Domain experts do not trust AI because it sounds confident. They trust it when the product respects how their work actually happens.
That means the agent has to be legible. It should show what it used, what it assumed, where uncertainty remains, and what action it recommends. It should know when to ask for clarification. It should make correction easy.
Trust is designed into the workflow
A trustworthy agent is not a floating chat box. It belongs inside a specific job. The interface should expose source material, decision points, approval steps, and consequences. The expert should be able to inspect the system without becoming a prompt engineer.
When the product is grounded in the workflow, trust has a place to attach.
Context must be tight
Experts are quick to notice irrelevant context. If a system cites the wrong policy, uses stale data, or ignores a known exception, the relationship is damaged. More context is not always better. The right context, ranked and constrained by the job, is what matters.
We design agents to retrieve narrowly, reason visibly, and fail in ways the user can understand.
The expert stays in the loop
The goal is not to remove the expert. The goal is to give the expert leverage. In many valuable domains, judgment is the product. AI should handle the repetitive assembly work, surface patterns, draft options, and remember context so the human can make better decisions faster.
Trust grows when the system makes the expert stronger instead of pretending the expert is obsolete.
