Automated pricing systems often operate as a black box, creating compliance and trust gaps. This agentic workflow inserts a rationale-generation step after each pricing decision. An LLM-based agent, prompted with the decision's context—input signals, model scores, and applied business rules—produces a concise, human-readable explanation. This rationale is logged with the price change in systems like Snowflake or Databricks and can be routed to dashboards or approval queues. The operational upside is faster incident diagnosis, smoother auditor reviews, and the ability to scale pricing automation without losing managerial control.




