01 / The definition
The key difference is who chooses the next step.
Traditional software follows a route a developer defined in advance. An AI agent can select the next step from the task, its current context, and the tools it is allowed to use. That makes it useful when the work has variable inputs or exceptions. Anthropic calls a preset sequence of model and tool calls a workflow, and reserves agent for systems where the model directs its own process. [2]
“Agent” is also used broadly in product marketing. A helpful test is to ask: Can the system choose and revise actions toward a goal, or does it simply return one response or follow a fixed script? Human oversight does not make a system less agentic; it defines where the system hands control back.
Goal + model decisions + permitted tools + feedback + a stopping rule = a practical modern AI agent.
02 / The mechanism
How does an AI agent work?
Most modern software agents combine a model, instructions, and tools. The model chooses a step; a tool provides information or performs an allowed action; the agent reads what happened and decides whether to continue. OpenAI describes this as a run loop with explicit exit conditions, including completion, error, or a maximum number of turns. [1]
- 01
Start with an outcome and a boundary
A person states the task, provides relevant context, and defines what the system may do. Clear instructions and permissions keep the work specific.
- 02
Select the next useful step
The model decides whether to answer, ask a question, retrieve information, or call an available tool. It can adjust the plan as new information arrives.
- 03
Use a tool and inspect its result
A tool might search documents, read a page, or prepare a draft. The result becomes new context, so the agent can continue, correct course, or stop.
- 04
Check the output and hand off
The run ends at a defined stopping point. A person reviews consequential actions, checks evidence, and decides whether to approve, edit, or reject the result.
The agent may repeat the choose → act → observe portion until it reaches a result, a limit, or a point that needs human input.
Not every agent has long-term memory, unrestricted internet access, or the ability to take external action. A read-only research agent and an agent permitted to edit records have very different risk and capability profiles.
03 / The distinction
Agent vs. chatbot vs. fixed automation
The terms overlap in real products, but this comparison helps identify what the system actually controls. [1][2]
| System | What directs the steps? | Typical result |
|---|---|---|
| Basic chatbot | The user asks; the system responds to that turn. | An answer, suggestion, or draft. |
| Fixed automation | Prewritten rules and branches determine the path. | The same known sequence for matching inputs. |
| AI agent | The model selects or revises steps within instructions and allowed tools. | A multi-step task result, tool action, or handoff. |
Example: “send a welcome email after signup” is often best handled by fixed automation. “Investigate this unusual support case, read the relevant policy, and draft a response” may benefit from an agent.
04 / Capabilities
What can AI agents do?
With appropriate tools, an agent can retrieve context, compare evidence, draft material, edit a file, update a record, or ask for approval. The model cannot call a tool it has not been given or reliably know facts it cannot verify. OpenAI groups tools into data, action, and orchestration roles. [1]
Find and connect evidence
Read documents, search a knowledge base, or compare records before forming an answer.
Turn insight into a draft
Write a support reply, a code patch, or a project plan with a reason to review it.
Use approved tools
Carry out bounded actions such as updating a field when the system has that access.
Stop at the right point
Return uncertainty, request missing information, or seek approval before a consequential change.
05 / In practice
Three concrete examples
A service case with an exception
A support agent reads the customer's message, retrieves an order and the current policy, then drafts a resolution. If the case is ambiguous or a refund exceeds its limit, it passes the case to a person. This is an illustrative workflow, not a claim about Growit. [1]
A code change with a test result
A coding agent inspects a repository, edits a scoped file, runs checks, and uses the errors to revise its patch. A developer reviews the diff and decides whether to merge it. This is an illustrative workflow. [2]
A brief with an approval point
In the signed-in O1 AI web beta, a person can configure an agent to use tasks, memory, and web search for a morning brief. If an agent wants to change something in a connected app, the request waits for the person to allow or decline it. The O1 device is a separate product in development.
Explore the O1 AI beta06 / Types
What are the different types of AI agents?
There is no single official number. In current product design, a useful distinction is a single agent with tools versus a multi-agent system where agents coordinate or hand off work. Systems can also be interactive or run in the background. More agents do not automatically make a system better; additional coordination brings complexity. [1][3]
In classic AI, Artificial Intelligence: A Modern Approach describes four basic agent-program designs: simple reflex agents respond to the current input; model-based reflex agents track hidden state; goal-based agents choose actions toward a goal; and utility-based agents weigh outcomes. Learning can extend these designs. That textbook taxonomy is useful for understanding behavior, though it is not a mandatory product checklist for today's LLM agents. [4]
07 / Safe use
Where agents need limits and human judgment
Agents can make incorrect inferences, use stale data, choose the wrong tool, or keep going after the best moment to stop. Tool access makes an error more consequential than a mistaken chat answer. OpenAI recommends layered guardrails and clear instructions; Anthropic recommends beginning with the simplest design that works and evaluating it against real tasks. [1][2]
A sensible first setup
- Define one bounded goal. State the task, the allowed sources, and what a good result looks like.
- Start with limited tools. Give read access first; add write access only when the task requires it.
- Set stop and approval points. Require review for public, financial, destructive, or hard-to-reverse actions.
- Check real outcomes. Test representative cases, inspect failures, and keep a way to correct or undo actions.
Use fixed automation when rules are stable and exceptions are rare. Use an agent when the system must interpret varied inputs and decide which step fits, and when you can evaluate the result. [1]
08 / Quick answers
Frequently asked questions
Clear answers to the terms that usually get mixed together.
What is an AI agent in simple terms?
It is software that pursues a goal by choosing and carrying out steps, often using an AI model and tools. It can inspect results and decide what to do next within its instructions and permissions.
Are AI agents and chatbots the same thing?
No. A basic chatbot answers a prompt. An agent can direct a multi-step task, select tools, inspect what happened, and continue or hand control back. Some products combine both behaviors.
How many types of AI agents are there?
There is no single universal count. A classic AI textbook describes four basic agent-program designs and explains learning as an extension. Modern products are also grouped by how they run, such as single-agent or multi-agent systems.
Can AI agents work without people?
Some can complete bounded tasks with little intervention, but their permissions and stopping rules still need to be set. A person should review high-impact, costly, public, or hard-to-reverse actions.
Do I need an AI agent framework to start?
No. A small agent can begin with one model, clear instructions, one or two well-defined tools, and a testable stopping condition. A framework can help later when orchestration or observability becomes complex.
Does Growit offer AI agents?
Growit O1 AI is a web beta with agents, tasks, memory, web search, image tools, and apps a user connects. Changes in connected apps wait for the user's approval. The O1 device is a separate product in development.
Editorial record
Primary source trail
Definitions and examples were checked against the following primary references on October 12, 2026. The Growit example reflects the O1 AI web beta and its approval controls.
- 01OpenAI · A practical guide to building agents
Model, tools, instructions, the run loop, and guardrails.
- 02Anthropic · Building effective agents
The distinction between fixed workflows and model-directed agents.
- 03Google Cloud · What are AI agents?
Definition, capabilities, and modern ways to group agents.
- 04Russell & Norvig · Artificial Intelligence: A Modern Approach, chapter 2
The classic reflex, model-based, goal-based, utility-based, and learning-agent taxonomy.
Editorial link interaction adapted from Skiper UI.