What is an AI Agent?

When you think of AI agents, do you imagine a personal AI assistant like Tony Stark’s Jarvis? Perhaps a calm-under-pressure TARS from Interstellar? Or, more on the scary spectrum, an amoral HAL 9000 straight out of 2001: A Space Odyssey?

Current technology doesn’t come close to that kind of science fiction (yet). But the field is evolving fast. What AI agents are capable of today looks nothing like it did even just a few months ago. 

Here’s how AI agents actually work, what they can do right now, and how to start using them.

Table of contents:

What is an AI agent?

An AI agent is software that can act autonomously to pursue a goal. It takes in information from its environment, decides what to do with it, and acts. More advanced AI agents can learn and adjust their behavior over time, iterating toward a goal. 

Some agents operate in the physical world—like robots, drones, and self-driving cars. Others are purely software-based, running inside computers to complete tasks. The components and interface of any given AI agent vary widely depending on what it’s built to do.

Building an AI agent used to require serious technical chops. That’s changed. With Zapier, you can describe your goal, and the built-in AI assistant will brainstorm and configure the agent for you, with secure access to apps across your entire tech stack. Or you can install Zapier MCP in your existing agent harness, like ChatGPT or Claude, so you never even have to leave the chat window. 

How do AI agents work?

For something so complex, the steps behind how AI agents work are fairly straightforward: they receive a goal, make a plan, take action, evaluate the results, and iterate until the job is done. Here’s what that looks like step by step.

• Goal initialization. You give the agent an objective, and it passes your input to the underlying AI model, which interprets what needs to be done and begins building a plan.

• Task planning. The agent breaks the goal into a sequence of tasks, determines the order to complete them, and identifies what tools or information it needs to move forward.

• Information gathering. The agent uses available tools—browsing the web, calling an external API, or delegating to another agent or AI model for specialized tasks—to collect what it needs. 

• Tool selection and action. Based on what it detects in the request, the agent picks the right approach: a file search if it needs to look something up, a function call if it needs to interact with an external system, or its base training if neither applies. It then executes the action and stores the results.

• Feedback and evaluation. The agent assesses how close it is to the goal, drawing on both external outputs and its own internal reasoning. If it’s not there yet, it revises its approach.

• Iteration. The agent keeps looping until the goal is met.

What are the key components of an AI agent?

AI agents are made up of different components—each handling a specific job—to help them achieve their goals. These include: 

• Sensors let the agent gather information—for example, images, sounds, and radio frequencies—from its environment. The sensors for physical agents include cameras, microphones, or antennae, among other things. For software agents, it might be a web search function or a tool that reads PDF files.

• Actuators are how the agent does something with what it knows. For a physical agent, that might be wheels or a robotic arm. For a software agent, it’s anything that creates an output or triggers a change—like creating a file or updating a record.

• Processors, control systems, and decision-making mechanisms make up the agent’s “brain.” They process sensor inputs, determine the best course of action, and issue commands to the actuators. Not all three are present in every agent, but some combination usually is.

• Learning and knowledge base systems store data that helps the AI agent complete tasks. For example, a database of facts, difficulties encountered, and solutions found. 

Since the form of an AI agent depends so much on the tasks it carries out, some AI agents may have all these components, while others don’t. For example, a smart thermostat may have basic sensors, a simple actuator, and a control system, but no learning component. A self-driving car, on the other hand, has everything on this list: sensors to read the road, actuators to steer and brake, decision-making to navigate traffic, and a learning system that gets better with every trip.

Types of AI agents

There are several types of AI agents, each with a different level of complexity and capability. Here’s a high-level overview of the current AI agent landscape. These aren’t mutually exclusive categories, though—most AI agents will span a few different types. (For a deeper breakdown, check out Zapier’s comprehensive guide on AI agent types.)

• Simple reflex agents respond to a specific input with a fixed, predefined action. They don’t store memory or reason about past interactions, so they’re fast and predictable. A spam filter with hard-coded rules or a basic thermostat are classic examples of simple reflex agents.

• Model-based reflex agents keep an internal model of the world, which helps them make better decisions when they can’t see the full picture. Rather than reacting to a single input with a single rule, they factor in context and recent history before acting. An email monitor that reassigns contacts to a re-engagement list after three unopened emails is a good example. 

• Goal-based agents work backward from a defined objective, using planning and reasoning to map out a sequence of actions to get there. Think of it like a GPS: give it a destination, and it figures out the route, navigating obstacles along the way. In practice, they handle things like scheduling meetings across time zones or researching leads that meet predefined criteria.

• Utility-based agents do everything a goal-based agent does, but go further by weighing trade-offs to find the best possible action, not just any action that reaches the goal. If a workflow involves competing priorities like cost, speed, and quality, a utility-based agent scores each option and picks the winner. Recommendation engines and traffic optimization systems fall into this category.

• Learning agents adapt their behavior based on experience, using past data, feedback, and machine learning to improve over time. They’re the most autonomous of the bunch, because they can operate in unfamiliar environments and get better without much manual input beyond the initial setup. A customer support chatbot that adjusts its tone based on user feedback is one example, and a spam filter that gets smarter with every flagged email is another.

• LLM agents are built on top of a large language model (LLM) and can reason through a problem, make a plan, use external tools, and execute multi-step tasks with limited human input. What separates them from a standard chatbot is that they don’t just answer questions—they act. Give an LLM agent an objective like “research our competitors and draft a positioning summary,” and it’ll break that down into steps, decide which tools to use, execute them in sequence, and self-correct if something goes wrong. Most of the AI agents people are building and using today are LLM agents.

