In short: A chatbot answers messages. An AI agent pursues a goal: it decides which steps are needed, uses tools such as a calendar, CRM or ticketing system to take them, and checks the result after each step. What separates it from a chatbot is not the quality of its language but its ability to act. That is as true in chat as it is on the phone.
We revised this post in October 2026. The previous version quoted resolution rates of 15 to 25 per cent for scripted bots and 60 to 80 per cent for AI agents, plus a fixed 70/30 split between AI and humans. We found no evidence for these figures, so they are gone. It also named channels and a language count that do not apply to EchoCall. The correct picture: EchoCall supports more than 30 languages. WhatsApp is currently not part of what you set up yourself in the EchoHub. Today the EchoCall chat agent runs on your website, with handover to your team.
What is the difference between AI agents and chatbots?
A chatbot reacts. One input leads to one output. With a classic rule-based bot, that output was written in advance and is triggered by a decision tree or a keyword [5]. An AI chatbot writes the answer itself using a language model. It sounds more natural, but the principle stays the same: it answers, it does not act.
An AI agent works differently. OpenAI describes agents as systems that independently accomplish tasks on a user's behalf [1]. The guide names two core characteristics. First, the language model controls the workflow: it recognises when a task is complete and can stop and hand control back to the user if something fails. Second, the agent has access to tools to gather information and take actions, within defined guardrails. Applications that integrate a language model but do not let it control the workflow are not agents by this definition, and simple chatbots are named explicitly [1].
Anthropic puts it even more plainly: agents are typically just language models using tools based on feedback from their environment, in a loop [2]. IBM adds that chatbots without agent capabilities have no tools, memory or reasoning, and need new input from the user for every step [3].
In short, the difference is not how well a system writes. It is who decides the next step.
If you want to decide specifically for your website chat, with a table, costs and a worked example, read AI Chat Agents vs. Traditional Chatbots: Differences, Costs and When a Rule-Based Bot Is Enough. This post sticks to the fundamentals.
The four building blocks of an AI agent
OpenAI breaks an agent down into three core components: the model, the tools and the instructions [1]. In customer service, a fourth building block joins them: the route to a person.
1. Model and instructions. The language model understands the request and decides. The instructions define what the agent is responsible for, what tone it uses and what it must not do.
2. Tools. OpenAI distinguishes three types [1]. Data tools retrieve information, such as an order status or free appointment slots. Action tools change something, for example adding an appointment or handing a customer service ticket over to a person. Orchestration tools connect several agents with each other.
3. The loop of reasoning and acting. In 2022, Yao and colleagues described an approach called ReAct, in which a language model generates reasoning traces and actions in an interleaved manner [4]. This loop is what makes an agent. An example: a customer writes "Can I move my Thursday appointment to next week?". The agent finds the existing appointment, checks free slots next week, suggests two, rebooks after confirmation and reports back. Each step depends on the result of the previous one.
4. Guardrails and handover. OpenAI explicitly advises planning for human intervention. Typical triggers are repeated failure and high-risk actions such as cancelling orders or making payments [1].
Workflow or agent: who decides the next step?
Not everything sold as an AI agent is one in the strict sense. Anthropic separates two designs [2]. Workflows are systems where language models and tools are orchestrated through predefined code paths. Agents are systems where the language model dynamically directs its own processes and tool usage.
Both have their place. A workflow is more predictable and easier to test. According to Anthropic, an agent pays off where it is difficult or impossible to predict the number of steps required [2]. That is often the case in customer service: one caller only wants the opening hours, the next wants to move an appointment, have an invoice explained and add a new delivery address.
In practice, ask every vendor who decides the next step. If it is a hard-wired flow in which a language model merely phrases the texts more nicely, you are looking at an AI chatbot with a good tone of voice. That may be perfectly fine. It just is not an agent.
AI agent, AI assistant, chatbot: three terms sorted
The terms are not standardised. This is how we use them:
| Chatbot | AI assistant | AI agent | |
|---|---|---|---|
| Who decides | the decision tree | the person using it | the agent, within limits |
| Typical task | answer fixed questions | draft, summarise, research | complete a task for a customer |
| Tools | none, or links | usually few, triggered by the person | calendar, CRM, ticketing system, shop |
| Counterpart | customer | employee | customer or employee |
| Example | buttons "Delivery" and "Returns" | draft of a reply email | move an appointment and confirm it |
An AI assistant supports a person who checks the result and uses it. An AI agent works through what a customer wants on its own, and only comes back to you when it reaches a limit.
How much autonomy makes sense?
