Designing AI features starts with a specific user task and clear boundaries. Adding a chat box is easy to describe; deciding what the system may do, when it must ask and how someone recovers from an error takes more thought.

Our recommendation is to begin with a narrow workflow that people can understand and review. Treat the examples below as design scenarios, not claims about deployed Contrivea client systems.

Choose a task before choosing an interface

Describe the workflow in one sentence: who needs to do what, using which information? For example, a support assistant might help an operator draft a response using approved help articles. That is more testable than “an intelligent assistant for everything”.

Then compare the AI option with a search field, template or conventional form. If a predictable filter solves the task well, an AI interaction may introduce avoidable uncertainty. Use AI where interpreting varied requests or drafting material provides a clear benefit.

Separate suggestions from actions

Showing a suggested response is different from sending it to a customer. Present that difference in the interface. A review screen should show the recipient, proposed message and any information being shared before an operator chooses to send.

For a hypothetical project-management assistant, “Suggest a revised deadline” and “Change the deadline” should be separate permissions. A useful starting point is a preview of the proposed change, followed by an explicit confirmation. Avoid labels that make a suggestion look like a completed action.

Make the system’s limits visible

  • Information: explain which sources the feature uses and whether those sources may be incomplete.
  • Access: tell users which account or workspace is connected and what the feature is allowed to change.
  • Progress: distinguish between preparing a result, waiting for approval, completing an action and failing.
  • Review: show the evidence or source material people need to assess an answer where appropriate.

Do not present confident wording as evidence that an answer is correct. If the system cannot complete a task reliably, it should explain the limitation and offer a usable next step.

Design the recovery path

Consider what happens when a request is ambiguous, a service is unavailable or the output is wrong. Can the person edit the result, retry a step or continue manually? For reversible changes, explain how to undo them. For consequential actions, define approval and escalation rules before connecting the feature to live systems.

These decisions need technical enforcement as well as clear screens. A disabled-looking button is not a substitute for checking permissions on the server. The NIST AI Risk Management Framework is a useful reference when planning a broader assessment of AI risks and responsibilities.

Prototype realistic states

A polished demonstration often shows only a successful answer. A useful prototype also covers an empty request, a confusing request, a slow response, missing permissions and an incorrect suggestion. Review those states with the people expected to operate the feature.

Our Targe studio project shows a workspace interface that can serve as context for discussing tasks and approvals. It is not evidence of an autonomous assistant deployment. Keep the distinction between a proposed feature, a prototype and a live capability clear in both the product and its marketing.

Agree the evaluation before launch

Define what a correct result looks like, who reviews it and which failures would stop a release. Useful measures might include task completion, correction effort and the frequency of inappropriate proposed actions. The right measures depend on the use case; a visually impressive demo is not enough.

Explore our product design and prototyping service or AI and automation service. We can begin with the workflow and decide what is useful to prototype before committing to a wider build.

Contrivea studio notes · Updated 17 October 2026. About the studio.