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.
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
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.
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.
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 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.
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.
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.
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.
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.
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.
