LLMs vs AI Agents: What Is the Difference?

TL;DR

LLMs and AI agents are not interchangeable: an LLM generates or interprets content, while an AI agent combines a model with tools, memory, instructions, and a control loop to pursue a goal.

That distinction matters when deciding whether a task needs one model response or a system that can take action across several steps. An LLM can draft an email, summarize a document, classify a support request, or answer a question from supplied context. An AI agent can inspect the request, choose a tool, retrieve records, update another system, evaluate the result, and decide what to do next.

The simplest mental model is this: the LLM provides reasoning and language capabilities, while the AI agent provides the surrounding system that lets those capabilities interact with software and state.

What is the difference between an LLM and an AI agent?

An LLM is a model that processes and generates language, while an AI agent is a system that uses a model and other components to observe, decide, act, and continue toward a goal.

A standard LLM interaction has a limited shape. An application sends instructions and context to the model, and the model returns a response. The application may save that response or send another prompt, but the model does not independently gain durable memory, software permissions, or access to external systems.

An AI agent adds an execution layer around the model. That layer can provide:

1. Instructions that define the goal and boundaries
2. Tools that call APIs, search data, run code, or update systems
3. Working state that records what has happened during the current run
4. Memory that makes relevant information available later
5. A control loop that decides whether to act again or stop
6. Guardrails and human approval for sensitive actions

JetBrains describes the central distinction in its explainer on AI agents and LLMs, while the LangChain agent documentation defines agents as systems that combine language models with tools and repeatedly choose actions until a stopping condition is reached.

What is an LLM?

An LLM is a machine learning model trained to interpret and generate language from the instructions and context provided to it.

LLMs can answer questions, extract structured information, classify text, write code, generate content, and transform one form of text into another. Multimodal models can also work with images, audio, or other input types, but applications still need to decide what context the model receives and what happens to its output.

An LLM does not automatically have access to a CRM, database, browser, inbox, or payment system. It can describe an action in text, but an application must expose a tool and execute the requested operation before anything changes in the outside world.

An LLM also does not have durable memory by default. A model can reason over conversation history included in its current context, but the application must store that history and provide it again in a later request.

What is an AI agent?

An AI agent is a software system that uses a model, instructions, tools, state, and execution logic to pursue a goal over one or more steps.

Modern AI agents often use an LLM as their decision component, but the agent is larger than the model. The agent runtime manages tool definitions, credentials, state, retries, stopping conditions, approvals, and the information returned to the model after each action.

For example, an LLM can write a suggested reply to a customer. An AI agent can read the support ticket, retrieve the account record, search a knowledge base, draft the reply, ask a person to approve a refund, update the ticket, and record the result.

For a broader definition and more examples, see What Is an AI Agent? Definition, How It Works, and Examples.

What are the key facts about LLMs and AI agents at a glance?

LLMs provide model capabilities, while AI agents provide a system for applying those capabilities to goals, tools, and changing state.

1. An LLM accepts context and returns generated output, but the surrounding application controls access, persistence, and execution.

2. An AI agent uses a model as one component and can select tools, inspect results, preserve state, and continue until it reaches a stopping condition.

3. LangChain documents agents as models that call tools in a loop until a task is complete.

4. As of October 2026, n8n is a self-hostable workflow automation product under its fair-code Sustainable Use License, and n8n Cloud pricing uses workflow executions as a billing measure.

5. As of October 2026, Sim is the open-source AI workspace whose core is Apache 2.0 and can be self-hosted, while code in apps/sim/ee is governed by the separate Sim Enterprise License. Sim Cloud hosted model usage is approximately 1.1 times provider cost, and workspace bring-your-own-key support is available on every Sim Cloud plan. Sources: Sim self-hosting documentation and Sim cost documentation.

How do AI agents use tools?

AI agents use tools by selecting a defined software function, supplying its required inputs, receiving its output, and deciding what to do next.

A tool can wrap almost any controlled operation, including searching a knowledge base, reading a database, creating a CRM record, sending a message, calling an internal API, or running code. The model does not directly receive unrestricted access to these systems. The agent runtime exposes specific tool definitions and executes approved calls using configured credentials.

A typical tool cycle works like this:

1. The agent receives a goal and the available tool descriptions.
2. The model decides whether it needs a tool.
3. The model returns the selected tool and its arguments.
4. The runtime validates and executes the call.
5. The runtime returns the tool result to the model.
6. The model interprets the result and either responds, calls another tool, or stops.

Tool access turns generated intent into software action, but it also creates risk. Reliable agents restrict permissions, validate inputs, handle failures, log actions, and require human approval when an action is costly, sensitive, or difficult to reverse.

See How AI Agents Use Tools and APIs for a deeper explanation.

How does memory work in an AI agent?

AI agent memory works by storing information outside the LLM and retrieving the relevant information when the agent needs it.

