Conversation is the interface
AI Agent vs Chatbot
Understand why chatbots can use tools and agents can use chat, then compare authority, completion and handoff through a group-booking example.
A chatbot is a conversational interface or product category. An AI agent is a way of organising task execution in which a model can choose steps and use tools within defined boundaries. The categories overlap. A chatbot can retrieve information, call tools and complete transactions; an agent can operate through chat, an application screen or an event-driven background process.
For a customer experience lead, the useful comparison is therefore not “talking versus doing”. It is what decisions the system makes, which actions it can execute, how it verifies the result and when a person takes responsibility. This guide uses an illustrative museum group-booking service to make those distinctions concrete.
Separate the conversation from the operating capability
A museum visitor might ask about group sizes, access arrangements and available visit times in a single message. A conversational interface is useful because it lets the visitor express a need naturally. Behind that interface, the system could use fixed topics, a retrieval workflow, a model-selected sequence of tools or a combination.
The interface alone does not reveal autonomy. A polished chat window might only generate information. A simple form might trigger a sophisticated agent that gathers evidence in the background. Buyers should inspect the task contract rather than judging capability from the visual experience.
Microsoft's generative orchestration documentation describes conversational systems that select tools, knowledge and topics. It is a primary example of why a chat interface does not imply an inability to act. See Copilot Studio's orchestration guidance.
Three designs for the same visitor request
Information chatbot
The system answers questions from approved visitor information and points to the booking route. It can be valuable without making reservations. Its acceptance evidence concerns factual support, freshness, language, appropriate clarification and a usable handoff. It must not imply that a place is reserved merely because it described a visit option.
Conversational booking workflow
The system gathers required fields in a defined order, checks a permitted availability service, asks for confirmation and submits a booking through a controlled command. This can be a capable chatbot without dynamic agent planning. The workflow designer has already specified the route and its exceptions.
Bounded booking agent
The system can adapt its investigation: clarify whether the group needs a guided visit, inspect the applicable options, identify which constraint prevents a match and offer supported alternatives. Its action authority remains explicit. It may prepare a booking proposal while a person or established transaction rule controls confirmation.
All three can use the same chat interface. Select the third only where adaptive choices improve the visitor's task enough to justify additional evaluation and operating responsibilities. The automation comparison examines that design choice in more detail.
Compare evidence, authority and completion
| Dimension | What to ask the supplier | Evidence to inspect |
|---|---|---|
| Knowledge | Which source supports an answer and when is it refreshed? | Applicable source and version |
| Decision control | Are the next steps fixed or selected by a model? | A representative execution trace |
| Authority | Can it read, draft, hold, book, change or cancel? | Tool contracts and permission checks |
| Completion | What proves that a reservation exists? | Destination receipt and current state |
| Exception handling | What happens when no supported option fits? | Clarification or handoff with useful context |
| Language | Does the same task work in Arabic and English? | Comparable task cases, not translated screenshots |
Ask for a demonstration that includes failure and uncertainty. A scripted happy path may show the interface clearly but leave the most important operational questions unanswered. Conversely, a narrow information chatbot should not be penalised for declining a transaction it never claimed to support.
Worked case: a group changes its requirements
Suppose the visitor initially asks about a morning visit for a group, then adds that the date is flexible but a particular access arrangement is essential. A useful system preserves which requirement is fixed and which can change. It should not interpret flexibility about the date as permission to discard the access need.
An information chatbot can explain the relevant arrangements and route the visitor to staff. A defined booking workflow can collect the new field and restart its availability check. A bounded agent may choose which approved sources or tools to consult next based on the constraint. The correct comparison is whether each design handles the agreed task, not whether it uses the word “agent”.
If no supported combination is available, the system should state that limitation and offer the approved next route. It should not create a plausible alternative unsupported by the actual booking system. If it proposes a reservation, the visitor must understand whether it is a draft, a temporary hold or a confirmed booking.
The proposed test artifact is a conversation that changes one constraint at a time. Review whether the system preserves the unchanged constraints, rechecks relevant evidence and avoids duplicate actions. This tests useful task continuity rather than memory for its own sake.
Design status messages around actual state
Conversational fluency can make unfinished work sound complete. Define user-visible states that correspond to the application: information provided, details needed, proposal prepared, confirmation pending, confirmed, cancelled or outcome uncertain. Use only the states relevant to the selected workflow.
A tool timeout after submission must not become “your booking is confirmed” because that is the expected conversational ending. Nor should it automatically become “nothing was booked”. The application needs to reconcile the destination and explain the current uncertainty. The orchestration guide covers recovery and durable state.
Confirmation must also apply to a specific proposal. If the date, group details or conditions change after the visitor confirms, determine whether a new confirmation is required. A generic “yes” earlier in the conversation should not authorise every later variation of the transaction.
Make human handoff part of the service
A useful handoff includes the visitor's stated needs, verified evidence, unresolved questions and any operation already attempted. Do not force the visitor to repeat the whole conversation while also assuming the receiving employee can safely rely on an unstructured transcript.
Define who receives the case, the supported service hours and what the visitor is told about response expectations. Do not promise immediate help unless that service commitment exists. If staff cannot act on the assistant's output, improve the handoff contract before increasing automation.
The customer support agent guide explores service-team workflows. The human oversight guide addresses authority and intervention more broadly.
Evaluate conversation quality separately from task success
A friendly answer can be wrong, and a correct answer can be hard to use. Assess source support, preservation of constraints, clarification quality, successful handoff and actual transaction state. Also assess clarity and tone, but do not let fluency conceal a failed task.
For Arabic and English journeys, test equivalent intentions with natural wording rather than only literal translations. Include mixed-language names and dates where relevant. Evaluate whether the action arguments remain correct across languages. The Arabic-native design guide explains why language quality extends beyond the surface reply.
Include users who change their mind, ask multiple questions or stop midway. A robust service should distinguish abandonment from completion and avoid executing after cancellation. Record the supported boundary so the evaluation does not quietly reward functionality the project never authorised.
What should a buyer choose?
If a supplier calls the product an agent because it connects to a language model, inspect the underlying task and controls. The agent versus LLM wrapper comparison provides a capability review that goes beyond the conversational label.
Choose an information chatbot when reliable answers and navigation solve the need. Choose a conversational workflow when the route to completion is known. Consider an agent when the path genuinely depends on evolving evidence, while preserving explicit permissions and transactional controls.
Existing chatbot work may remain valuable. Approved content, conversation research, identity integrations and human handoff processes can support a later agent design. Assess what to retain instead of assuming a complete replacement is necessary. Equally, do not relabel a product as an agent and assume its operating obligations have changed automatically.
Open for AI develops scoped business agents around the task and its required controls. To review whether your conversational service needs an agent, bring a real journey where answering alone is insufficient, the permitted actions and the evidence that would prove completion. That separates a useful capability decision from a naming exercise.