--- title: "Chatbot vs AI agent: platform comparison" description: "The word agent is unregulated. A real message, what each kind of product does step by step, and how many steps are left after the reply." url: https://studiochat.io/blog/chatbot-o-agente-que-estas-comprando locale: en translations: { es: https://studiochat.io/es/blog/chatbot-o-agente-que-estas-comprando.md, pt: https://studiochat.io/pt/blog/chatbot-o-agente-que-estas-comprando.md } site: https://studiochat.io --- [ Back to blog](/blog) Over the last two years the word agent replaced the word chatbot on nearly every site in the industry, ours included. The practical problem is that nobody controls that word. There is no certification, no agreed technical definition, and nothing stopping a product that did not change from calling itself something new. So understanding the difference means looking at something else: what each one does, action by action, with a real message. As in the other two posts in this series, what we say about third parties is their own public description, read on 17 October 2026. ## [The word agent is unregulated](#the-word-agent-is-unregulated) It is worth seeing how far this goes. Twilio, a communications infrastructure company, describes itself as a platform where "humans and AI agents work together". Gupshup talks about "autonomous AI agents". Infobip says it is an "operating system for agentic customer experiences". Zendesk and Intercom talk about AI agents built into the help desk. Agentforce carries the word in its name. All of those statements are true in the sense each company uses them. What can no longer be done is comparing on that word. ## [The same message, put to all three](#the-same-message-put-to-all-three) This is the test, and it can be run in any demo: bring a real message from your operation and ask them to resolve it in front of you. Below are four real messages. For each one, what a button flow does, what an assistant that answers does, and what an agent that executes does. Not the wording of the reply: the list of actions. The same message, put to all three Pick the message that comes in every day: I want to change the delivery addressSend me September's invoiceI need 20 boxes of the 480 for ThursdayRunning late today, I don't leave until 10 Button flow Shows a menu of options and none of them is changing the address. what it does, step by step * Shows the menu it has loaded * Cannot find the option and offers a person * left for the customer: 3 * left for the team: 2 5 steps pending Assistant that answers Correctly explains how an address gets changed and sends the link to the form. * Searches the knowledge base * Writes the steps out and sends the link * left for the customer: 2 * left for the team: 1 3 steps pending Agent that executes Changes the address and sends the confirmation with the new destination. * Identifies the account from the number writing * Finds the order in the system * Checks it has not shipped yet * Writes the new address onto the order * Notifies the carrier of the change * Sends the confirmation to the chat * left for the customer: 0 * left for the team: 0 0 steps pending With the same message there are 5 steps pending in the first case, 3 in the second and 0 in the third. What changes is not the wording of the reply: it is whether anything touched a system. The three types are categories of behaviour, not products. Any platform can land in either column depending on the case, which is why the test gets run on your messages and not on the demo's. Look at the right hand column on the order message. Six actions, four of them against different systems, one of them a write. That is an AI agent. The other two columns answer well and touched nothing. The difference is not the quality of the wording. It is that after the third column there is no work left. This object also has [a page of its own](/which-steps-remain), in case you want to send just that. ## [What each platform on the list actually does](#what-each-platform-on-the-list-actually-does) With the usual caveat: these are their phrases, not our evaluations. | Platform | Its phrase | What that phrase suggests about its focus | | ---------- | ------------------------------------------------------------------------------------------- | ------------------------------------------------- | | ManyChat | Automations for Instagram, WhatsApp, TikTok and Messenger; "chat marketing" | Marketing conversations at scale, self serve | | Twilio | "Humans and AI agents work together", over APIs for SMS, RCS, voice and email | The channel and orchestration across channels | | Infobip | "One operating system for agentic customer experiences" | Unified messaging with an experience layer on top | | Gupshup | "Autonomous AI agents for every customer conversation", for marketing, commerce and support | Automated commercial conversations | | Zendesk | AI agents that "resolve issues end to end" | Resolution inside the help desk flow | | Intercom | "The only helpdesk with a natively integrated AI agent" | The agent as part of the helpdesk, not an add on | | Agentforce | "Autonomous support at enterprise scale", integrated into the Salesforce ecosystem | Execution inside the CRM | | Sierra | "The leading conversational AI platform for businesses" | The brand's conversational experience | | StudioChat | Agents that execute inside the systems the company already uses | Execution against third party systems, by process | Almost all of them say agent and almost none of them say the same thing. That is not a contradiction: they are using the same word to describe the different place where each one is strong. ## [Why StudioChat is on the execution side](#why-studiochat-is-on-the-execution-side) Because that is the product decision we made, and it is the one that produces the third column of the tool above. An agent that executes needs permissions over systems that are not ours and an implementation per process. That is slower to launch than a frequently asked questions bot, and we say so up front rather than afterwards. What you get is the case closing. Zero steps pending on the messages that eat the team's day today, which no well written reply achieves. And this choice has a property worth keeping in mind: execution includes answering. An agent that enters an order into the ERP also gives out opening hours. The reverse does not happen, which is why choosing execution leaves no process out. The client figure we publish is Takenos: 70% of tickets resolved with no human involvement, with a response time of around one minute. To understand exactly what executing means, there is the post on [reading, writing or deciding](/blog/leer-escribir-decidir), which is the permissions ladder worth holding any proposal up against. ## [The test worth asking for in the demo](#the-test-worth-asking-for-in-the-demo) One, and it is this post's. Bring three real messages from your operation, the kind that come in two hundred times a month. Ask them to resolve them in front of you. Then count two things: which systems it touched, and how many steps were left pending when it finished answering. If the answer to the first is none, you already know which column the product is in, whatever it is called. Frequently asked questions ### What's the difference between a chatbot and an AI agent? A chatbot answers: after its reply the work still exists and somebody else does it. An agent executes: it identifies the account, queries the systems, writes into them and leaves the case closed. The practical test is taking a real message and counting what each one did step by step, and how many steps were left pending afterwards. ### What exactly does an AI agent do with a message? With an I want to change the delivery address, an agent identifies the account from the number writing, finds the order in the system, checks it has not shipped, writes the new address, notifies the carrier and sends the confirmation. That is six actions against real systems, not one well written reply. ### Why does every platform say it has agents? Because the word has no technical definition anybody controls, and because the category genuinely moved that way. The name stopped being useful for comparing, so you have to look at what each one can execute against the company's systems. Share [ X](https://x.com/intent/post?text=Chatbot%20or%20agent%3A%20the%20same%20message%20put%20to%20all%20three&url=https%3A%2F%2Fstudiochat.io%2Fblog%2Fchatbot-o-agente-que-estas-comprando) [ LinkedIn](https://www.linkedin.com/sharing/share-offsite/?url=https%3A%2F%2Fstudiochat.io%2Fblog%2Fchatbot-o-agente-que-estas-comprando) [ WhatsApp](https://wa.me/?text=Chatbot%20or%20agent%3A%20the%20same%20message%20put%20to%20all%20three%20https%3A%2F%2Fstudiochat.io%2Fblog%2Fchatbot-o-agente-que-estas-comprando)Copy link Ignacio Puig Moreno Ignacio Puig Moreno · Co-founder · Business, StudioChat Want agents like these working for your team? [Talk to us now](https://cal.com/studiochat)