Begin with a specific responsibility
An AI agent is useful when it has a defined job, the information needed to perform that job, and clear boundaries. 'Add AI to the business' is not a product requirement. 'Help staff prepare a first draft from approved internal information and route it for review' is much closer.
The strongest candidates are repeated workflows where the expected input, useful output, permitted tools, and review path can be described. If the process is not understood, automation usually makes the uncertainty faster rather than making the work better.
A chatbot and an agent are not always the same thing
A public chatbot may answer questions on a website. An agent may work inside a portal, prepare research, classify requests, retrieve approved knowledge, or coordinate a defined step in a larger application. The right interface depends on who is doing the work and what decision comes next.
Do not choose a conversational interface just because it looks familiar. Sometimes a structured form, review queue, or guided workflow is faster, safer, and easier to evaluate.
Decide where human control is required
Sensitive, irreversible, financial, legal, or customer-facing actions may require approval or a clear escalation path. Permissions should be limited to what the responsibility needs, and the product should make it visible when a person must review an output.
Human control is not a failure of automation. It is part of responsible product design, especially when context is incomplete or the cost of a wrong action is high.
Evaluate the workflow with real scenarios
A demonstration can show possibility, but a production decision needs representative tests. Use normal cases, ambiguous requests, missing information, conflicting instructions, unsafe requests, and service failures. Define what a good response looks like before judging the system.
Track useful measures such as completion quality, review effort, escalation rate, failure recovery, and time saved. Avoid claiming perfect accuracy or autonomous performance that has not been verified in the actual operating environment.
When to wait
An AI agent may be premature when the source information is unreliable, the process changes every week, nobody owns the outcome, or the business cannot support review and maintenance. Improving the workflow or building the web application around it may be the better first move.
A short discovery phase should end with a clear recommendation: build an agent, improve the underlying system first, or leave the task with people. The technology should follow the responsibility, not the other way around.