The difference between an AI agent and a chatbot is what happens after the model replies: a chatbot's job ends with the answer, while an AI agent uses tools to act in other systems and decides its own next step until a task is finished. OpenAI's guide to building agents puts simple chatbots explicitly outside its definition of an agent, because the model in a chatbot does not control the workflow. For a business, the practical question is whether the job ends in an answer or in a change to a record, an order or an account; the first needs a chatbot, the second needs an agent and the controls that come with it.

What is the difference between a chatbot and an AI agent?

OpenAI defines agents as systems that independently accomplish tasks on your behalf. Its guide then draws the boundary directly: applications that integrate language models but do not use them to control workflow execution, such as simple chatbots, single-turn LLMs, or sentiment classifiers, are not agents.

Two characteristics make the difference, in OpenAI's description. An agent uses the model to manage the workflow, recognizes when a workflow is complete and can proactively correct its actions if needed, and can halt and hand control back to the user if it fails. And it has access to tools both to gather context and to take actions, choosing which to use based on where the task stands.

Anthropic's engineering guide makes the same split with different words. Agents are systems where LLMs dynamically direct their own processes and tool usage. Its customer support example is useful because it shows the two are not opposites: support combines familiar chatbot interfaces with enhanced capabilities through tool integration. The chat window can stay the same. What changes is what sits behind it.

How do AI agents and chatbots compare side by side?

Dimension Chatbot AI agent
Goal Answer the current message Complete a task, across several steps if needed
Tools None, or read-only search over documents Data tools to look things up and action tools to change records, send messages or issue refunds
Memory The conversation so far, resent with every turn The conversation plus external memory and the state of the systems it works in
Autonomy One reply per user turn Loops through tool calls until the task is done or a stop condition is reached
Typical failure A wrong or invented answer A wrong action in a real system
Cost per interaction One model call per reply Many model calls per task, each carrying the growing context
Main controls Answer quality, source documents, escalation to a human Tool permissions, approval gates, logs, iteration limits

A few rows need the detail behind them. On memory, a language model API does not remember anything between calls: Anthropic's documentation describes each turn's input as containing all previous conversation history plus the current user message. Agents that need to remember across sessions are given a memory store; Anthropic's memory tool, for example, lets Claude store and retrieve information across conversations in a directory of memory files.

On cost, the loop is the multiplier. In Anthropic's data, agents typically use about 4× more tokens than chat interactions. On pricing, Anthropic has observed companies charging only for successful resolutions; Intercom's Fin, for instance, lists $0.99 per outcome as of October 2026.

What sits between a chatbot and an AI agent?

Very few systems are purely one or the other. It helps to think of a spectrum with five steps, each adding one capability:

  1. Scripted bot. A decision tree or keyword matcher with fixed answers. No language model, no surprises, and no ability to handle a question nobody anticipated.
  2. Language model chatbot. A model answers from its training and its instructions. Fluent, but it only knows what it was trained on and what you put in the prompt.
  3. Retrieval assistant. The system searches your documents and gives the relevant passages to the model before it answers. This is retrieval-augmented generation, which the original 2020 paper described as combining a model's parametric and non-parametric memory. It answers from your policies, but it still only answers.
  4. Tool-using assistant. The model can call functions to look things up, such as an order status or account details. OpenAI's function calling guide lists examples ranging from reading data to issue refunds for a lost order. With read-only tools, this is still an assistant.
  5. Agent. The model decides which tools to call, in what order, including tools that change things, and keeps going until the task is resolved or handed to a person.

Most of the value for customer questions is captured at steps 3 and 4. The jump to step 5 is where the risk changes, because the system can now act.

Is conversational AI the same as an AI agent?

No. Conversational AI describes the interface: a system you talk to in natural language, by text or voice. An AI agent describes the capability: a system that completes tasks with tools. A voice bot that reads out opening hours is conversational AI but not an agent. A background agent that processes refund requests from an inbox is an agent with no conversation at all. A support assistant that talks to the customer and issues the refund is both.

