A chatbot is a conversational interface; an AI agent can also choose steps and use tools to complete a task. That distinction alone does not tell you what your company needs. A chat window can start a tightly controlled process, while an agent can operate without a visible conversation. Start with the decision you want to support and the work that needs to happen afterwards.
This guide helps you define a useful first application. Consider answering questions, gathering information, preparing a suggestion and executing an action separately. That makes it easier to identify where flexible language handling helps and where ordinary software gives you greater predictability. The examples are illustrative rather than descriptions of delivered OXIAE customer systems.
Start with a concrete verb
Write the proposed task as a specific sentence. “Answer questions about opening hours” mainly requires current information and a clear reply. “Process an order” involves identity, product selection, availability, pricing agreements and a change in a business system. Buying both under the same chatbot label can hide the most consequential differences in responsibility.
Separate a broad assignment into reading, assessing, proposing and executing. An employee might ask software to summarise a customer request without permitting it to change contact details. Such a limited first version is often easier to evaluate. Also consider what an error means: an awkward summary, a missed enquiry and an unauthorised contract change require different controls and recovery options.
When a fixed workflow is a better fit
A fixed workflow suits tasks whose normal path and exceptions can be described in advance. Validating a form, storing a file and creating a task rarely need model-directed planning. You can include one AI step, such as classifying free text, while keeping the surrounding process in ordinary software with explicit validation and known states.
Investigate the configuration of existing products first. Your CRM might already support enquiry forms, notifications and automatic task assignment. Adding another system may mainly create maintenance work. Flexibility becomes valuable when variation in the actual work requires interpretation. A large number of clicks does not by itself justify an agent; a reliable integration can remove those clicks as well.
When a chatbot is sufficient
A chatbot can help visitors find relevant information, clarify a subject or gather information for a conversation. Its value may lie in understandable answers, suitable sources and an easy route to a person. It does not need to act independently in external systems. Clear limits can help users understand what they can reasonably expect from the interaction.
Pay particular attention to questions requiring personal context. A public website assistant can explain general delivery times, but an individual order status requires dependable identification and appropriate access. Distinguish public information from records belonging to a particular customer. A conversation is not evidence that the person is entitled to an account, document or customer file.
A fixed workflow may suit the business problem better. Assess the process itself as well: Which business process should you automate first?.
When agent behaviour adds value
Agent behaviour becomes useful when the next step depends on information discovered along the way. An internal assistant might identify a missing order reference, ask for it and then propose the appropriate lookup. OpenAI’s function-calling documentation explains how a model can request a tool; application code processes that request and controls the actual execution.
Reflect this separation in the brief. “The agent can use our calendar” leaves too much unspecified. You can expose separate capabilities for reading availability, preparing an appointment suggestion and confirming a booking. Language can remain flexible while the actual operations remain bounded. Each additional tool also creates more potential combinations that need evaluation, documentation and ongoing support.
Illustrative example: enquiries at a print studio
A fictional print studio receives emails requesting brochures. The first application extracts quantity, format and requested delivery date, then lists missing information in a draft response. It makes no price commitment. An employee reviews the draft and decides whether the enquiry is ready to progress. This combines language handling with a fixed process without requiring an independently negotiating sales agent.
A later version could retrieve available product options from a maintained catalogue. Automatically producing quotations introduces further questions about price versions, discounts, stock and validity periods. That expansion should be a separate decision. The appearance of one fluent conversation should not conceal the greater consequences of a mistake once commercial commitments become possible.
Compare the whole operating burden
Compare options across preparation, source management, review, recovery and handover. An agent that saves thirty seconds of handling but frequently requires ten minutes of investigation may offer poor value. Measure useful outcomes alongside review effort. Include the time employees spend keeping examples, instructions and operational information current as the business changes.
Identify who will understand the application when its builder is unavailable. A process diagram may explain a fixed workflow; an agent additionally needs an up-to-date view of its tools, decision boundaries and evaluation cases. A cheap prototype says little about this continuing responsibility. Ask competing suppliers to use the same task boundary and exception scenarios so that proposals remain comparable.
Make the first choice testable
Collect examples from the intended process and include difficult variants: a missing reference, conflicting requirements, an unknown product and a request outside the permitted task. Describe a useful result for each example. Sometimes that means answering, sometimes asking one targeted question and sometimes handing over. A representative test collection makes otherwise abstract capability claims concrete.
Ask employees to judge these examples without focusing only on writing quality. Check factual support, invented commitments and whether the next step is recorded correctly. Then introduce the application to a limited group. Retain the examples for regression checks after changes. OpenAI’s evaluation guidance supports task-specific testing; the acceptance boundaries still need to reflect your own process and its consequences.
Record the decision and its limits
The decision document can be short. Record the current problem, selected task, alternatives, first users and most important error consequences. State which operations remain with employees and explain why. Include an acceptable outcome and an example requiring the system to stop. That gives a future operator a practical understanding of the design rather than only its product name.
Schedule review once you have encountered enough varied situations, rather than relying only on a date. Testing mostly straightforward questions provides little evidence about conflicting or incomplete enquiries. Expand only when the current task is manageable and the next capability has a clear purpose. Agents, chatbots and fixed workflows can eventually work alongside one another, each covering the tasks that suit its strengths.
Turn the explanation into a project decision
Record your assumptions, unknowns and acceptance checks in the matching tool. Use the service guides to discuss delivery and scope.
Open the preparation tool ↗Questions about this topic
Can a chatbot perform actions?
Yes. A conversational interface can connect to functions that read or change records. The interface therefore reveals little about permissions. Ask which operations exist, how they are checked and how employees distinguish a proposal from an operation that actually completed.
Is an AI agent always more expensive?
No. However, a flexible task involving many tools usually creates more combinations to evaluate. Compare the complete scope, source preparation, review and maintenance. A small agent task can be simpler than a large chatbot covering multiple channels and departments.
Should we replace our existing form?
Not necessarily. A form may remain the clearest route for predictable requests. An assistant can offer explanations or identify missing information alongside it. Compare usability and completeness before replacing a familiar route that already serves customers well.
Continue with a related question
What does an AI agent cost? Budget for delivery and operation
Build a realistic AI-agent budget covering scope, integrations, evaluation, usage, human review and maintenance, without invented price promises.
AI agents & chatbotsWhich permissions should an AI agent receive?
Design bounded AI-agent permissions for reading, proposing and executing, with approval boundaries, practical access checks and recovery examples.
AI agents & chatbotsTesting an AI agent: turn fluent answers into useful evidence
Build a practical AI-agent test set covering sources, actions, exceptions, handover and repeatable acceptance criteria.
n8n & process automationWhich business process should you automate first?
Choose a first automation using evidence about volume, handovers, exceptions and useful outcomes. Includes a worked selection example.
Sources and technical background
- OpenAI: Function calling ↗
Tool calls are proposals processed by application code; the application remains responsible for actual execution.
- OpenAI: Evaluation best practices ↗
Task-specific evaluation, representative cases, human calibration and continued regression testing.