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A chatbot is built around conversation: it receives text or voice and responds. An AI agent is built to pursue a goal by choosing and taking actions, sometimes across multiple steps and connected tools. The categories can overlap—a chatbot can be the interface to an agent—so the useful question is what the software can do after it replies.
What is a chatbot?
A chatbot is software people interact with through messages or voice. It may use fixed rules, menus, or decision trees; an AI chatbot may also interpret natural language, use context, retrieve information, and generate flexible replies. Some chatbots can perform limited tasks within the conversation, but conversational interaction is the defining feature.
For example, a rule-based support bot might guide someone through a fixed order-status flow. An AI chatbot might understand a natural-language question and retrieve the order details. Both are chatbots because the interaction is organized around a conversation.
What is an AI agent?
An AI agent is software configured to work toward a goal: it interprets inputs or changing context, selects actions, and may carry out a multi-step workflow with limited human involvement. Tools, APIs, memory, and access to external systems can let it do more than return information. NIST’s glossary attributes this definition to NIST AI 100-2e2025: “Software programs that can interact with their environment, receive information, and undertake self-directed actions in service of a larger, externally-specified goal.” NIST’s agent glossary notes the originating document as the context for that wording.
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In Microsoft’s phrasing, “Unlike a large language model, an agent doesn’t only return content for a human to act on.” An agent might, for instance, coordinate a product exchange, update inventory, notify connected business systems, and confirm the result. That is a possible workflow, not a capability every agent has.
What’s the difference between a chatbot and an AI agent?
The shortest distinction is conversation versus action. “Chatbot” describes how people interact with software; “agent” describes its goal-directed role and ability to act. A chatbot normally answers and waits for the person to decide what comes next. An agent may select steps, use tools, and continue toward an outcome.
| Question | Chatbot | AI agent |
|---|---|---|
| What defines it? | Conversational interaction | Goal-directed behavior |
| How does it proceed? | May follow rules or use AI to answer within a conversation | May select steps and actions based on context |
| What happens after a reply? | Often waits for the person to act or ask another question | May use tools or APIs to continue a workflow |
| Can it affect other systems? | Not necessarily; some handle bounded tasks | Potentially, depending on its tools and permissions |
These are not mutually exclusive product categories. An agent can have a chatbot interface, and one product can combine chatbot, assistant, and agent features. IBM also notes that “virtual agent” is used inconsistently: it can mean a chatbot or more advanced customer-service software, depending on the vendor or product. Treat the label as a starting point, not proof of capability. See IBM’s chatbot overview, Google Cloud’s comparison of agents, assistants, and bots, and Microsoft’s terminology guide.
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Yes. A chat window can be the way a person gives instructions to an agent, while the agent performs work behind the scenes. A system that only answers questions in chat is a chatbot; a system that can also select and execute authorized actions toward a goal has agent-like capabilities. The interface alone does not settle the classification.
How to tell what a system actually does
When assessing a tool—or deciding how to describe it—look beyond its name and ask:
- Purpose: Is it mainly designed to converse and answer, or to complete an outcome?
- Control flow: Does it follow a fixed path, or choose among steps based on context?
- Actions: Can it call tools or APIs, write data, or trigger workflows, or does it only give a person content to act on?
- Oversight: Does a person approve every step, review only important actions, or delegate a bounded task?
- State and memory: Does it use only the current prompt, retain conversation state, or remember information across sessions?
- Permissions and effects: Which systems can it reach, under whose identity, and can it send, change, delete, or spend something?
These questions describe capability more precisely than a product label. Microsoft’s adoption guidance explains how tools can move an agent from retrieving information to participating in business processes: AI Agent Adoption Guidance for Organizations.
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Why an agent’s permissions and oversight matter
A text-only answer usually leaves the user to decide what to do. Tool-enabled agents can make direct changes, so their access and autonomy affect the risk. Microsoft’s AI agent shared responsibility model identifies risks including prompt injection that drives actions, excessive agency, confused-deputy problems, memory poisoning, unbounded loops, and failures of trust between agents.
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- Give each tool only the permissions it needs, and authorize actions at the point they are taken.
- Require human approval for high-impact or irreversible actions.
- Log activity and set limits on steps and cost to contain errors or runaway workflows.
- Scope instructions and treat connected systems, stored memory, and other agents as potential sources of untrusted input.
“Agent” describes software behavior and a degree of autonomy; it does not establish human-like understanding or consciousness. To judge a particular system, examine its inputs, tools, permissions, approval checkpoints, and actual outcomes.
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Why the terminology can be confusing
There is no single product-label boundary shared by the cited sources. IBM says terms such as “virtual agent” may be used for chatbots or more advanced service software, while products increasingly combine chatbot, assistant, and agent features. Microsoft’s style guidance uses “bot” broadly for an app that automates tasks or engages through conversation, and suggests “chatbot” when the conversational mode needs to be clear: Microsoft’s AI and bot terms.
For general context on agentic AI, NIST describes its work on trust, evaluation, standards, and risk management in its Agentic AI overview. The practical distinction remains what a system is able and authorized to do, not which term appears in its name.
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