After You Build an AI Assistant

What should your assistant actually do?

Describe the task, the information it uses, and the result someone needs. Preparing a draft and sending it are different responsibilities. Reading a record and changing it are different permissions.

Keep human approval where a mistake could have meaningful consequences. An assistant should not receive broad access merely because it might be convenient.

• What information can it use?
• What does an acceptable result look like?
• Which actions require a person to approve them?

What needs maintaining after it works?

Review examples from real use. Instructions that worked for an early demo may not cover a new customer request, changed policy, or missing information. Connected services and AI models can also change.

• Check useful results and unexpected answers.
• Review instructions when the task or business rules change.
• Test connections and access when tools change.
• Compare representative examples before switching models.
• Keep a fallback when the assistant cannot finish the task.

Should an AI assistant run locally or in the cloud?

Local AI can give you more control over the environment; cloud services may offer capabilities or convenience your task needs. Compare the work, data handling, cost, hardware, and maintenance rather than assuming one option always wins.

Local is not automatically private or secure. Access, storage, connected tools, and the way the assistant is used still matter.

Can someone maintain an assistant you built yourself?

Yes, provided the setup can be reviewed and the required access and support are agreed. Start with what it does now, examples of good and poor results, and the task you want it to keep handling.

Your existing builder or team may already provide that care. Additional management is useful when the assistant needs more attention than they can give it, not because it must be rebuilt.