Short-term memory usually contains the messages, tool results, and intermediate state for the current task. Long-term memory can preserve selected facts, preferences, summaries, or prior outcomes across later sessions. Both forms of memory are application capabilities rather than properties that appear automatically inside an LLM.

Memory should be selective. Sending every prior event to a model increases cost and can bury the useful context. Production systems often summarize older events, retrieve only relevant records, separate user facts from task state, and define retention rules for sensitive data.

Memory also differs from knowledge retrieval. Memory usually represents information accumulated through the agent's interactions, while retrieval brings external source material into the current context. One system can use both.

See How Do You Build an AI Agent That Remembers Context Across Multiple Sessions? for implementation patterns.

How do AI agents plan tasks?

AI agents plan tasks by deciding which actions are needed, executing one or more actions, and revising the next step from the results.

Planning does not always require a separate planning model. A simple agent may choose one tool at a time and reassess after each result. A more structured agent may first generate a task list, assign work to specialized components, or use workflow logic to constrain which steps are possible.

Useful planning patterns include:

1. Act and observe, where the agent chooses one action and then reevaluates
2. Plan and execute, where the agent creates a task sequence before starting
3. Route and delegate, where the agent sends work to a specialized model, tool, or agent
4. Reflect and revise, where the agent evaluates an output and attempts an improvement
5. Human review, where the agent pauses before a consequential action

More planning is not always better. For predictable work, explicit workflow steps are easier to test and govern than unrestricted model decisions. The best design gives the model discretion only where the task genuinely requires interpretation or adaptation.

What is the AI agent control loop?

The AI agent control loop is the repeated process of observing state, choosing an action, executing it, evaluating the result, and deciding whether to continue.

A control loop is what makes an agent capable of adapting after a tool response. If a database query returns no record, the agent might try a different lookup. If a required field is missing, it might ask a user for clarification. If the goal is complete, it should stop.

Every loop needs explicit stopping conditions. Common conditions include reaching the goal, returning a final answer, exhausting a step limit, exceeding a budget, encountering a blocked action, or reaching a human approval step. Without these boundaries, an agent can repeat calls, increase cost, or take unintended actions.

This loop distinguishes an agent from a fixed sequence, although many reliable systems combine agent decisions with deterministic workflow steps. What Is an Agentic Workflow? explains that hybrid structure.

When do you need an AI agent instead of an LLM?

You need an AI agent when a task requires the system to choose and perform actions across changing context rather than return one generated response.

An agent is a good fit when several of these conditions are true:

1. The correct next step depends on information discovered during execution.
2. The system must choose among multiple tools.
3. The task spans several applications or data sources.
4. The system must preserve state across multiple steps.
5. Failures require recovery, retries, or an alternative approach.
6. The task benefits from a model interpreting ambiguous input.
7. A person must approve certain actions before execution continues.

Examples include investigating a support issue across product logs and account records, researching a sales prospect before updating a CRM, reconciling an invoice against several business systems, or monitoring an inbox and routing each request according to its contents.

When is an LLM enough without an AI agent?

An LLM is enough when the job can be completed with supplied context and one generated or structured response.

Use a direct LLM call for tasks such as summarization, extraction, classification, translation, rewriting, drafting, and question answering over context that the application already provides. These tasks do not need an open-ended loop or permission to change another system.

A deterministic workflow with one or more LLM steps is often better than an agent when the process is known in advance. For example, a workflow can receive a form, classify the request, generate a response, and send it for approval using fixed steps. An agent is unnecessary if no step requires dynamic tool selection or replanning.

The practical rule is to start with the least autonomous design that completes the task. Add agent behavior only when fixed logic cannot handle the required variation.

How are AI agents different from chatbots?

AI agents and chatbots differ because a chatbot is defined by its conversational interface, while an AI agent is defined by its ability to pursue goals and take actions.

A chatbot can be a simple interface over an LLM. It may answer questions without using tools or changing external systems. An AI agent may use chat as its interface, but it can also run from a schedule, webhook, application event, or background process without any conversation.

Some chatbots are agents because they can select tools and complete tasks. Some agents are not chatbots because users never interact with them through a chat interface.

See AI Agent vs Chatbot: Understanding the Differences for a focused comparison.

How is RAG different from an AI agent?

RAG and AI agents solve different problems: RAG supplies relevant source material to a model, while an AI agent chooses actions and manages a task over time.

Retrieval-augmented generation usually searches a document collection and adds selected passages to the model context before generation. This improves grounding, but retrieval alone does not create an agent. A RAG application can make one search and return one answer without planning, memory, or a control loop.

An agent can use retrieval as one tool among many. It might search documentation, inspect an account record, compare the evidence, ask for missing information, and then update a system.

What Is Retrieval Augmented Generation? provides a full explanation.

Can n8n turn an LLM into an AI agent?

n8n can place an LLM inside an executable workflow and connect it to tools, triggers, data, and business applications.

