Operations | Monitoring | ITSM | DevOps | Cloud

It has become common knowledge that the nature of software development has changed as AI-code generation and agent-based features gain adoption. In perhaps a more subtle shift, the fundamentals of software instrumentation are changing too. As OpenTelemetry becomes the standard instrumentation layer across enterprises, with thousands of developers (many from Honeycomb) actively contributing to it, the nature of the telemetry data captured itself is evolving to meet the growing demand for rich context.

You might expect an AI-SRE agent to target 100% reliable services, ones that never fail. It turns out that past a certain point, however, increasing reliability is worse for a service (and its users) rather than better! Extreme reliability comes at a non-linear cost: maximizing stability limits how fast new features can be developed, dramatically increases the operational cost, and reduces the features a team can afford to offer.

Dashboards provide a useful prism through which we can study the broader evolution of the IT professional’s role in the era of agentic AI. For years, dashboards have been the centerpiece of IT work, serving as the interface where teams interpret system behavior, diagnose issues, and plan actions. Dashboards epitomize the relationship between humans and their systems: humans observe, interpret, and act. As agentic AI enters the picture, that relationship begins to change. Let’s explore how.

As Large Language Models (LLMs) evolve from simple chatbots into agentic workflows, the need for a standardized way to connect them to external data and infrastructure has become critical. In a recent workshop hosted by Nir Adler, Innovation Engineer at Komodor, we explored how to bridge this gap using the Model Context Protocol (MCP).

AI assistants are quickly becoming a primary interface for how people interact with software. Developers ask them how to integrate APIs. Users ask them how products work. Buyers ask them how tools compare. Increasingly, the first explanation someone receives about your product does not come from your website, your documentation, or your sales team. It comes from an AI assistant. That shift has an important consequence that many organizations are only starting to notice.

Here’s the uncomfortable truth: most companies do not have an AI problem. They have a delivery problem wearing an AI costume. MIT’s Project NANDA research has been widely cited for a brutal headline statistic: roughly 95% of corporate generative AI pilots fail to produce measurable business impact or returns, while only about 5% break through to meaningful outcomes. (Yahoo Finance) The models are impressive. The demos are dazzling. The budgets are real.

AI is the new frontier for FinOps maturity. It introduces fresh spend patterns and new opportunities for value. As GPUs, inference, and retraining reshape costs, FinOps maturity grows through visibility, forecasting, and shared mindset about how these workloads drive business impact. In this 2025 post, I gave my guidelines for implementing AI tagging to give business context and clarity to vague AI invoices. Now, I’m sharing the next level up: how to drive FinOps in AI with AI.