Autonomy is not an end in itself. For most organisations, levels work well:
- Information. The agent answers questions from your knowledge base and does not act.
- Preparation. The agent records the request and hands over to a person who decides.
- Low-risk execution. The agent books appointments, opens tickets and looks up orders.
- High-risk execution. Cancellations, credits and payments. Here OpenAI advises handing the decision to a person [1].
OpenAI also recommends starting small, validating with real users and growing capabilities over time [1]. An agent that first only books appointments, and does so reliably, is worth more than one that is meant to do everything.
Whatever the level, the EU AI Act applies. Under Article 50(1), providers must design AI systems that interact directly with people so that those people are informed they are dealing with an AI [6]. In the EchoCall chat widget, this notice appears automatically. For phone agents, you write it into the first message yourself, and the editor reminds you with an info box.
On the phone: IVR menu versus voice agent
The same difference applies to the phone. The classic menu with "Press 1 for..." is the chatbot of the telephone: fixed paths, fixed announcements. A voice agent turns speech into text, lets a language model decide, answers with a synthetic voice and uses the same tools as a chat agent.
At EchoCall, an AI voice agent has its own system tools for this: end the call, detect voicemail, send keypad tones, detect the caller's language and transfer to a phone number. On top come actions such as an appointment in Google Calendar or Cal.com, or an entry in HubSpot. The voice agent starts at €75 a month for 250 minutes, or €0.30 per minute [7]. Phone numbers and telephony costs are not included.
What this looks like day to day is covered in the AI Voice Agents guide. You can find the chat agent under AI chat agent.
Checklist: agent or chatbot?
- Do the wording and topics of requests vary a lot? Then that points to an agent.
- Should something get done, such as an appointment, a status check or a ticket? Then the agent needs a tool for it.
- Does the number of steps depend on the individual case? Then an agent makes more sense than a rigid flow.
- Do many requests come in by phone? Then a voice agent belongs on the list, not just a chat.
- Are there only a few fixed questions, no connected systems and one language? Then a simple chatbot is enough.
- Are there high-risk actions? Then plan the handover to a person from the start.
If you answer yes to several questions, try an agent with a single tool before you build more. On the demo page, an agent is built from your website address in about 20 seconds, and you can write to it or call it in the browser without signing up. If you want to continue after that: the trial covers 30 voice minutes and 30 chat conversations for 14 days, and the credit card is verified with €1. Our team answers questions via the contact form.
Frequently asked questions
What is the difference between AI agents and chatbots?
A chatbot answers messages, either with pre-written texts or with the help of a language model. An AI agent pursues a goal, decides on the next steps itself and uses tools such as a calendar, CRM or ticketing system to complete a task. It hands over to a person when it gets stuck or when an action is too risky.
What is the difference between an AI agent and an AI assistant?
An AI assistant supports a person who makes the decision and uses the result, for example when drafting an email. An AI agent completes a task itself, such as moving an appointment for a customer, and stays within defined limits while doing so. The terms are not standardised, and vendors use them differently.
Is an AI agent harder to set up than a chatbot?
The effort is different. With a rule-based bot, you build every path by hand. With an agent, you maintain a knowledge base, write clear instructions and test every tool. The demo gives you a first impression by building an agent from your public pages in about 20 seconds. For real operation, plan time for testing with typical requests.
Can I switch from my existing chatbot?
Yes. The answers from your old bot and your FAQ pages become the agent's knowledge base. At EchoCall you upload files such as PDF, TXT, Markdown or CSV, add single URLs, have the website crawled or write free text. After that, you add tools one at a time.
Where is the data stored at EchoCall?
EchoCall runs the platform in IONOS data centres in Frankfurt and Karlsruhe. The EchoHub also has a zero-PII mode. For your own use case, it is still worth checking which personal data your agent actually needs.
Do AI agents need more oversight than chatbots?
Yes, because they act. Define which tools the agent may use, what it must not promise and when it hands over to a person. Read along with the conversations in the first weeks, and only extend its permissions once the simple tasks run reliably.
Sources
- OpenAI, A practical guide to building agents: cdn.openai.com
- Anthropic, Building effective agents (19 December 2024): anthropic.com
- IBM, What Are AI Agents?: ibm.com
- Yao et al., ReAct: Synergizing Reasoning and Acting in Language Models (2022): arxiv.org
- Consumer Financial Protection Bureau, Chatbots in consumer finance (6 June 2023): consumerfinance.gov
- Regulation (EU) 2024/1689 (AI Act), Article 50: eur-lex.europa.eu
- EchoCall pricing: echocall.de/en/pricing
All links were checked on 6 October 2026. This post is not legal advice.
