An AI agent is software that pursues a goal across multiple steps. It reads context, reasons about what to do next, calls tools to act, observes the result, and repeats until the goal is met or a limit trips. That loop separates an agent from a model that only answers questions. On Major, the agent reasons once and builds an app for the repeatable part, so the work becomes stateful and governable.

What does an AI agent do exactly?

An AI agent reads context from prompts or connected systems, decides on a next step, calls a tool or API to carry it out, checks what came back, and loops. A chatbot only replies. An agent changes records in real systems, so it needs scoped credentials, error handling, and durable state. On Major that state lives in a managed database inside the app the agent builds.

What is the difference between an AI agent and a chatbot?

A chatbot responds to messages with text and stops. An AI agent takes actions across multiple steps, calling tools and data sources to reach a goal. The dividing line is action. Because agents write to systems of record, they need permissions, audit logs, and a way to reconstruct what they did, which a chatbot never required.

What are the main types of AI agents?

The classic taxonomy lists five: simple reflex, model-based reflex, goal-based, utility-based, and learning agents. They differ in how they decide, from fixed rules on the current input to weighing trade-offs and improving from feedback. Most LLM-based agents today are goal-based or utility-based, and single versus multi-agent is an architecture choice.

How do you make an AI agent safe to run in production?

Scope its tools, run the actions through deterministic code, and log everything. Let the model decide what to do, then have a permission-checked app with typed inputs execute the write under the identity of the person who started the run. That makes every action attributable and replayable, and it keeps repeatable steps out of the model.

What Is an AI Agent? Definition, Types, and How to Build One

A precise definition of an AI agent, the five classic types, how the loop works, and what separates a production agent from a demo: reasoning you can audit and actions that run as code.

Jose GironJune 5, 2026Updated October 21, 2026

Person drawing a process flow on a whiteboard to map the steps an AI agent will run

An AI agent is a software system that uses a large language model to pursue a goal with some autonomy. It reads context, reasons about what to do, uses tools to act, observes the result, and loops until the goal is met or a limit trips. A chatbot only responds. An agent acts on your behalf across multiple steps, which is why anyone evaluating an agent platform needs to ask what happens after the model decides.

Major is the enterprise platform where agents build the software they run on. The agent reasons once about a repeatable step, builds an app for it, and from then on runs the app. The model keeps the judgment calls. Everything else runs as code with a database, permissions, and an audit trail.

What makes something an agent

Three parts of the definition carry the weight. There is a goal, which is more than a single prompt. There is autonomy, meaning the system picks its own next step instead of following a fixed script. And there are tools, the way the agent reaches outside its own text to read data and change things.

Remove the tools and you have a model that talks. Remove the autonomy and you have a workflow. IBM, Google Cloud and AWS all land close to this: software that perceives, decides, and acts toward a goal.

Anthropic's Building Effective Agents draws the line cleanly. Workflows are systems where LLMs and tools are orchestrated through predefined code paths. Agents are systems where LLMs dynamically direct their own processes and tool usage. That dynamic control is the capability, and it is also the governance problem, because the path is no longer fixed in advance.

How an AI agent works

Underneath the marketing, an agent runs a four-stage loop.

Perceive: read context from a prompt, a connected source, or the previous step.
Reason: use the model to pick the next action given the goal.
Act: call a tool, query a source, or write to an external system.
Observe: read what came back and go around again.

Run the loop once and you have a tool call. Run it repeatedly, choosing each next step, and you have an agent.

The five types of AI agents

The classic taxonomy sorts agents by how they decide.

Simple reflex agents act on the current input with fixed rules and no memory.
Model-based reflex agents keep an internal model of the world.
Goal-based agents choose actions that move them toward a defined goal.
Utility-based agents weigh trade-offs and pick the action with the most value.
Learning agents improve from feedback over time.

Most LLM-based agents sold today are goal-based or utility-based. Single-agent versus multi-agent is an architecture decision, not a different definition.

Agent vs chatbot vs assistant vs RPA

These get used interchangeably and should not be.

AI agent: reasons toward a goal and acts through tools. Reads a dispute, checks history, issues a refund.
Chatbot: responds with text and takes no action. Answers an FAQ in a support widget.
AI assistant: helps a human who approves each action. Drafts an email you then send.
RPA bot: repeats a fixed script with no reasoning. Copies invoice fields between two systems and breaks when the screen changes.

The dividing lines are reasoning and action. An agent has both, which is what makes it useful and what makes it something you have to govern.

