Adding an assistant to an existing product looks like a self-contained feature and behaves like a cross-cutting one. The model is the easy part - an API call. What takes the time is everything around it: what the assistant is allowed to see, what it is allowed to do, how it gets enough context to be useful, and what happens when it is confidently wrong in front of a paying customer.
Permissions come first and they are not negotiable. The assistant must operate strictly as the user who invoked it, never with elevated service credentials. This sounds obvious and is violated constantly, because passing the user's identity through to every tool the assistant calls is more work than giving it broad access. The failure mode is severe: an assistant that can read records its user cannot is a data breach with a friendly interface, and in a multi-tenant product it is a cross-customer breach.
Context is the difference between something useful and something generic. An assistant that only sees the user's typed question produces answers the user could have got from a search engine. One that knows which screen they are on, which record they are viewing, their role and their recent actions can answer the question they actually have. Build that context assembly deliberately, and keep it minimal - every irrelevant thing you include costs tokens, adds latency, and gives the model more opportunity to fixate on the wrong detail.
Then decide what the assistant may do rather than say. Read-only assistants are straightforward. The moment it can change state - update a record, send a message, issue a refund - you need an explicit action layer with its own permission checks, a confirmation step showing the user exactly what will happen, and an audit trail recording that the action came from the assistant on this user's behalf. Never let a model call a mutating endpoint directly because it decided to.
Design the refusal path before the happy path. The assistant will be asked things outside its knowledge, and the default behaviour of a language model is to produce something plausible rather than to decline. Grounding answers in retrieved material, showing the source alongside the answer, and returning an honest I do not have that information are what make users trust the feature over time. One confidently invented answer costs more credibility than ten good ones earn.
Finally, instrument it from day one - not just usage, but quality. Log every interaction with its retrieved context and its response, and give users a way to flag bad answers. That flagged set becomes your evaluation suite, and without one you cannot tell whether next month's prompt change improved anything. Teams that ship the assistant and add measurement later spend months unable to answer whether it is getting better.
