TL;DR
An AI agent is goal-directed software that uses reasoning and tools to act toward an outcome rather than only produce text on request.
The agent runs a cycle that combines decision-making, available actions, retained context, and an objective. It plans the next step and acts through a tool. After observing the result, it updates what it knows and checks whether to stop or repeat the loop.
A chatbot responds to each message and then waits. An agent pursues an objective and can act across multiple steps.
A raw large language model can provide the reasoning component of an agent. The full agent gives that model access to external capabilities and stored context so it can act.
What is an AI agent?
An AI agent is goal-directed software that uses reasoning and tools to act toward an outcome rather than produce a single reply. Google Cloud's overview of AI agents and McKinsey's explainer use similar definitions. After receiving a goal, the agent decides what to do and acts through its available tools.
Four ideas in that definition carry the weight, and each one sets an important boundary.
Goal-directed means the agent works toward an outcome you specify, not a one-off response. You give it an objective like "resolve this ticket" or "book a meeting," and it keeps working until the goal is met or blocked. A system that answers one prompt and stops is not goal-directed.
Reasoning means the agent breaks a goal into steps and decides what to do next based on what it learns. It evaluates results and adjusts its plan. Fixed if-then automation follows a script and does not revise its plan, so it fails this test.
Uses tools means the agent can call external functions, APIs, databases, or other software to get information and make changes. Without tools, a language model cannot interact with external systems. With tools, an agent can search a knowledge base and update connected systems such as a calendar or system of record.
Takes action means the agent changes state in the world, not just its own output. It can send an email or update a record directly. A system that describes what should happen but cannot do it is an assistant, not an agent.
These systems differ in how independently they pursue multi-step work and whether they can act through tools.
| System | Decision-making | Tool use | Autonomy and multi-step action | Typical use case |
|---|---|---|---|---|
| AI agent | Chooses and adjusts steps based on results | Calls external tools, APIs, databases, or software | Pursues a goal across multiple steps until it succeeds or reaches a limit | Resolving support issues, scheduling meetings, researching questions, or changing code |
| RPA | Follows fixed, pre-scripted rules | Operates the systems included in its script | Repeats a defined sequence and does not revise its plan | Automating stable, rule-based processes |
| Chatbot | Generates a response to each message | May retrieve information needed for a reply | Waits for the next user message instead of pursuing a goal independently | Answering questions in a conversation |
| Copilot | Helps a person decide or create | May use connected tools under the person's direction | Assists with steps while the person remains in control of the task | Supporting writing, analysis, coding, or other user-led work |
How do AI agents work?
An AI agent repeats a four-stage loop until it meets its goal, a pattern IBM's overview of agentic reasoning also describes as perceiving, reasoning, and acting in a cycle. It assesses the current state and plans the next step before calling a tool. After the tool returns a result, the agent records the observation and checks whether the objective is complete. If more work remains, that result informs the next pass instead of leaving the agent bound to its initial plan.
The planning stage decides what to do next. A language model reads the desired outcome and current context, including earlier attempts, before choosing an action. That action often requires a tool because the model cannot query a database or send an email on its own. Retained context carries information between passes so the agent can track a multi-step task without repeating work. The completion condition tells the cycle when to stop.
A room-booking agent uses each tool result to narrow its options. It first calls a calendar tool to find open rooms and stores the three matches in memory. Because no room is confirmed yet, the agent continues by checking their capacity. The room-details tool shows that only one seats six people, so the agent records that room and attempts to book it.
A booking conflict gives the agent new information for its next pass. When the tool reports that someone else reserved the room, the agent checks its memory and confirms that the other rooms are too small. It then searches for another option until it books a suitable room or reports that none is available. A static script might stop at the conflict, but the agent can use the tool result to revise its plan.
How is an AI agent different from a chatbot?
A chatbot responds to what you say. An AI agent pursues a goal you set and takes the actions needed to reach it. A support chatbot answers "Where is my order?" with a status message and stops. A support agent can use connected systems to check the order and shipping carrier before issuing a refund when its configured workflow permits one.
The mechanical difference comes down to autonomy and tools. A chatbot maps input to output inside a single conversation and waits for your next message. An agent runs a loop that selects the next action and calls external tools. It continues until it meets the goal or reaches a limit you defined. That loop lets an agent complete multi-step work without a person prompting each step.
