An enquiry arrives, a contact is recorded, a booking is confirmed and a task is assigned. These are predictable steps. A deterministic workflow can make the sequence explicit, check its inputs and show where it failed. Adding an AI agent does not automatically make that process better.
Reasoning can be useful when the task needs interpretation: making sense of an unstructured brief, comparing information from several sources or drafting a response that a person will review. The first question is whether that judgement creates enough value to justify the extra uncertainty.
The responsibilities should stay clear. A CRM owns the contact and customer journey. A workflow tool coordinates defined steps. An AI assistant or agent performs a bounded task using the data and tools it is allowed to access. A person retains the decisions that require approval.
The design should include failure before the first successful demonstration. What happens if a provider is unavailable, a tool returns incomplete information or an instruction conflicts with the business rules? Expected behaviour, fallback and handover need to be specified and tested.
For a first engagement, choose one process and define its inputs, outputs, owner and approval boundary. A useful result may be a workflow, an assistant, a combination of both or a simpler change to the existing system.