Change Request Analysis AI Agent for Banks

The workflow should earn additional authority through measured performance rather than receiving broad access on day one. For banks, this matters in data-intensive, regulated workflows involving money movement, risk, reporting and customer documentation. A change request analysis role can organize scope, dependency, test and rollback information before human approval. The target output is a change review 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. Keep credentials customer-owned, scope tool access to the minimum needed and record the evidence used for each important output. 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 change request analysis work in banks. Run the same examples through the manual process and the proposed agent-assisted process. Measure review completeness, missing-risk detection and preparation 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.