. A changed interface, unavailable service, or missing permission may interrupt the task.
- Costs can accumulate. Repeated model calls and tool operations can consume time and resources.
- Completion needs evidence. A confident statement should be checked against a saved result, a verified record, or another appropriate outcome.
How to start using an AI agent
Choose one bounded task with an outcome you can assess. For example: “Read these five approved documents and create a comparison table with a source for each claim.” That is easier to evaluate than “manage my business.”
Specify the permitted information, available actions, spending or time limits, and decisions that require your review. Test with reversible work first. Compare the result with doing the task yourself, including the time needed to check and correct it.
A practical deployment question is: Can the system complete this particular job reliably enough, within these permissions, at an acceptable cost? That question is more useful than choosing software by its label alone.
Frequently asked questions
Are all AI agents fully autonomous?
No. Autonomy varies by design and permission. Some systems prepare work for review; others can execute a defined set of actions independently.
Do AI agents need access to the internet?
Not necessarily. An agent can work with local files or internal tools if those provide what the task requires.
Can an AI agent replace a whole workflow?
Sometimes parts of a workflow can be delegated, but suitability depends on the task, integrations, error tolerance, and oversight. Start by testing a specific job rather than assuming end-to-end replacement.
Further reading
For a technical discussion of agent loops, tools, workflows, and the trade-offs involved, see Anthropic's Building effective agents (originally published December 2024). This introduction uses those architectural distinctions; the examples and evaluation questions above are illustrative.