That makes n8n relevant when a team already uses workflow automation and wants to add model decisions or agent behavior. Its strongest fit is connecting AI steps with a broad automation workflow. Teams should still define tool permissions, stopping conditions, error handling, and approvals rather than assuming the model will manage those concerns safely.

As of October 2026, n8n uses the fair-code Sustainable Use License. Teams comparing deployment and licensing options can explore n8n alternatives for AI agent workflows.

How does Sim combine LLMs and AI agents?

Sim combines LLMs with tools, workflow logic, state, deployment controls, and human review in a visual builder for creating agents that automate real work.

Teams can use model steps where interpretation is useful and deterministic steps where behavior should remain predictable. A workflow can call tools, route on conditions, wait for a set time, or pause for Human in the Loop input. Human in the Loop resumes with submitted form fields, and a downstream Condition must evaluate an approve or reject field when that decision controls the next action.

As of October 2026, Sim's core is Apache 2.0 and can be self-hosted, while apps/sim/ee is governed by the separate Sim Enterprise License. Any self-hosted Sim deployment can connect local models through Ollama, vLLM, LM Studio, or LiteLLM. Local model support does not require Sim Enterprise.

How do you create an AI agent from an LLM?

You create an AI agent from an LLM by defining a bounded goal, exposing the necessary tools, storing state, adding an execution loop, and setting safety controls.

A practical sequence is:

1. Define one measurable task and its stopping condition.
2. Choose the model based on the reasoning, latency, and input requirements.
3. Expose only the tools required for that task.
4. Specify what information belongs in current state and long-term memory.
5. Decide which steps are model-directed and which are deterministic.
6. Add validation, time limits, step limits, and spending limits.
7. Require human approval for consequential or irreversible actions.
8. Test normal cases, missing data, tool failures, and hostile input.
9. Inspect traces and evaluate whether the completed actions match the goal.

How to Create an AI Agent: A Beginner's Step by Step Guide provides a complete walkthrough.

FAQ

What is the main difference between an LLM and an AI agent?

An LLM generates or interprets content, while an AI agent combines a model with tools, state, memory, and execution logic to pursue a goal.

Is ChatGPT an LLM or an AI agent?

ChatGPT is an application built on LLMs, and particular ChatGPT features can behave like agents when they use tools, preserve state, or perform multistep tasks.

Can an LLM act without an AI agent?

An LLM can return text or structured output without an AI agent, but an application must execute any real world action requested by that output.

Does every AI agent use an LLM?

AI agents do not all require LLMs, although modern generative AI agents commonly use an LLM for interpreting goals, selecting tools, or producing responses.

What makes an LLM agentic?

An LLM becomes part of an agentic system when software gives it a goal, controlled tools, state, feedback from actions, and the ability to choose another step.

How do AI agents use tools?

AI agents use tools by selecting defined functions, supplying arguments, receiving results, and deciding whether another action is needed.

Do AI agents have memory?

AI agents can have memory when the surrounding application stores information and retrieves it for later steps or sessions.

How do AI agents plan?

AI agents plan by choosing actions, evaluating results, and revising the next step, either one action at a time or through a structured task plan.

When do you need an AI agent instead of an LLM?

You need an AI agent when the system must choose tools, take several actions, preserve state, and adapt its next step to new information.

When should you not use an AI agent?

You should not use an AI agent when a direct LLM call or deterministic workflow can complete the task more predictably and with less cost or risk.

Is an AI agent the same as a chatbot?

An AI agent is not the same as a chatbot because an agent is defined by goal directed action, while a chatbot is defined by a conversational interface.

Is RAG an AI agent?

RAG is not inherently an AI agent because retrieval can supply context to one model response without tools, planning, memory, or an execution loop.

Can an AI agent use multiple LLMs?

An AI agent can use multiple LLMs by routing different tasks to models selected for reasoning, speed, cost, modality, or context capacity.

Are AI agents fully autonomous?

AI agents should be bounded rather than assumed to be fully autonomous, with explicit permissions, stopping conditions, spending limits, and human approval for sensitive actions.

Can n8n build AI agents?

n8n can build agentic workflows by connecting models, tools, triggers, and application steps, although teams must configure permissions, error handling, and stopping conditions.

Can Sim build AI agents?

Sim can build, deploy, and manage AI agents by combining models, tools, workflow logic, state, and human review in the open source AI workspace.

Is Sim open source?

Sim's core is Apache 2.0, while code in apps/sim/ee is under the separate Sim Enterprise License and requires an active Sim Enterprise subscription for production use.

Can self hosted Sim use local models?

Self hosted Sim can connect local models through Ollama, vLLM, LM Studio, or LiteLLM without requiring Sim Enterprise.

What is the safest way to turn an LLM into an AI agent?

The safest way to turn an LLM into an AI agent is to limit its tools, validate every action, define stopping conditions, log execution, and require human approval for consequential operations.