A chatbot mainly generates a response to a prompt. An AI agent uses a model to manage a workflow: it can choose tools, observe what happens, and take further steps toward a goal. That shift—from answering to directing actions—is the useful distinction. What an agent can actually do, and how much risk it creates, depends on its tools, permissions, autonomy, and human checkpoints.
What is an AI agent?
An AI agent is a system in which a model helps control a workflow rather than only producing an answer. It may decide what step to take next, use an available tool, examine the result, and continue, stop, or return control to a person. OpenAI’s practical guide to building agents excludes applications that use a language model but do not let it control workflow execution, such as a simple chatbot, a single-turn model call, or a sentiment classifier.
The label alone does not tell you how independent a system is. Some agents operate within a narrow, supervised workflow; others can take multiple steps before asking for input. A chatbot can also have tools. The important question is whether the model is directing a process and what that process is permitted to affect.
How do AI agents differ from chatbots?
| Dimension | Chatbot focused on replies | Agent workflow |
|---|---|---|
| Primary role | Responds to a user’s prompt | Works toward a goal by managing steps in a workflow |
| What happens after a response | Typically waits for the next prompt | May select a tool, inspect its output, and decide whether to continue |
| Access and action | Depends on the product; a chatbot may also have tools | Depends on configured tools and permissions; may read information or take permitted actions |
| Human involvement | A person usually supplies prompts and uses the answer | A person may set limits, clarify intent, review a plan, or approve consequential steps |
| Risk profile | Often centered on the answer’s accuracy and content | Can also include the effects of actions taken through connected tools |
These are practical patterns, not rigid product categories. A system’s workflow, not its marketing label, determines whether it is merely generating text or exercising operational control.
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What can an agent do?
Capabilities depend on the software’s tools and permissions. NIST’s August 2025 discussion of tool use in agent systems groups capabilities around perception, planning, analysis, resource management, and action. Perception can include searching the web or querying databases; actions can include running code, using computer interfaces, calling software extensions, or interacting with other tools.
- Gather information: search websites, inspect files, or retrieve data from connected systems if those sources are available.
- Plan and analyze: break a goal into steps, compare information, and decide what to try next.
- Make changes: edit files, submit information, or interact with other services if write access is enabled.
- Work across steps or applications: combine permitted tools to complete a larger task, with or without pauses for a person to review.
For example, Anthropic describes a receipt workflow that can extract details from receipt photos, categorize expenses, and submit them through a company system, while asking a person to check an unclear policy detail. That is an illustration of a configured workflow, not a capability that every agent has. The workflow’s model, tools, surrounding software, and environment all shape its behavior, as explained in Anthropic’s guide to trustworthy agents in practice.
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Why do permissions change the risks?
An agent cannot take an action for which it lacks an enabled tool or permission. But once it can access data or change something, an error may have consequences beyond a poor answer. NIST describes access patterns ranging from read-only, through constrained write access, to write access. Risk also depends on what the system can affect, how autonomous it is, and whether an action can be reversed.
- Read-only access can still expose sensitive information or produce misleading summaries, but it does not by itself authorize changes to the connected system.
- Constrained write access can permit a narrow set of changes, limiting the scope of possible mistakes.
- Write access can let a system alter records or trigger actions; the consequences depend on the specific system and the actions allowed.
These modes are useful when assessing a real deployment, but the risk is not determined by permission level alone. A reversible edit to a draft is different from an irreversible action affecting another person or an important account.
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What can go wrong with an AI agent?
Prompt injection from untrusted content
A web page, message, or other external content may contain malicious instructions designed to redirect a model. OpenAI calls this prompt injection: third-party instructions are added to the context the model sees. NIST also identifies indirect prompt injection as an agent-security concern. An agent that can read external content and use tools may encounter instructions that conflict with the user’s intent. OpenAI’s prompt-injection guidance recommends limiting access, using specific instructions, and reviewing important actions before confirmation.
Misunderstood goals or mistaken actions
An agent may infer the wrong preference or move ahead when it should ask a question. Anthropic notes the trade-off: frequent check-ins can slow a task, but acting without clarification can produce an outcome the user did not want. Human checkpoints matter most when an ambiguous choice or consequential action is involved.
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Excessive access and unintended impact
Broad access increases the range of information an agent can expose and the changes it can make if it errs. A system with write permissions can also make a mistake that is harder to reverse than an inaccurate response. The relevant questions are what it can reach, what it can change, and how serious or reversible those changes are.
Reliability and security weaknesses
Connecting model outputs to software can combine familiar software vulnerabilities with risks specific to model-directed workflows. NIST’s summary of responses to its RFI on AI-agent security discusses concerns including data poisoning, specification gaming, and actions driven by misaligned objectives. These failures need not begin with a malicious user: a system can pursue a poorly specified goal in an unintended way.
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How to reduce risk without assuming it can be eliminated
Safeguards should reduce the possible impact of a mistake and make important decisions visible to a person. OpenAI and Anthropic describe limiting permissions and using review or approval points; NIST emphasizes access patterns and the conditions in which systems operate.
- Give the system only the accounts, files, tools, and data needed for the task.
- Prefer read-only or narrowly constrained write access when full write access is unnecessary.
- State the goal and boundaries specifically instead of granting broad discretion.
- Ask the agent to show a plan before execution when the task could have significant consequences.
- Require human confirmation before consequential actions, and make it possible to pause or intervene.
- Have the agent ask for clarification when a user’s intent or an important preference is unclear.
These practices limit exposure and consequences; they do not guarantee that a system will behave correctly. NIST’s May 2026 summary reports that commenters to its RFI widely agreed that agents present novel security threats and that existing cybersecurity practices need adaptation. This is a qualitative description of respondents’ views, not a measured percentage.
How to assess a specific agent
Compare the workflow, not the word “agent” in a product description. NIST identifies functionality, access patterns, risk, reliability, modality, monitoring, and autonomy as useful dimensions; Anthropic also emphasizes the model, tools, surrounding system, and environment.
Quick Recap
- Task: What goal is it designed to pursue, and what can it perceive, decide, and do?
- Access: Which websites, files, accounts, and external systems can it reach?
- Permission level: Is access read-only, constrained-write, or write-enabled?
- Impact and reversibility: What is the worst plausible consequence of a mistake, and can the action be undone?
- Autonomy: How many steps can it take before it asks the user?
- Reliability and monitoring: Can a user or operator see what it did and inspect the results?
- Human checkpoints: Does it show its plan, ask questions when intent is unclear, and seek approval before consequential steps?
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