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

Key Takeaways

• An AI agent is a software system that uses a language model to pursue a goal with some autonomy: it reads context, decides what to do, acts through tools, and adjusts based on the result.
• A chatbot responds. An agent takes actions on your behalf, across multiple steps, against real systems.
• The core mechanic is a loop: perceive, reason, act, observe, repeat until the goal is met or a limit is hit.
• The classic taxonomy has five types: simple reflex, model-based reflex, goal-based, utility-based, and learning agents.
• What makes an agent trustworthy in production is not more autonomy. It is that the reasoning is observable and the actions are deterministic and logged.

The actual definition

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, chooses a course of action, uses tools or data sources to act, then observes the result and adjusts. A chatbot only responds to you. An agent takes actions on your behalf across multiple steps.

That definition lines up with how the major references frame it. IBM describes an agent as a system that can autonomously accomplish tasks by reasoning and using tools. Google Cloud and AWS land in the same place: software that perceives, decides, and acts toward a goal rather than waiting for the next instruction. The agreement is worth stating plainly, because the word gets stretched to cover almost anything with an LLM behind it.

Three parts of the definition are load-bearing. There is a goal, which is more than a single prompt. There is autonomy, meaning the system chooses its own next step rather than following a fixed script. And there are tools, which are how the agent reaches outside its own text to read data and take action. Remove the tools and you have a model that talks. Remove the autonomy and you have a workflow. An agent needs both.

Why this matters now

The shift underway is from AI that answers to AI that acts. 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 it does something in the world that you then have to account for.

That is exactly why the governance bar rises the moment a system becomes agentic. When AI only produces text, the worst case is a bad answer a human can ignore. When AI takes actions against your systems of record, the worst case is an action you cannot undo and cannot reconstruct. The interesting question stopped being "can it reason" and became "can you trust what it did." MIT Sloan makes a similar point in its explainer on agentic AI: the capability is real, and so is the accountability problem that comes with it.

The sub-concepts worth knowing

How an AI agent works

Underneath the marketing, an agent runs a loop with four stages.

1. Perceive: read the relevant context, from a prompt, connected data sources, or the result of a previous step.
2. Reason: use the model to decide what to do next given the goal and the context.
3. Act: call a tool, query a data source, or take an action in an external system.
4. Observe: look at what came back, then loop again, until the goal is met or a stopping condition trips.

The loop is the whole idea. A model that runs the loop once is closer to a single tool call. A model that runs it repeatedly, choosing each next step, is behaving as an agent. Anthropic draws this distinction cleanly in Building Effective Agents: workflows are "systems where LLMs and tools are orchestrated through predefined code paths," while agents are "systems where LLMs dynamically direct their own processes and tool usage." The dynamic control is what people mean when they say agentic, and it is also the source of the governance problem, because the path is no longer fixed in advance.

The main types of AI agents

The classic taxonomy, from decades of AI research and repeated in IBM's own breakdown, names five types by how they decide.

• Simple reflex agents act on the current input using fixed rules, with no memory.
• Model-based reflex agents keep an internal model of the world, so they can act on more than the immediate input.
• Goal-based agents choose actions by reasoning about which ones move them toward a defined goal.
• Utility-based agents go further and weigh trade-offs, picking the action that maximizes some measure of value.
• Learning agents improve their behavior over time from feedback and experience.

A second axis matters in practice. A single-agent system has one agent doing the work. A multi-agent system splits the work across several agents that coordinate. Most LLM-based products people call agents today are goal-based or utility-based, often with a learning component, and the choice between single and multi-agent is an architecture decision, not a definition.

Agent vs chatbot vs assistant vs RPA

The categories near "agent" get used interchangeably and should not be.

• AI agent What it does: Reasons toward a goal and takes actions through tools across steps · Acts on your behalf?: Yes · Example: Reads a dispute, checks history, issues a refund
• Chatbot What it does: Responds to messages with text · Acts on your behalf?: No · Example: Answers an FAQ in a support widget
• AI assistant What it does: Helps a human who stays in control of each action · Acts on your behalf?: Partly; the human approves · Example: Drafts an email you then send
• RPA bot What it does: Repeats a fixed, scripted sequence of clicks or calls · Acts on your behalf?: Yes, but with no reasoning · Example: Copies invoice fields between two systems

The dividing lines are reasoning and action. A chatbot has neither beyond producing a reply. An assistant reasons but leaves the action to you. An RPA bot acts but does not reason, so change the screen layout and it breaks. An agent both reasons about what to do and takes the action, which is what makes it powerful and what makes it something you have to govern.

Reasoning vs execution: what makes an agent production-ready

Here is the distinction the reference pages tend to skip. In a demo, an agent reads context, decides, and acts all inside one reasoning loop. That is fine until the action is irreversible and someone asks what happened. If the deciding and the doing are fused, you usually cannot reconstruct, at the moment of action, what the agent saw, what options it had, and why it chose the one it did.

The fix is to separate the two layers. Let the agent reason: read across data sources, weigh the situation, choose a path. Then have it take action through a deterministic step with typed inputs, an enforced permission check, and a logged record, the same way any audited piece of software would. The reasoning stays flexible. The execution stays accountable. You can observe what an agent decided and did because the decision and the action are both captured, not blurred into one opaque call. More autonomy is not the goal. A scoped agent whose actions you can replay is worth more in production than a fully autonomous one you cannot.

Common misconceptions

"Is ChatGPT an AI agent?" On its own, no. Base chat responds to prompts and stops. It is not pursuing a goal across steps or taking actions in your systems. It becomes agentic when it is given tools and an act-observe loop, for example when connected to data sources and allowed to call functions. The model is the engine. The agent is the model plus tools plus the loop.

"An agent is just a clever system prompt." A prompt shapes how a model responds. It does not give the model the ability to read your CRM, call an API, and act on the result. Tools and the loop are what separate an agent from a well-worded chatbot.

"More autonomy is always better." It is not. Unbounded tool access is how agents take actions nobody intended. The useful question is not how autonomous an agent is, but how scoped its tools are and how completely you can audit what it touched.

What we're building at Major in response

The position we hold is simple to state. Agentic reasoning belongs in AI agents. Deterministic execution belongs in apps. Conflating the two is what makes AI agents feel ungovernable, and separating them is what makes them shippable in a regulated enterprise.

Major is the OS for Enterprise Agentic AI. In practice, it lets you build internal apps and the AI agents that act through them, with SSO, RBAC, and audit logs handled at the platform layer. An agent reasons across connected data sources. When it needs to take an action, it calls a Major-built app that executes the write with typed inputs, an RBAC check at the query layer, and an audit trail you can export to your SIEM.

Take a concrete example. An agent investigating a refund dispute reads the dispute from Stripe, the customer history from Postgres, and the refund policy from Slack. Then it calls a "Process Refund" app with typed inputs: customer ID, amount, reason. The agent's reasoning shows up in run history. The refund itself is a logged query, identical to any other app action, executed under the identity of the user who started the run rather than the publisher's. If anyone asks what happened, the answer is fully reconstructable.

Major lets you build, deploy and govern complex AI apps and AI agents in a secure, enterprise ready environment.
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