For a full breakdown of where the two overlap and where they diverge, read AI Agent vs Chatbot: Understanding the Differences.
How is an AI agent different from a raw LLM?
A large language model generates a response from a prompt, while an AI agent places that model inside software that can call external systems and retain context while pursuing an outcome. The LLM supplies the decision-making capability, and the surrounding agent software enables action across multiple steps.
Think of the LLM as the engine and the agent as the vehicle. On its own, an LLM cannot check today's calendar, query a database, or send an email. It only has the information in its prompt and training. When you give that model access to connected functions and stored results, it can run repeated cycles until it completes the task.
External capabilities and persistent context let the model interact with real systems while carrying information across steps. With permission to continue, the agent can choose its next action based on the observed result instead of waiting for another instruction.
A large language model can summarize text, answer questions, classify information, generate structured output, and help select a next step. An AI agent places a model—or another decision mechanism—inside a larger system with instructions, state, tools, permissions, control logic, and feedback from previous actions.
The practical distinction is action and control. An LLM produces an output when a person or application calls it. An AI agent can use that output to decide whether to query a database, call an API, ask for approval, update a record, or continue working toward a defined outcome. Some agents also combine language models with deterministic rules, retrieval systems, classifiers, and human approval steps.
Is an AI agent the same thing as a copilot?
An AI agent is not the same thing as a copilot because a copilot primarily assists a person, while an agent can be delegated a bounded outcome and execute multiple steps toward it.
A copilot usually waits for a user to request help, review a suggestion, or approve the next action. The person remains the primary operator. An agent can monitor for a trigger, determine a next step, use authorized tools, inspect the result, and continue without requiring a new instruction at every stage.
The categories can overlap. A copilot may include agentic features, and an agent may pause to ask a person for guidance. The clearest test is who controls the sequence: if the person directs each meaningful step, the system behaves more like a copilot; if the system selects and executes steps within defined limits, it behaves more like an agent.
What is an LLM agent?
An LLM agent is an AI agent that uses a large language model as a decision-making component for interpreting context, selecting actions, or generating outputs.
A typical LLM agent follows a loop:
- Observe the request, event, or current state.
- Interpret the context and determine a next step.
- Select an available tool or generate an output.
- Execute the action within its permissions.
- Inspect the result and decide whether to continue, stop, retry, or request human input.
The LLM does not provide the whole agent by itself. The surrounding system supplies tool definitions, credentials, memory or state, execution rules, error handling, permissions, and stopping conditions. Production agents often combine flexible model decisions with deterministic conditions so that sensitive actions remain predictable and reviewable.
How do AI agents use tools?
AI agents use tools by selecting structured operations—such as API calls, database queries, searches, messages, or application actions—and passing the required inputs to an execution layer.
A tool typically defines what operation is available, which inputs it accepts, and what result it returns. The agent or its control logic selects the tool, constructs the input, receives the result, and uses that result to determine the next step. The execution layer, not the language model itself, handles credentials and performs the external action.
Safe tool use depends on boundaries. An agent should receive only the permissions it needs, validate important inputs, restrict high-impact operations, record execution results, and require human approval when the cost of an incorrect action is high.
Sim is the open-source AI workspace where teams build, deploy, and manage AI agents by connecting model steps, tools, conditions, and human checkpoints in a visual builder. Teams evaluating implementation environments may also encounter n8n as an incumbent in workflow automation; the key architectural question remains whether the system merely runs a predefined sequence or lets an agent choose among bounded actions based on context.
What’s the difference between AI agents and scripts or macros?
AI agents can choose among bounded actions based on context, while scripts and macros normally execute a predefined sequence of instructions.
A script is useful when the inputs, steps, and expected outputs are known in advance. A macro is useful for replaying a repeatable series of interface actions or commands. Both are usually deterministic: the same conditions lead to the same programmed path unless a developer adds explicit branches.
An AI agent is useful when a task contains variable language, incomplete information, changing context, or several valid paths. The agent can interpret the situation and select an allowed next action instead of requiring every possible path to be written in advance.
Agents do not replace scripts or macros in every case. A reliable system often uses an agent to interpret context and choose a path, then uses deterministic code to perform sensitive or well-defined operations.
