What is an AI agent and how is it different from a chatbot?

An AI agent is a program built on a language model that picks its own steps and gets the task done in your work tools: it reads the conversation, creates tasks, builds reports. A chatbot only answers messages, either from a script written in advance or, if it runs on a neural network, in its own words. If requests repeat and the answers are known in advance, a chatbot is enough, because it costs less. If every task is new and getting it done means going through several systems, you need an agent.

How an AI agent works

At the core of an agent is a language model (LLM). It's a neural network like the ones behind ChatGPT or Claude. On its own, a model can only write a reply to a message. It becomes an agent when it's given tools and uses them until the task is done.

Tools are access to work systems: the chat, the task tracker, the knowledge base, the calendar, the code repository. Once it has a task, the agent takes a first step, looks at the result and decides what to do next. Step by step it gets to the result, and at the end it reports back.

For example, a manager writes to the agent: "Pull together what we decided about the launch this week and create tasks for the open questions." The agent reads the week's discussions, writes down the decisions, finds the questions nobody answered, creates tasks for them in the tracker and assigns them to the people in the discussion. Then it sends a summary with links. The agent makes the plan itself.

How an AI agent differs from a chatbot

A chatbot does what a developer wrote into its script in advance, while an agent decides what to do as it works on the task. There are two kinds of chatbots: a classic one runs on a script of commands and buttons, and a chatbot on a neural network writes its own answer.

Take one request. An employee writes in the chat: "Move my vacation from July 10 to July 17." A scripted chatbot opens the vacation change form if it has that script, and if it doesn't, it says it didn't understand the command. A neural network chatbot explains how to move a vacation under company rules, but the employee files the request themselves. An agent checks how many vacation days are left, files the request in the HR system, sends it to the manager for approval and tells the employee the request is in.

Sometimes the same service is both a bot and an agent. ChatGPT and Claude answer like chatbots, but with access to email, a calendar or a browser they work as agents. So look at what the program does: if it only replies with text, it's a chatbot, and if it carries out steps in other systems, it's an agent.

When a chatbot is enough and when you need an agent

The choice depends on how similar the requests are and how many systems it takes to handle them.

A scripted chatbot fits when requests are all alike and the answers are known in advance: order status, office hours, a visitor pass request through a form. It costs less because it doesn't call a neural network: you only pay for the server. It gives the same answer to the same request, so you can check its answers in advance. It breaks on a request nobody planned for when setting it up.

A neural network chatbot fits questions people ask in different words when the answer is in your documents: vacation rules, plan terms, instructions for connecting to the VPN. It's connected to a knowledge base and writes its own answer. The downside is that the model sometimes states something untrue with full confidence, so answers to important questions get checked against the source. Every request to the model costs money.

An AI agent is what you need when every task is a little different and getting it done means going through several systems: sorting out a customer request and drafting a reply, building a report from the tracker and the chat, finding a bug in the code. An agent costs more because one task takes several requests to the model, and with each one it passes the instructions and everything it has already done all over again. An agent can also get an action wrong, for example assign a task to the wrong person.

You can connect a bot and an agent together. The bot takes routine requests through a form, and the agent handles an unusual question in the same chat.

How safe is it to let an agent take actions

An agent sees and does only what it's been given access to. So it only gets the chats and systems it needs, and actions that are hard to undo stay with a person: the agent prepares a draft email or task, and an employee checks it and sends it. The agent's messages are signed with its name, so you can always tell what a person did and what the agent did.

Why agents are connected to the team chat

To sum up a discussion or create a task, an agent needs the team's conversation, and that lives in the team chat. So you call the agent right into the chat.

Without an agent in the chat, it goes like this: copy the discussion, paste it into a separate AI chat, wait for the answer, go back to the messenger and retell the result to your teammates. And only the person who asked sees the answer. When the agent works in the chat, an employee @mentions it in the same discussion, and the agent reads what it's about on its own. The answer shows up right where the discussion happened, and everyone in it sees it.

It's best to call the agent into a thread. A thread is a discussion branch under a specific message. A thread has one topic, the agent reads only that, and its replies don't clutter the main chat. In one chat you can have several conversations with the agent in different threads, and they don't overlap.

In Pachca, an agent is connected as a bot: it has its own name and avatar, and its messages carry a "Bot" label. The agent responds when someone @mentions it, replies to it in a thread or sends it a direct message. It sees public channels, and of the private channels, chats and threads, only the ones it's been added to. If you remove the agent from a chat, it loses access to that chat right away, and the administrator decides who in the company can connect agents.

Threads in Pachca are cross-chat threads: you can @mention any teammate into a thread with the agent, even if they aren't in the chat. They'll see the whole thread history, including what the agent has already done. You can also start a conversation with the agent without tying it to a chat: click +, select New thread and @mention the agent.

Kai is an agent we built for ourselves at Pachca. A company can build the same kind of agent on the Pachca platform for its own tasks. A developer asks in a thread: "@Kai, why did the tests fail in the latest PR?" Kai reads the automated check report, finds the bug in the recent code changes and posts the fix in the same thread.

Beyond code, Kai makes edits to the website copy and handles about 90% of those tasks, so requests to the engineering team have been cut in half. In support, Kai reviews a request, finds the answer in the knowledge base and writes a draft reply, and a teammate checks it and sends it. An answer to a technical question now takes 2 minutes instead of 30. These are our own numbers as of August 2026.

You can connect an agent just for yourself or for the whole team. We explained how the two differ in the article "Personal vs. team AI agents". To start, ask the agent to sum up a long thread: a mistake here won't break anything, and after a week you'll see which tasks to hand it next.