Operations | Monitoring | ITSM | DevOps | Cloud

The infrastructure management landscape is undergoing a fundamental transformation. Infrastructure as Code has already revolutionized how we provision and manage cloud resources by treating infrastructure as software. The next evolutionary step involves intelligent automation that can understand, adapt, and optimize these configurations independently.

ITIL 5 launched in January 2026, and for the first time in the framework's 40-year history, AI governance is front and center. If you're running incident management, on-call rotations, or building operational tooling, this matters: the gap between AI adoption and AI governance is about to become a compliance and operational risk issue. I’m not usually a big ITIL fan, but this guidance has some genuinely useful framing and questions.

For much of the past decade, many of the most ambitious ideas in artificial intelligence lived primarily in research papers, labs, and long-term roadmaps. Agentic AI was no exception. The concept of AI systems capable of reasoning, planning, and acting autonomously was widely discussed but largely theoretical. But earlier this month, Gartner released its report The Future of NetOps Is Agentic, reflecting a growing consensus that this has changed. What was once conceptual is now becoming operational.

Financial services AI demands more than models and prompts. Context engineering provides real-time, governed, and explainable intelligence with Elastic serving as the foundational context layer. Artificial intelligence in financial services is no longer constrained by model capability. The real bottleneck is context.

The inevitable happened. A while back, Gartner projected that in 2026, 30–50% of all new SaaS product features would use LLM inference. That meant OpenAI-style costs would become a standard part of SaaS COGS. Today, OpenAI has become one of the most operationally significant line items for SaaS companies. But for many teams, this creates an uncomfortable gap. Engineering sees OpenAI as a fast path to innovation.

InfluxDB 3 Model Context Protocol (MCP) server lets you manage and query InfluxDB 3 (Core, Enterprise, Dedicated, Serverless, Clustered) using natural language through popular LLM tools like Claude Desktop, ChatGPT Desktop, and other MCP-compatible agents. The setup is straightforward. In this article, we will focus on setting up InfluxDB 3 Enterprise using Docker with Claude Desktop.

This article is part of our series on how Datadog engineering teams use LLM Observability to build, monitor, and improve AI-powered systems. At Datadog, we’re always looking for ways to make complex data easier to explore.