Use a chatbot for a bounded conversation—such as answering a question, explaining a topic, or drafting text. Choose an AI agent when you need a system to pursue a goal through multiple steps, use tools, inspect the results, and decide what to do next. If the steps are known and repeatable, a workflow or ordinary function may be simpler and more predictable than either.
The distinction is not the interface: an agent can have a chat interface, and a chatbot can use tools. The key question is how much control the system has over the process and whether it can take action on your behalf.
What is the difference between an AI agent and a chatbot?
A chatbot is generally designed to respond to a person in a conversation. It can answer, explain, brainstorm, draft, or retrieve information. An AI agent is set up to work toward a goal by directing its own process: it can choose steps, call tools, observe what happens, and adjust its approach.
Anthropic describes an agent as operating in a loop of planning, acting, observing, and adjusting until it finishes or needs human input. That loop—not whether the product looks like a chat window—is the useful distinction. A conversation can be the way you give an agent instructions or review its work.
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Which should you use?
| Your task | Better starting point | Why |
|---|---|---|
| Ask a one-off question, get an explanation, brainstorm, or draft something | Chatbot | The main output is a response for you to assess; autonomous execution may add little. |
| Follow known steps in a stable order, using clear rules | Workflow or function | A predefined path is easier to predict and control. Microsoft recommends a function if it can handle the task. |
| Handle unstructured inputs, changing conditions, exceptions, or several decisions | Agent, with guardrails | An agent may be useful when it needs to make choices and use tools as the situation develops. |
| Take high-impact actions, or work where mistakes are hard to detect | Human-led or human-reviewed process | Keep a person responsible for checking consequential work and approving actions. |
These are starting points, not guarantees. Anthropic recommends using the simplest solution that meets the need; an agent can add latency and execution complexity. Microsoft likewise distinguishes open-ended work that needs autonomous planning from well-defined work with an explicit execution order. See Anthropic’s guide to building effective agents and Microsoft’s overview of its agent framework.
What does an agent add?
An agent typically combines a model that makes decisions, tools it can call, and instructions that define its task and limits. Tools may let it read information from documents, databases, CRM systems, or the web; make changes such as updating a record or sending a message; or coordinate with other agents. OpenAI outlines these components in its practical guide to building agents.
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The agent’s distinguishing feature is its control loop: it can use information returned by a tool to choose or revise its next step. In an example from Anthropic, an expense-submission agent could transcribe receipts, extract amounts and vendors, categorize expenses, and submit them. If it notices a policy issue or lacks required information, it could ask for permission or clarification before continuing. This is a vendor example, not an independent performance test; see Anthropic’s discussion of trustworthy agents.
How to assess whether an agent is appropriate
Before delegating a task, assess its repeatability, potential impact, how easily an error can be detected, and how time-sensitive it is—the criteria Microsoft recommends considering. Ask these practical questions:
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- Task fit: Does the job end with an answer, or does it require several steps using tools?
- Predictability: Are the steps stable enough to encode in a workflow?
- Permissions: What information can the system read, and what can it change, send, or submit?
- Oversight: Can a person approve sensitive steps, intervene, or stop the work?
- Error detection: Can someone verify the result before it has consequences?
- Time and complexity: Do the benefits of flexible execution justify added latency and operational complexity?
Microsoft’s guidance is direct: “Delegating work to AI doesn’t transfer accountability.” Its decision guide for Copilot or an agent recommends reviewing and validating AI output. For sensitive actions, set approval boundaries and keep a way to pause or stop execution when the product supports those controls.
What can go wrong, and what safeguards help?
An agent with permission to act can misunderstand what you want and cause an unintended side effect. It may also be vulnerable to prompt injection: malicious content in material it reads may try to steer it toward actions you did not request. Anthropic identifies these as risks of agents that act with less human oversight.
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Reduce the risk by limiting access to what the task needs, requiring approval for consequential actions, and making the result reviewable. Do not treat a product’s “agent” label as evidence that its autonomy or controls are suitable for your work. Check the actual permissions and intervention options available in that product.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What do current agent counts tell you?
The authors of The 2025 AI Agent Index, published for FAccT ’26 in 2026, assessed 30 agents in their index. Their findings describe that sample, not every agent on the market:
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| Finding in the index | What it describes |
|---|---|
| 20 of 30 agents supported MCP | The index sample; not a market-wide adoption estimate. |
| 23 of 30 agents were fully closed at the product level | The product-level status reported for the index sample. |
| 20 of 30 agents documented pause or stop mechanisms | Controls varied by category and product. |
| 14 of 30 agents had chat interfaces for end-user operation | Chat interfaces and agent behavior can coexist. |
The index also reports that autonomy varies within a product and that higher autonomy is not necessarily better. These figures are useful context, not a controlled comparison of chatbot and agent reliability, cost, or performance. For a specific product, test representative tasks and verify its output before using it for consequential work. The findings are in The 2025 AI Agent Index.
How to choose between specific products
Compare products against the job and the controls it needs—not just the category name. A chatbot may be enough if you want a response to review. An agent may fit a task involving several tool-mediated decisions. If the sequence is fixed, a workflow may be the clearer choice.
The sources cited here do not provide a controlled, like-for-like benchmark of chatbot and agent reliability or total cost across products. Evaluate a candidate on representative inputs, inspect what it can access and change, and confirm that a person can check or interrupt work where needed.
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