How do AI agents, LLMs, copilots, scripts, and macros compare?
AI agents are distinguished by delegated, bounded action; LLMs generate or interpret content; copilots assist people; and scripts or macros follow predefined instructions.
| Technology | Primary role | Who selects the next step? | Can it act in external systems? | Best fit |
|---|---|---|---|---|
| AI agent | Pursues a defined outcome within permissions | The agent, deterministic control logic, or both | Yes, through authorized tools | Variable multi-step work that requires contextual decisions |
| Large language model | Generates, transforms, or interprets content | The calling person or application unless an agent layer is added | Not by itself | Language understanding, generation, classification, and structured output |
| Copilot | Assists a person during a task | Primarily the person | Sometimes, usually with user direction or confirmation | Interactive drafting, analysis, recommendations, and guided work |
| Script | Executes programmed instructions | The developer’s predefined logic | Yes, when programmed and authorized | Stable, repeatable processes with known rules |
| Macro | Replays a recorded or configured sequence | The predefined sequence | Yes, within the environment it controls | Repetitive interface actions and simple command sequences |
When should you use an AI agent instead of an LLM, copilot, script, or macro?
An AI agent is the better choice when a task requires contextual decisions and multiple authorized actions, while an LLM, copilot, script, or macro is better when generation, human control, or a fixed sequence is sufficient.
Use an LLM when the desired result is content or analysis and no external action is required. Use a copilot when a person should remain actively in control of each important decision. Use a script or macro when the process is stable enough to define in advance. Use an agent when the system must interpret changing information, choose among permitted paths, act through tools, and evaluate what happened next.
For related distinctions, see AI Agent vs Chatbot: Understanding the Differences, AI Agents vs RPA: When to Use Each for Enterprise Automation, How AI Agents Make Decisions (vs. Rule-Based Systems), and What Is an Agentic Workflow? (And How It’s Different From Automation).
What are some examples of AI agents?
A support agent aims to resolve a customer's issue without a human handoff, as long as each action falls within its configured permissions. It uses connected tools to retrieve the customer's account and order history, then checks a knowledge base for the relevant policy. For a refund request, the agent verifies eligibility and submits the transaction through the payment system. It updates the ticket only after confirming the result, while an unsuccessful transaction can trigger a retry or escalation.
A scheduling agent works toward booking a meeting that fits the participants' calendars and working hours. For a request such as "find 30 minutes with the design team next week," it checks availability before proposing a suitable slot. After someone confirms, the agent creates the event with invitations and a video link. If a later conflict occurs, the agent can rebook within its configured permissions. For a closer look at this use case, see the best AI agents for scheduling and calendar management.
A research agent aims to answer a question that no single source resolves. It divides the question into focused searches and reads relevant material from the web or an internal document store. By retaining partial findings in memory, the agent can compare sources and search again when evidence is missing. Once it has enough support for an answer, the agent writes a cited synthesis.
A coding agent changes a codebase to add a feature or fix a defect. For a task such as "add pagination to the user list endpoint," it inspects the relevant files and edits them with a file tool. The agent then runs tests and uses their results to revise the change when needed. Because it checks its work against the codebase, it can respond to real feedback instead of only suggesting a snippet.
How do you build an AI agent in practice?
You can build an agent by mapping each stage of the loop to a concrete Sim primitive instead of treating the workflow as an abstract collection of parts:
- Reasoning → Agent block. Configure the Agent block's model and instructions so it can interpret the current context and select the next step.
- Tool use → tool calls. Attach the integrations and functions the Agent block may call. The selected tool executes the action, and its result returns to the agent for the next decision.
- Memory → Tables and Knowledge Bases. Use Tables to store and query structured workflow records, such as completed attempts or pending requests. Use Knowledge Bases when the agent needs to search source documents for relevant context.
- Goal → Agent block instructions. State the desired outcome and stopping criteria in the Agent block's instructions so the result of each tool call can be evaluated against a concrete definition of done.
During each pass, the Agent block chooses an action, a connected tool call executes it, and the returned observation informs the next choice. The workflow can read or write structured state through Tables and retrieve source material from Knowledge Bases before the Agent block determines whether the stated outcome has been reached. The Sim agents documentation explains how the blocks connect, and How to Build AI Agents With Sim provides a practical walkthrough.
