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.
Sources and further reading
What makes AI agentic?
Agentic AI refers to systems designed to pursue a goal through a sequence of actions rather than only produce a one-step response. An AI agent typically interprets the current situation, selects or revises a plan, uses permitted tools or data sources, observes the result and decides what to do next. This loop can include memory, task decomposition and checks that help the system recover when an intermediate step fails.
The word agentic describes behavior, not a guarantee of human-like independence. Real systems operate within software permissions, model limits, tool access and human-defined objectives. The most useful distinction is therefore operational: a conventional assistant mainly answers a prompt, while an agent can carry a multi-step task forward, act through tools and use feedback from earlier steps to choose later ones.
Common parts of an AI agent
- Goal and instructions: define what the agent is trying to achieve and what it must not do.
- Planning: breaks a larger objective into smaller actions or selects the next useful step.
- Tools: may include search, databases, code execution, APIs or business software.
- State or memory: keeps relevant results from earlier steps available to later decisions.
- Evaluation: checks whether an action worked before continuing or retrying.
Agentic AI architecture: model, state, tools and control
Agentic AI is best understood as a system architecture rather than a new kind of model. A model provides language understanding or reasoning, but an agent also needs task state, memory, tools, permissions and a control loop that decides what happens next.
Google Cloud describes the core building blocks of agents as models, grounding, tools, data architecture, orchestration and runtime. Those components explain why two products using the same underlying language model can behave very differently: one may only answer questions, while the other can pursue a goal across connected systems.
Planning and stopping conditions
An agent can break a goal into steps or make one decision at a time based on the latest result. Reliable systems also define stopping conditions. Without them, an agent may repeat work, continue after the goal is already satisfied or spend resources on an unproductive loop.
Memory and state
Conversation history is only one kind of state. An agent may need to remember files it has changed, tools it has already used, intermediate results, user preferences and which approvals remain outstanding. State should be scoped to the task and identity so information does not leak between unrelated users or jobs.
Tools and permissions
Tools let an agent move from language to action. Search, databases, code execution, calendars, business APIs and robots are all possible tools. Access should be narrowly scoped: the fact that an agent can reason about an action does not mean it should automatically be authorized to perform it.
AI agents, chatbots and generative AI are not the same
A chatbot is primarily conversational. Generative AI is primarily concerned with producing or transforming content. Agentic AI adds goal-directed execution and feedback. These categories can overlap because a chatbot may be the interface to an agent, and an agent may use a generative model as its reasoning engine.
For a direct comparison, see AI agents vs chatbots and agentic AI vs generative AI.
Where AI agents are used
Agents can help with coding, research, support workflows, data analysis, scheduling and operations when tasks require multiple dependent steps. Dedicated AI agent examples are useful because the important question is whether the system maintains a goal and acts on feedback, not merely whether a vendor calls it an agent.
Single agents and multi-agent systems
One agent is often enough. When work benefits from genuinely different roles or tool boundaries, several agents can cooperate in a multi-agent system. Multiple agents add coordination and security complexity, so they should solve a real architectural problem rather than being added for novelty.
Security and delegated authority
NIST’s 2026 AI Agent Standards Initiative focuses on secure operation, agent identity and interoperability. Those concerns follow directly from delegation: an agent can act on behalf of a person or organization, so systems must define whose authority it is using and what that authority permits.
Important controls include authentication, authorization, least privilege, audit logs, human approval for high-impact actions and defenses against prompt injection from untrusted content. See AI agent security for a deeper treatment.