Under the hood, both use the same mechanism to reach other systems. Anthropic's documentation describes tool use as letting the model call functions you define, after which it returns a structured call that your application executes. The difference between an assistant and an agent is which functions you expose and how many steps you let it take on its own.

When is a chatbot enough?

When the job ends in information. If customers ask about policies, opening hours, product details or how to do something, a retrieval assistant grounded in your own documents answers most of it, and the worst case is a wrong answer that a human can correct. Anthropic notes that for many applications, optimizing single LLM calls with retrieval and in-context examples is usually enough.

A chatbot is also the right first step when your knowledge base is not ready. An agent acting on outdated policies does more damage than a chatbot quoting them. If support volume is the driver, our guide to customer support automation covers where answers end and actions should begin.

When do you need an AI agent instead?

When the job ends in an action and the steps vary from case to case. Anthropic's description of support agents is a good checklist: the conversation requires access to external information, actions such as issuing refunds or updating tickets can be handled programmatically, and success can be measured through resolutions. OpenAI adds that agents fit decisions with nuanced judgment and exceptions, rulesets too complex to maintain, and heavy reliance on unstructured data such as documents.

Concrete cases include changing a booking, processing a return with a policy check, updating a CRM after a call, or chasing missing documents for a claim. You can see how products and businesses use agents for this kind of work in our list of AI agent examples.

What can go wrong with each?

A chatbot fails by saying something wrong. That can be costly, but it is visible and it can be corrected in the next message.

An agent fails by doing something wrong. OWASP calls this Excessive Agency: the vulnerability that enables damaging actions to be performed in response to unexpected, ambiguous or manipulated outputs from an LLM, with excessive functionality, permissions and autonomy as the usual root causes. Agents that read emails, documents or web pages are also exposed to indirect prompt injection, which OWASP says occurs when a model accepts input from external sources, such as websites or files that alter its behavior.

The controls follow from that. OpenAI recommends that actions which are sensitive, irreversible, or have high stakes should trigger human oversight until you trust the agent's reliability, with canceling orders, authorizing large refunds and making payments as its examples.

Which one should your business build?

Answer five questions in order:

  1. Does the job end in an answer or an action? Answers point to a chatbot or retrieval assistant. Actions point to an agent.
  2. Does it need private data, such as an account or order? Then you need at least read-only tools, which is step 4 on the spectrum, not necessarily an agent.
  3. Can you write the steps down in advance? If yes, a workflow with a model inside one step is cheaper and easier to audit; we explain that choice in AI agents vs workflow automation.
  4. What is the worst action it could take, and who approves it? If you cannot name the approver, you are not ready to let it act.
  5. What is a conversation worth? An agent's extra model calls are easy to justify on a refund dispute and hard to justify on a store hours question.

If the answers point to an agent, Qubba builds custom AI agents around your own systems, scoped per process with a fixed written quote after a 30 minute call.

Frequently asked questions

Can I turn my existing chatbot into an AI agent?

Usually yes, one step at a time.
Ground it in your documents first, then add read-only tools such as an order lookup, and only then add a single action tool behind an approval step.
Each step can be tested on its own before customers see it.

Are AI agents more expensive than chatbots?

Per interaction, yes, because an agent makes several model calls per task and each one carries the context so far.
Whether that is expensive depends on the value of the task.
Paying several times more per conversation to resolve a refund end to end can still be cheaper than a person handling it.

Do AI agents remember past conversations?

Only if they are built to.
The model itself keeps nothing between requests; memory comes from what the application stores and sends back, such as a conversation history, a customer record or a memory file.
Decide deliberately what an agent should remember, because stored memory is also data you have to protect.

Is it safe to put an AI agent in front of customers?

It can be, if its tools are limited to the task, authorization is enforced in your systems rather than in the prompt, and irreversible actions wait for approval.
Start with a narrow scope, log every tool call, and widen it only when the logs show it is working.