Why agents matter now, and where they break

A model that drafts an email is useful. A system that reads the thread, checks the customer record, decides a refund is warranted, and issues it is a different kind of thing, because you now have to account for what it did.

When AI only produces text, the worst case is a bad answer a person can ignore. When it writes to your systems of record, the worst case is an action you cannot undo and cannot reconstruct. MIT Sloan makes a similar point in its explainer on agentic AI: the capability is real and so is the accountability problem.

The usual failure is fusion. In a demo, the agent reads, decides, and acts inside one reasoning loop. When the action is irreversible and someone asks what happened, you cannot say what the agent saw, what options it had, or why it chose one. It also re-reasons the same routine steps on every run, so cost and variance grow with usage.

Build the agent on Major

Major separates the two layers. The agent layer reasons and decides. The app layer executes and holds state. Here is how a refund-handling agent looks when built that way.

Connectors: Stripe for the charge and refund, Postgres for customer history, Slack for the escalation channel. Credentials are scoped per connector and never sit in the prompt.
The app that holds state: a Refunds app with a managed database table of disputes, decisions, amounts and outcomes, plus a review view for the support lead. This is the control surface for people.
The agent and its judgment: the agent reads the dispute and policy and decides whether a refund is warranted and how large. It builds the app the first time it works out the routine path, then runs the app on later disputes.
The trigger: a workflow fires on a new dispute, or on a schedule that sweeps open ones.
The write: the refund executes inside the app with typed inputs (customer ID, amount, reason), an RBAC check, and a logged query under the identity of the person who started the run.
Escalation: amounts above a threshold route to a person in Slack for approval before the app runs the write.
Governance: SSO, role-based access and audit logs come with the platform, so every action is attributable and exportable.

The agent's reasoning appears in run history and the action appears as an app event. If anyone asks what happened in March, both halves are reconstructable. You can start building free and describe the Refunds app in a sentence.

Common misconceptions

Is ChatGPT an AI agent? On its own, no. Base chat answers prompts and stops. It becomes agentic when it has tools and an act-observe loop. The model is the engine. The agent is the model plus tools plus the loop.

Is an agent just a clever system prompt? No. A prompt shapes a response. It cannot read your CRM or write to an API. Tools and the loop make an agent.

Is more autonomy always better? No. Unbounded tool access is how agents take actions nobody intended. The question to ask is how tightly scoped the tools are and how completely you can audit what they touched.

What we believe at Major

Reasoning belongs in the agent. Repeatable execution belongs in apps. When the two are fused, agents feel ungovernable and expensive. When they are separated, the work runs the same way every time, holds its own state, and costs less per run as the library of apps grows. Reason once. Run forever.

This piece does not cover how to evaluate agent quality, which is a separate and harder problem. The model still does the judgment work. It does less of the routine work.

Start building on Major

Build a governed agent on Major: connect your systems, let the agent reason once about the repeatable steps, and run them as apps with scoped credentials, RBAC and audit logs.

Frequently asked questions

What is an AI agent?: An AI agent is software that pursues a goal across multiple steps. It reads context, reasons about what to do next, calls tools to act, observes the result, and repeats until the goal is met or a limit trips. That loop separates an agent from a model that only answers questions. On Major, the agent reasons once and builds an app for the repeatable part, so the work becomes stateful and governable.
What does an AI agent do exactly?: An AI agent reads context from prompts or connected systems, decides on a next step, calls a tool or API to carry it out, checks what came back, and loops. A chatbot only replies. An agent changes records in real systems, so it needs scoped credentials, error handling, and durable state. On Major that state lives in a managed database inside the app the agent builds.
What is the difference between an AI agent and a chatbot?: A chatbot responds to messages with text and stops. An AI agent takes actions across multiple steps, calling tools and data sources to reach a goal. The dividing line is action. Because agents write to systems of record, they need permissions, audit logs, and a way to reconstruct what they did, which a chatbot never required.
What are the main types of AI agents?: The classic taxonomy lists five: simple reflex, model-based reflex, goal-based, utility-based, and learning agents. They differ in how they decide, from fixed rules on the current input to weighing trade-offs and improving from feedback. Most LLM-based agents today are goal-based or utility-based, and single versus multi-agent is an architecture choice.
How do you make an AI agent safe to run in production?: Scope its tools, run the actions through deterministic code, and log everything. Let the model decide what to do, then have a permission-checked app with typed inputs execute the write under the identity of the person who started the run. That makes every action attributable and replayable, and it keeps repeatable steps out of the model.