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Agentic FinOps

Diagram showing WasteLine architecture, with an AI coding agent connecting to CLI and MCP server, then 49 deterministic rules producing findings and scripted remediation

WasteLine: AWS waste detection built around your AI coding agent

AWS cloud waste detection and remediation through your AI coding agent. 49 deterministic rules. Free tier, no SaaS connector. Built by OptimNow.

4 mai

From Chatbot to Agent: What Real AI Architecture Looks Like

Most internal AI agents fail because they are just ChatGPT wrappers. Learn the four architectural pillars required for production AI agents.

15 mars

A conceptual architecture illustration showing MCP as a real-time coordination layer connecting policy engines, live cost data, and infrastructure state.

Why MCP is the pivot for cost and policy governance

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

11 janv.

a transition from ungoverned AI activity to controlled agentic FinOps.

The Dawn of Agentic FinOps

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

2 janv.

Le Problème du Temps Réel

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.

18 déc. 2025

iceberg, where only a small visible tip represents ‘reported AI costs’ and a large, complex structure beneath the surface represents ‘unseen AI behaviour and shadow AI usage.

Why AI costs management require Real-Time Intelligence, not another dashboard

What you'll learn: Why traditional dashboards miss 90% of AI cost activity, or how real-time intelligence and Agentic FinOps are changing the competitive landscape. Part of the FinOps for AI series. → Start here: The Dawn of Agentic FinOps ← Previous post: Why traditional FinOps breaks with AI workloads: a story of costs, tokens, and fat tails Most dashboards show you where the money went Your AWS Cost Explorer displays $47,000 in AI spending last month. The chart shows a 35%

15 déc. 2025

On the left, a steady, smooth cloud cost line resembling predictable server usage. On the right, a jagged, volatile line representing AI token costs.

Why traditional FinOps breaks with AI workloads: a story of costs, tokens, and fat tails

AI workloads break traditional FinOps because they generate volatile, real-time costs. Learn how to adapt your practice with allocation-first, real-time ingestion, and business-level unit economics.

9 déc. 2025

AI agent architecture diagram showing MCP protocol connecting Claude LLM to AWS Cost Explorer for automated FinOps analysis and cloud cost optimization

How AI Agents and MCP Are Transforming Cloud FinOps

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.

21 nov. 2025

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