The bottom line
An AI agent combines a model that can choose the next step with connected capabilities and retained context so it can act toward an objective. It pursues that outcome across multiple steps and uses each action's result to decide what comes next.
Start with one narrow workflow and clear permissions. Choose a task with an observable loop, such as routing support tickets or proposing meeting slots. Once that task works reliably, you can add tools or grant the agent more autonomy.
If you are deciding whether fixed automation is enough, AI Agents vs RPA: When to Use Each for Enterprise Automation explains where rule-based automation remains the better fit.
FAQ
Does an AI agent need an LLM?
An AI agent does not strictly need an LLM; it needs a mechanism that selects actions in pursuit of a goal. In Sim, an Agent block can use an LLM to interpret instructions and choose among connected tools. This gives teams a flexible way to automate tasks whose next step depends on context. An AI agent can work without an LLM when rules, planning algorithms, classifiers, or other decision systems are sufficient to choose and execute actions toward its goal.
Does RPA count as an AI agent?
RPA generally does not count as an AI agent because it follows fixed steps rather than selecting new actions from observed results. In Sim, fixed workflow blocks can handle predictable steps while an Agent block can choose tools and revise its approach at runtime. This distinction helps teams reserve agents for work that requires adaptation instead of adding unnecessary complexity to stable processes.
How autonomous are AI agents really?
Agent autonomy is the degree to which an agent can select and execute steps without human approval. In Sim, teams can limit an agent's connected tools and place approval steps before sensitive actions such as issuing a large refund. These controls let routine work proceed automatically while keeping higher-risk decisions under human oversight. An AI agent should have only the autonomy required for its task, with narrow permissions, explicit limits, observable execution, and human approval for high-impact actions.
Can an agent use more than one tool?
A multi-tool agent can choose among several connected tools as it works toward a goal. In Sim, an Agent block might search a Knowledge Base, call an API, and save its findings in a Table during the same workflow. This lets one agent complete multi-step tasks without handing each stage to a person.
What’s the difference between an AI agent and a large language model?
An AI agent is a system that can select and perform actions toward a goal, while a large language model generates or interprets content and may serve as one component of the agent.
Is an AI agent the same thing as a copilot?
An AI agent is not the same thing as a copilot because a copilot primarily assists a person, while an agent can execute a bounded sequence of actions without a new human instruction at every step.
What is an LLM agent?
An LLM agent is an AI agent that uses a large language model to interpret context, select actions, or generate outputs within a system that supplies tools, permissions, state, and control logic.
How do AI agents use tools?
AI agents use tools by selecting structured operations, supplying the required inputs, receiving execution results, and using those results to choose the next permitted step.
What’s the difference between AI agents and scripts or macros?
AI agents can adapt their next action to context, while scripts and macros normally follow paths that a developer or user defined in advance.
Does an AI agent need tools?
An AI agent does not always need external tools, but an agent needs some ability to affect its environment or internal state if it is expected to do more than generate a response.
Can a copilot have agentic features?
A copilot can have agentic features when it selects or executes multiple steps, but it remains copilot-like when a person directs or approves the important actions.
Are AI agents just automation?
AI agents are a form of automation that can interpret context and choose among bounded actions, whereas traditional automation usually follows explicitly programmed triggers, rules, and paths.
What is the difference between an AI agent and a chatbot?
An AI agent can pursue a goal and act through tools, while a chatbot is primarily a conversational interface and may or may not include agent capabilities.
What is the difference between an AI agent and RPA?
An AI agent selects actions using context and defined constraints, while robotic process automation usually follows configured rules to perform repetitive interface or application tasks.
When should you use a script instead of an AI agent?
A script is preferable to an AI agent when the task has stable inputs, known rules, a predictable sequence, and no need for flexible interpretation.
When should you use an AI agent instead of a copilot?
An AI agent is preferable to a copilot when a bounded task should continue across several steps without requiring a person to direct each step.
Is an LLM enough to build an AI agent?
An LLM is not enough to build a complete AI agent because the agent also needs instructions, tools or actions, permissions, state, execution logic, error handling, and stopping conditions.
