Why AI governance fails at scale, and how MCP enables real-time coordination between FinOps, platform, and policy to control AI costs at speed.

Agentic AI shifts FinOps from reporting cloud costs to governing autonomous systems. This article explores what changes, why it matters, and how to adapt.

Traditional FinOps relies on delayed billing data. AI workloads evolve in minutes. This gap is where costs explode. In this post, I explain why agentic FinOps requires a real-time runtime, not better dashboards. Using MCP (Model Context Protocol), FinOps agents can query live cost, usage, and policy data across clouds, detect anomalies as they happen, and act before waste becomes sunk cost. MCP is not hype. It is the missing plumbing for scalable FinOps in the age of AI.

Learn how agentic AI and Model Context Protocol (MCP) are transforming cloud FinOps from manual cost reporting to autonomous optimization. This hands-on demo shows AI agents analyzing AWS billing data, detecting anomalies, and generating executive dashboards in minutes. Discover practical architectures using Claude, Amazon Bedrock, and AWS Cost Explorer for enterprise-grade FinOps automation with built-in security and audit capabilities.

In this blog post, explore the synergy between cloud optimization and FinOps, uncovering strategies to enhance efficiency, reduce costs, and drive sustainable IT practices for businesses seeking to maximize their cloud operations.

AI Agents & MCP in cloud Financial Management and CloudOps: Beyond the Hype