A chatbot is defined by how you interact with it: you send a message or speak, and it responds. An AI agent is defined by what it can do toward a goal: it can interpret context, choose actions, use tools and potentially carry out a multi-step task. The categories overlap—a chatbot can be the interface to an agent—so the useful question is what happens after the system answers.
What is a chatbot?
A chatbot is software that communicates with people through text or voice. It may use fixed rules, menus and decision trees, artificial intelligence, or a mixture of these approaches. A basic support chatbot might guide someone through a set order-status options. An AI chatbot may understand a natural-language question, use conversation context and retrieve relevant information.
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Some chatbots can perform bounded tasks within a conversation, so the label does not mean a system can only produce text. The defining feature is that conversation is its main interaction pattern.
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What is an AI agent?
An AI agent is software configured to pursue a goal by interpreting information and taking actions. Depending on its design, it may select steps based on context, call tools or APIs, interact with other systems and continue a workflow with limited human involvement. 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 points readers to the originating document for context.
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Tools can change a system from retrieving information to participating in a process. Microsoft Learn puts it this way: “Tools transform the agent from a passive information retriever into an active participant in business processes.” Microsoft’s AI agent adoption guidance describes agents in terms of their components and actions.
What’s the difference between a chatbot and an AI agent?
The simplest distinction is conversation versus action. A chatbot provides a conversational way to interact; an agent is organized around reaching an outcome. An agent may use a chatbot interface, but a chat window alone does not make a system an agent.
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| Question | Chatbot | AI agent |
|---|---|---|
| What defines it? | Conversational interaction | Goal-directed behavior and capacity to act |
| What happens after a response? | Often waits for the person to decide or take the next step; some chatbots can perform bounded tasks | May choose and carry out further steps toward the goal |
| How does it handle a workflow? | May follow fixed rules or answer within a conversation | May select steps based on context and coordinate actions through tools |
| What should you check? | Its rules, information sources and task limits | Its tools, permissions, memory, oversight and effects on connected systems |
This is not simply a contrast between an old, unintelligent bot and a smart new agent. Chatbots can use AI, while agent-like capabilities can be combined with conversational interfaces. IBM describes how products may combine chatbot, assistant and agent features, and Google Cloud likewise distinguishes among these roles while noting their overlap. IBM’s chatbot overview and Google Cloud’s AI agent overview use terminology that reflects this continuum.
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Yes. “Chatbot” describes an interaction pattern; “agent” describes a system’s role and ability to act toward a goal. One system can have a chatbot interface and agent capabilities. For example, a support system could converse with a customer while an agent behind that interface coordinates an exchange, updates inventory, notifies other business systems and confirms completion. These are possible behaviors, not capabilities every chatbot or agent has.
To classify a particular system, look beyond its name and ask what it is actually permitted and configured to do.
How to tell what a system really does
Use these questions to compare systems or evaluate a product described as an “agent”:
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- Purpose: Is it mainly meant to converse and answer, or to complete a defined outcome?
- Control flow: Does it follow a fixed script, or choose steps and actions according to context?
- Action capability: Can it call tools or APIs, write data or trigger workflows, or does it only return content for a person to act on?
- Oversight: Must a person approve every action, review only important ones, or delegate a bounded task?
- State and memory: Does it use only the current prompt, maintain the current conversation, or retain information across sessions?
- Permissions and effects: Which systems can it reach, what identity does it use, and can it send, change, delete or spend anything?
These answers are more informative than a marketing label. “Virtual agent,” for example, is used inconsistently: IBM notes that it can refer to a chatbot or to more advanced customer-service software. Microsoft’s terminology guidance recommends “bot” for an app that automates tasks or uses a conversational interface, with “chatbot” useful when the conversational mode needs clarifying. Microsoft’s AI and bot terms offers its editorial usage guidance.
Why agent capabilities raise different safety questions
A text-only answer usually leaves the user to decide what to do. A tool-using agent can create direct effects in connected systems. Microsoft Azure’s AI agent shared responsibility guidance identifies risks including prompt injection that drives actions, excessive agency, confused-deputy problems, memory poisoning, unbounded loops and failures of trust between agents.
For a system with action capabilities, the practical safeguards include:
- Give each tool only the permissions it needs.
- Authorize actions at the point they are taken, rather than assuming that a broad goal grants permission for every step.
- Require human approval for high-impact or irreversible actions.
- Log activity and set limits on steps and cost to help detect or contain runaway behavior.
- Scope instructions and consider whether stored memory could be altered or misused.
Calling software an “agent” does not establish that it understands like a person or operates without constraints. Assess its actual inputs, tools, identity, permissions, human checkpoints and outcomes. NIST’s overview of agentic AI places the subject in the broader context of trust, evaluation, standards and risk management.
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