• Computer use agents take LLM agents a step further, adding the ability to operate a computer directly by clicking, typing, and browsing the way a human would. Instead of calling an API, they interact with software through the interface itself, which makes them useful for tasks that don’t have an API option. OpenAI’s Operator and Anthropic’s computer use features are examples of this in practice. 

• Multi-agent systems combine multiple specialized agents under an orchestrator that delegates tasks, collects outputs, and keeps iterating until the goal is met. They’re designed for complex, multi-step workflows that are too broad for a single agent to handle, and they’re a key part of how AI agent orchestration works in practice.

Build AI agents with Zapier

The AI agents you can build today are nowhere near Jarvis or HAL 9000 level (probably the best for humankind, frankly). But they’re more capable than you might expect, and a lot more accessible than they used to be. 

With Zapier, you can describe the goal you’re trying to accomplish, and Zapier Copilot will brainstorm and configure the agent for you, connecting it to the apps you already use across your entire system. If you prefer working from an AI assistant like Claude or ChatGPT, you can install Zapier MCP and safely take action across your apps without leaving the chat window. 

However you build, every connection runs through a single governed layer, so you control exactly which apps your agent can touch, set permissions at a granular level, and audit activity across your stack from one place.

What are AI agents? FAQ

The possibilities here are astonishing, but what happens if and when AI agents start spreading into every part of our lives? Can we trust this technology to assume more critical tasks in the future?

There are no clear answers yet, as all of this is still very new. Here’s a roundup of the core questions floating online on this issue.

Is ChatGPT an AI agent?

Not on its own. But with the right setup, it can become one.

Out of the box, ChatGPT is an agent harness: it has the reasoning engine and the tools to be an agent, but it needs connections to external systems to act on the world. For example, when you install Zapier MCP into ChatGPT and connect it securely across your tech stack, you can prompt it to take agentic actions like identify emails in your inbox that need a follow-up, drafting a context-aware reply for each one based on the conversation history, and sending them—directly from your chat.

Do AI agents perpetuate bias and discrimination?

An AI model is only as impartial as the data it’s been trained on—so yes, they’re biased. Tackling these problems involves making changes to machine learning processes and creating datasets that represent the full spectrum of the human world and experience.

Who’s to blame when an AI agent makes a mistake?

A thorny issue in ethics and law, it’s still unclear who should be blamed for accidents and unintended consequences. The developers? The owners of the hardware/software? The human operator? As new legislation is created and industry guardrails are implemented, we’ll be able to understand what kinds of roles AI agents can—and can’t—play.

Are reasoning models AI agents?

No. AI reasoning models are very good at thinking through hard problems using chain-of-thought reasoning, but they can’t take action in external systems on their own. They respond when prompted, just like a standard chatbot.

That said, you can install Zapier MCP in the AI chat environments that run these models—like Claude or ChatGPT—to give the underlying reasoning model a way to act on its conclusions.

What’s the difference between an LLM and an AI agent?

An LLM is an AI model trained on massive amounts of text data to understand and generate human language. It’s a powerful reasoning engine, but on its own, it just responds to prompts.

An AI agent, often powered by an LLM, is a fuller system: it combines that reasoning capability with sensors, actuators, knowledge bases, and control systems to perceive its environment and take action in it. 

How do AI agents integrate with existing systems and workflows?

There are two common technical approaches: connecting the agent to a retrieval-augmented generation (RAG) platform to give it access to your knowledge bases, or using API function calls to let it interact directly with external services.

In practice, Zapier handles both without building connections from scratch. It gives your agent governed access to 9,000+ apps out of the box, and offers different entry points depending on how you work: Zapier MCP for connecting directly from a chat environment like Claude or ChatGPT, the Zapier SDK for building agents in code-based tools like Cursor or Claude Code, or the CLI for working directly from your terminal.

What are the risks of using AI agents?

AI agents introduce a new range of challenges and risks, including: 

• Data privacy: AI agents can process enormous amounts of data, and depending on how it’s stored and shared, that creates real exposure for leaks and unauthorized access to private information.

• Bias: Because AI agents are built on LLMs trained on imperfect data, they can produce unfair or skewed outputs. The higher the stakes in the workflow, the bigger the potential impact on the people it affects.

• Unintended consequences: A reasoning failure, bug, or misconfiguration can have unpredictable downstream effects, especially when agents are operating autonomously across multiple systems.

• Security vulnerabilities: AI agents are susceptible to prompt injection attacks, where malicious inputs manipulate the agent into exposing data, taking unauthorized actions, or behaving in ways its designers never intended.

• Loss of human oversight: As agents take on more complex, multi-step work, the decision-making process can become opaque—creating accountability gaps when something goes wrong.

• Ethical dilemmas: When agents are deployed in workflows with moral or societal implications, determining who’s responsible for their decisions becomes a lot tougher.

Learn more: AI security risks (and how to manage them) 

How does human-in-the-loop fit within AI agent workflows?

Human-in-the-loop (HITL) frameworks add oversight to AI agent workflows by building in pause points. At pre-determined moments, the agent stops and sends a notification to a human reviewer, who can approve, adjust, or redirect before the agent continues.

It’s one of the most practical ways to manage the risks that come with autonomous systems, especially for high-stakes decisions where a wrong move has real consequences. Zapier has native human-in-the-loop functionality built into its AI workflows, so you can decide exactly where a human needs to stay in the loop and where the agent can run on its own.

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This article was originally published in June 2023 by Miguel Rebelo. The most recent update was in July 2026.