AI agents can work through multi-step tasks: they interpret a goal, use tools they have been given access to, check the results, and decide what to do next. A chatbot usually responds to a prompt in conversation. The difference is not whether you can chat with the software; it is whether the model controls a workflow and can take permitted actions.
What are AI agents?
An AI agent is a model-powered system that pursues a task by choosing steps, using available tools, and responding to what happens. It may continue until it completes the task, reaches a limit, or needs a person to make a decision. Anthropic defines an agent as an AI model that directs its own processes and tool use rather than following a fixed script (Anthropic, “Trustworthy agents in practice”).
That does not mean an agent has unlimited independence or reliably gets every step right. Its behavior is bounded by its instructions, tools, permissions, and the software coordinating its work. A model cannot, for example, send an email or update an expense system unless the surrounding system gives it a way to do so.
How are AI agents different from chatbots?
A chatbot is commonly used to answer questions or generate text in a conversation. An agent can also have a chat interface, but it may use the model to control steps in a workflow—such as looking up information, calling a tool, checking the result, and choosing another action. OpenAI draws the distinction this way: “Applications that integrate LLMs but don’t use them to control workflow execution—think simple chatbots, single-turn LLMs, or sentiment classifiers—are not agents.” (OpenAI, “A practical guide to building agents”)
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To judge what a system actually does, look at its workflow and permissions rather than its product label or interface:
- Workflow control: Does it only produce a response, or does it decide and carry out steps toward a goal?
- Tool access: Can it retrieve information from or take actions in connected systems?
- Adaptation: Can it inspect an outcome and choose a different next step, or does it follow a fixed sequence?
- Autonomy and approval: Which actions can it take on its own, and which require confirmation or a handoff to a person?
- Risk and oversight: What could happen if it misunderstands, and are its permissions, safeguards, logging, and human review suited to that risk?
A chat interface alone does not establish that a product is an agent. Nor does connecting a model to a tool automatically make every use autonomous: the system might still require a person to direct each step.
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What can AI agents do?
Agents can be useful when work involves multiple steps, information from connected tools, or exceptions that call for contextual decisions. Examples described by the source organizations include:
- Prepare an expense submission: An agent could transcribe a receipt image, extract the vendor and amount, categorize the expense, and submit it through a company system. If the expense is flagged, it might ask for policy information rather than proceed without it. (Anthropic)
- Resolve a customer-service request: An agent could handle a workflow involving a refund or other resolution, while routing an unusual or high-value case to a human for review. (OpenAI)
- Coordinate workplace processes: Agents can support repeatable work that crosses shared systems and includes handoffs, timing requirements, or structured outputs. (OpenAI Academy)
- Retrieve and process data: An agent can use tools to fetch information, perform an action or transaction, break a task into smaller parts, and assess tool outputs. (Google Cloud glossary)
These are examples of possible workflows, not guarantees that every agent can perform them. The particular system must have suitable tools, access, instructions, and safeguards.
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What parts make up an AI agent?
Implementations differ, but the following pieces explain how an agent can move from a request to an action:
- Model: Interprets the request and context, then generates responses or possible next steps.
- Tools: APIs, functions, services, or interfaces that let the system retrieve information or act elsewhere.
- Instructions and guardrails: Define the agent’s role, constraints, and permitted behavior.
- Orchestration and state: Coordinate tool calls and decisions across steps, and keep track of relevant task context.
- Environment: Determines where the agent runs and which files, sites, or systems it can access.
These are explanatory components, not a required blueprint. OpenAI describes models, tools, and instructions as core elements; Google Cloud also discusses orchestration, memory, and planning, while Anthropic discusses the surrounding execution environment. (OpenAI; Google Cloud; Anthropic)
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When should you use an agent instead of a chatbot?
Use a chatbot when you want a conversation, explanation, draft, or one-off answer and do not need the system to carry out a workflow. Consider an agent when the task is repeatable but involves several steps, connected tools, changing context, or exceptions that are difficult to cover with a fixed script.
For stable, predictable tasks with clearly defined steps, conventional deterministic automation may be a better fit. OpenAI Academy contrasts workflows that follow explicitly defined steps with agents that use a model to interpret context and make bounded decisions (OpenAI Academy). The key question is whether the task benefits from contextual decision-making enough to justify the added complexity and oversight.
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What risks should you consider?
An agent can misunderstand a request, take an unintended action, or be influenced by malicious instructions in material it encounters. Anthropic describes prompt-injection attempts that seek to induce costly actions; OpenAI recommends human intervention for sensitive, irreversible, or high-stakes steps such as canceling orders, authorizing large refunds, or making payments. (Anthropic; OpenAI)
For an agent you use or deploy, safeguards should match what it can affect:
Quick Recap
- Grant only the system access and permissions the task requires.
- Set explicit conditions for stopping, asking for confirmation, or handing the task to a person.
- Test edge cases and ambiguous requests, not just the routine path.
- Require human approval before actions with meaningful financial, operational, or personal consequences.
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