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An LLM is a model that interprets and generates language. An AI agent is a larger application or workflow that uses a model to pursue a task, often by calling tools, checking results, and deciding what to do next. A single-turn model answers a question; an agent may search, inspect what it finds, and continue until it finishes or needs a person to intervene.
What is an LLM, and what is an AI agent?
A large language model (LLM) is the component that processes input and generates output, such as a written answer or a classification. By itself, a model does not necessarily browse the web, update a record, or carry out a multi-step task.
An AI agent is a system built around a model to accomplish a goal. It may combine the model with instructions, tools, workflow logic, and limits on what actions are allowed. Google Cloud describes an agent application as one that processes input, reasons with available tools, and acts on its decisions; OpenAI’s agent documentation similarly describes a configuration built around a model and instructions, with optional runtime behaviors. Those are architectural descriptions, not a promise that every agent has the same capabilities.
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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesThe practical distinction is whether the model helps control the workflow. OpenAI’s A practical guide to building agents says applications that use an LLM without letting it control workflow execution—such as simple chatbots and single-turn LLMs—are not agents.
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How does an agent differ from a model?
| Aspect | LLM | AI agent |
|---|---|---|
| Role | Interprets input and generates language or other model outputs. | Uses a model within a system designed to pursue a task. |
| Actions | Returns an output; external actions require something else to carry them out. | May call tools or interact with connected systems, subject to its permissions. |
| Control flow | Often handles a prompt and response. | May run a multi-step loop, using results to choose a next action. |
| State and context | Uses the context provided for a request. | May add orchestration or memory, but persistent memory is not universal. |
| Boundaries | Behavior is shaped by its instructions and the application using it. | Can also be constrained by tool permissions, guardrails, and human approvals or handoffs. |
| Typical fit | One-off questions, drafting, or other requests where an answer is the desired result. | Repeatable tasks with structured outcomes, connected tools, or event-triggered steps. |
How does an AI agent work?
An agent can repeat a cycle of planning, acting, observing the result, and adjusting its next step. For example, given a request to find a current answer, it might search a permitted source, inspect the results, and either refine the search, report what it found, or ask for human input. Anthropic describes this iterative approach in Trustworthy agents in practice.
- Interpret the goal: The model uses the request and system instructions to determine what outcome is needed.
- Select an action: If the workflow allows it, the agent chooses a tool or step, such as searching or querying an application.
- Observe the result: The system returns the tool’s output to the model or workflow.
- Continue or stop: The agent can take another permitted step, provide a result, or hand control to a person.
This does not make an agent an independent person. Its authority depends on the tools and permissions it is given, the guardrails around those tools, and where the workflow requires human review. Google Cloud’s explanation of agentic workflows describes the LLM as a reasoning engine within a workflow, while orchestration coordinates steps such as planning, tool use, and data flow.
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When should you use an agent instead of an LLM?
Use a model directly for a one-off answer
A standard chat or model call is often simpler when you want an explanation, a draft, brainstorming, or another response that does not need to change an external system. OpenAI Academy notes that ordinary chat may be preferable for open-ended brainstorming and exploratory writing.
Consider an agent for repeatable work with actions
An agent is a better fit when a task has a defined outcome and benefits from a sequence of tool-based steps—for example, retrieving information, checking it against a rule, and preparing a structured result. It may also suit workflows started by a time or event, provided the system has the required access and controls.
Agent terminology alone does not tell you how much automation a product provides. Check which tools it can use, whether actions require approval, how it handles failures, and whether a person can review or stop its work.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Does every AI agent have the same architecture or autonomy?
No. “Agent” describes a broad kind of system, not one universal runtime or fixed level of independence. Some systems use a managed runtime; others run orchestration in an application controlled by the developer. The implementation determines which component manages state, tool calls, handoffs, and workflow execution.
For example, OpenAI’s agent runtime guide distinguishes its managed Agents API, an Agents SDK running within a developer’s application, and direct model responses through the Responses API. These are OpenAI-specific options, not a general industry taxonomy. Its agent definitions describe a model and instructions with optional tools, guardrails, MCP servers, handoffs, and structured outputs.
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When evaluating an agent, focus on its actual configuration and boundaries rather than assuming it has persistent memory or can act without supervision. Google Cloud’s generative AI glossary also uses orchestration to describe managing aspects such as state, planning, tool use, and data flow; a particular product may implement only some of them.
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