TL;DR
An AI agent is a goal-directed software system that reasons about a task, uses tools, and takes action to reach an outcome rather than only producing text on request.
The agent runs a reasoning-tools-memory-goal loop. It plans a next step, acts through a tool, observes the result, updates its memory, and checks whether the goal is met before repeating.
A chatbot responds to messages and stops. An agent pursues an objective and can act across multiple steps, a distinction this article covers separately.
A raw large language model is only the reasoning part of an agent. The full agent wraps that model in tools, memory, and the ability to act.
What is an AI agent?
An AI agent is a goal-directed software system that reasons about a task, uses tools to gather information or take actions, and works toward an outcome rather than a single reply. This framing aligns with Google Cloud's overview of AI agents and McKinsey's explainer. The agent receives a goal, decides what to do, and acts through the tools it can reach.
Four words in that definition carry the weight, and each one rules something in or out.
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.
Reasons means the agent breaks a goal into steps and decides what to do next based on what it learns. It plans, evaluates results, and adjusts. 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 effect change. Without tools, a model can only produce text. With them, an agent can search a knowledge base, query a calendar, or write to a system of record.
Takes action means the agent changes state in the world, not just its own output. It sends the email, updates the record, or merges the code. A system that describes what should happen but cannot do it is an assistant, not an agent.
How do AI agents work?
An AI agent works by running a continuous loop, not by executing a fixed script. The loop moves through four stages that repeat until the goal is met. The agent perceives the current state and plans a next step, acts on that plan by calling a tool, observes what the tool returned, and updates its memory before checking whether the goal is satisfied. Each pass through the loop uses the result of the last pass as new input, so the agent adjusts its plan based on what actually happened rather than on what it predicted.
The reasoning stage decides what to do next. A language model reads the goal, the current state, and everything the agent has already tried, then chooses an action. That action often means calling a tool, because the reasoning model on its own cannot query a database, send an email, or run code. Memory carries context between passes so the agent does not repeat work or lose track of a multi-step task, and the goal acts as the exit condition that tells the loop when to stop.
The stages depend on each other in sequence. Reasoning is useless without tools to act on its decisions. Tools are blind without memory to record what they already returned. Memory is aimless without a goal to measure progress against. When the four run as a cycle, the agent can handle a task that no single model output could finish in one shot.
Consider an agent asked to find and book a meeting room for six people next Tuesday afternoon. On the first pass, it reasons that it needs the room list, calls a calendar tool, and observes that three rooms are free. It writes those three rooms to memory, then checks the goal and sees the task is not done because none is confirmed yet. On the second pass, it filters by capacity, calls the room-details tool, and observes that only one of the three seats six people. It records that room, checks the goal again, and moves to book it.
On the third pass, the agent calls the booking tool and observes a conflict because someone reserved the room seconds earlier. Rather than fail, it reads its memory, sees the two rejected rooms, and reasons that neither fits the headcount. It loops back to search for a new option, and the cycle continues until it confirms a room or reports that none exists. The recovery from a failed booking shows why the loop matters. A static script would stop at the conflict, while the agent treats the failure as one more observation and plans around it.
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 read the same question, look up the order in connected systems, check the shipping carrier, and issue a refund if the workflow allows it.
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, deciding what to do next, calling external tools, and continuing until the goal is met or it 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. An AI agent wraps that model inside a larger system that can plan, use tools, remember, and act toward a goal. The LLM handles the reasoning step of the loop. The agent is everything around it.
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 same model access to tools, a way to store results between steps, and permission to run multiple cycles until a goal is met, you turn it into an agent.
Three additions do the work. Tools connect the model to real systems it can call. Memory lets the agent carry context across steps instead of treating each prompt in isolation. Autonomy lets it decide what to do next based on what it observed, then loop again without waiting for a new instruction each time.
A raw LLM answers a question. An agent takes a goal, breaks it into steps, acts on each one, and adjusts as it goes. The reasoning is shared. The ability to act is what separates them.
