Change Request Analysis AI Agent for Utility Companies

A production-grade agent should make routine preparation faster while leaving consequential judgment with the right person. For utility companies, this matters in asset, supplier, production, maintenance, inventory and shipment workflows where operational accuracy and timing matter. 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. Start read-only where possible, use allow-listed data sources and require approval before any external message or record change. 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 utility companies. 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.