Handover Preparation AI Agent for Wealth Management Firms

The workflow should earn additional authority through measured performance rather than receiving broad access on day one. For wealth management firms, this matters in data-intensive, regulated workflows involving money movement, risk, reporting and customer documentation. A handover preparation role can compile current status, dependencies, risks and next actions for a role or project handover. The target output is a handover pack, not an opaque autonomous decision.

The operating boundary should be written before tools are connected. Specify the allowed sources, required fields, freshness expectations, acceptance criteria and the person who owns exceptions. Use explicit acceptance criteria, representative test cases and a rollback path before moving from assisted work to unattended execution. This makes the same workflow easier to test, audit and improve because failures can be traced to a source, instruction, permission or model behavior.

Build a small benchmark from real, permission-safe examples of handover preparation work in wealth management firms. Run the same examples through the manual process and the proposed agent-assisted process. Measure missing-context rate, reviewer acceptance and handover time. Add human correction rate, exception rate, latency and total cost per accepted output so a faster workflow is not mistaken for a better one when quality falls.

Keep a holdout set for later changes to instructions, tools or models. A change should be promoted only if it improves the outcomes that matter without creating unacceptable safety, compliance or customer-experience regressions. This turns model selection into an operating decision based on evidence rather than a one-time product preference.