AI agents are software systems that can pursue goals by observing a situation, planning steps, using tools, taking actions, and checking the results with limited supervision.
What makes an AI system an agent
An AI agent does more than generate a one-time answer. It operates in a loop: understand a goal, inspect the current state, choose an action, use an available tool, observe the result, and decide what to do next. The loop may be short and tightly constrained or may span many steps.
The “intelligence” can come from a large language model or another model, but the agent also depends on ordinary software: memory, permissions, APIs, databases, rules, and stopping conditions. An agent is therefore a system, not just a model.
How AI agents plan and use tools
Planning can be explicit, such as breaking a request into a checklist, or implicit in repeated decisions about the next action. Tools extend what the model can do: search, read files, execute code, query databases, schedule events, control a robot, or interact with business software.
Tool access also changes the risk profile. A wrong answer in a chat may be inconvenient; a wrong action taken through an account, payment system, or production service can have real consequences. Reliable agents need permission boundaries, logs, validation and, for high-impact actions, human approval.
AI agents vs. agentic AI and generative AI
Agentic AI describes the broader approach of building AI systems that pursue goals with some autonomy. An AI agent is a concrete system that implements that pattern. Generative AI mainly produces content; an agent can use a generative model but adds state, tools, actions, and feedback.
Multiple agents can also be coordinated, but more agents do not automatically mean better results. Coordination introduces additional failure modes, cost, latency, and security concerns.
Where AI agents are useful—and where caution is needed
Agents can help with research, software maintenance, customer workflows, data operations, and robotics when tasks require several connected steps. They are most dependable when goals are specific, tools are constrained, success can be checked, and irreversible actions require confirmation.
NIST’s 2026 AI Agent Standards Initiative highlights both the growing usefulness of autonomous agents and the need for secure, interoperable ways for agents to act on behalf of users. That makes identity, authorization, provenance, and auditable behavior central engineering concerns.
