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An artificial intelligence (AI) agent is a software system that receives information about an environment and takes goal-directed actions in it with some degree of autonomy. Unlike a system that only returns an answer, an agent can use what it observes to choose and carry out an action—for example, by calling a function or interacting with another system. This is a practical working definition, not a universal formal standard: the term is used inconsistently, and its boundaries are still evolving.
What makes software an AI agent?
Three elements are useful for recognizing an agent: it receives or processes information from an environment, it has a goal, and it can select and execute actions in pursuit of that goal. NIST’s glossary, citing NIST AI 100-2e2025, defines agents as “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 makes clear that the goal need not originate with the software itself; it may be specified by a person or another system.
The European Commission’s AI Act Service Desk describes a similar pattern: an agent receives and processes environmental input, then executes actions based on that processing that may interact with or affect its environment. The Commission’s explanation of AI agents notes that the term is used inconsistently and that its boundary remains blurry. The OECD likewise compares several definitions rather than setting one universal meaning in its 2026 review, The agentic AI landscape and its conceptual foundations.
How is an AI agent different from a chatbot?
The important difference is what the software can do, not which kind of AI model it uses. A chatbot that only responds to prompts provides information or conversation. An agent can also use information to act in an environment—for example, issue a function call that triggers an operation in another system. Some agents plan and execute multiple interactions, but that is not a necessary condition for every system described as an agent.
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A conversational interface can therefore be part of an agent, but conversation alone does not make a system one. Ask whether it can select and carry out actions toward a goal, rather than only generate a response.
How much autonomy does an agent need?
Autonomy is a matter of degree, not an all-or-nothing threshold. One system might carry out routine steps with limited human supervision; another might propose an action but wait for a person to approve each consequential step. Both can fit the practical definition if they use environmental information to select and execute goal-directed actions.
When describing or comparing a system, specify its oversight: can it act without approval, does it need approval for certain actions, or does a person direct each step? Calling something an agent does not establish that it is fully independent.
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What should you check when evaluating an AI agent?
The following are practical comparison questions drawn from the ways institutional definitions describe agents. They are explanatory axes, not a formal standard or scoring system.
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- Autonomy and oversight: Which actions can the system take on its own, and which require human direction or approval?
- Action scope: Can it only return information, or can it change state by calling tools, submitting requests, or controlling another system?
- Environment: Does it operate in a digital service, a physical setting, or both?
- Goal and adaptability: Can it pursue a set goal as inputs change, and adjust its actions? Some definitions emphasize these abilities, but they are not uniform criteria.
These questions are useful because two products both called “agents” may have very different capabilities and levels of oversight. The label alone does not tell you what actions are possible or how they are controlled.
What an AI agent does not have to be
The term does not universally require a particular architecture or a particular level of sophistication. An AI agent is not necessarily:
- Fully autonomous or free of human supervision.
- Continuously learning from its actions.
- Part of a multi-agent system.
- Powered by a large language model.
- Capable of general-purpose reasoning.
Those may describe particular systems, but they are not shared requirements in the definitions reviewed by NIST, the European Commission, and the OECD. The more useful test is whether the system receives information from an environment and uses it to choose and carry out actions toward a goal.
Does the EU AI Act define AI agents as a separate legal category?
No. The European Commission says “AI agent” is not a separately defined legal category under the EU AI Act. Agents will usually be AI systems, so the Act’s existing rules for AI systems—and, where relevant, general-purpose AI models—may apply. The relevant legal analysis depends on the system’s properties and intended purpose, not simply on whether someone calls it an agent. See the Commission’s AI-agent FAQ and the consolidated EU AI Act text dated 27 July 2026.
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As stated in the Commission FAQ accessed on 7 October 2026, transparency rules apply from 2 August 2026 when an agent is intended to interact with natural persons or generate content. High-risk requirements apply on later dates where an agent qualifies as high-risk. These dates and the applicable obligations can depend on the system and its use; consult current official guidance for a particular deployment.
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