What are some examples of AI agents?
A support agent has one goal: resolve a customer's issue without a human handoff when the workflow permits it. It reads the incoming ticket, pulls the customer's account and order history through connected tools, and checks a knowledge base for the relevant policy. If the customer wants a refund, the agent verifies eligibility, issues the refund through the payment system, and updates the ticket status. It observes whether the refund succeeded before closing the loop, so a failed transaction can trigger a retry or escalation rather than a false confirmation.
A scheduling agent works toward booking a meeting that fits everyone involved. Given a request like "find 30 minutes with the design team next week," it reads each attendee's calendar, applies constraints such as time zones and working hours, and proposes a slot. Once someone confirms, the agent creates the event, sends the invites, and adds a video link. When a conflict appears later, it can rebook if its permissions and workflow allow it, because the goal is a scheduled meeting, not a single calendar lookup. 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 breaks the question into sub-queries, searches the web or an internal document store, and reads what it finds. It holds partial findings in memory so it can compare sources, spot gaps, and run follow-up searches to fill them. The output is a synthesized answer with citations, produced after the agent has gathered enough evidence to stop.
A coding agent has the goal of making a codebase do something new or fixing what is broken. Given a task like "add pagination to the user list endpoint," it reads the relevant files, plans the change, and edits the code through a file-editing tool. It can then run tests, read the results, and revise its changes when a test fails. That observe-and-correct cycle separates a coding agent from a model that only suggests a snippet, since the agent can verify its work against real feedback before declaring the task done.
Each example runs the same loop in a different context. The agent starts with a goal, reasons about the next step, acts through tools, observes the result, and updates what it knows before deciding whether the goal is met.
How do you build an AI agent in practice?
Building an agent means assembling the reasoning-tools-memory-goal loop on a platform that handles the wiring for you. In Sim, you can configure an Agent block with a model, instructions, and the tools it can use. You can also connect Tables for structured records and Knowledge Bases for documents an agent can search as it runs.
These surfaces correspond to the loop described above. Agent blocks hold the model, instructions, and tools an agent uses to plan and act. Tables can give an agent structured data to read and write across runs, such as a support ticket history or pending scheduling requests. Knowledge Bases store documents an agent can search when a task needs grounded information, which helps a research agent answer from your own sources rather than guessing.
A working agent in Sim can combine these into a single flow. An Agent block decides what to do, calls a tool to act, writes a result to a Table, and checks whether the goal is met before looping again. The Sim agents documentation covers how these blocks connect and what each configuration option controls. For a practical walkthrough, read How to Build AI Agents With Sim.
The bottom line
An AI agent is a goal-directed system that combines reasoning, tools, memory, and action. It does not just return a response; it can pursue an objective across multiple steps, observe the result of each action, and adjust its next move.
Start with one narrow workflow and clear permissions. A support agent that classifies and routes incoming tickets, a scheduling agent that proposes slots, or a research agent that gathers cited information gives you an observable loop to test. As the workflow becomes reliable, you can expand the tools and autonomy around it.
If you are deciding whether a 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?
Most modern agents use a large language model as their reasoning engine because it can interpret instructions, plan, and choose tools. Simpler agents can use rules or classical planning algorithms instead. The defining trait is goal-directed action, not the specific model powering the reasoning.
Does RPA count as an AI agent?
Robotic process automation follows fixed, pre-scripted steps, while an AI agent can decide how to pursue a goal at runtime and adapt to new observations. The distinction is reasoning and adaptation rather than the ability to automate a task.
How autonomous are AI agents really?
Autonomy is a spectrum. Production agents commonly have defined tool permissions and human approval requirements for sensitive actions. A support agent might handle routine tickets independently while escalating larger refunds to a person.
Can an agent use more than one tool?
Yes. A research agent might query a search API, read a knowledge base, and write results to a database in one task. It chooses which tool to invoke at each step based on the goal and the result of its previous action.