Operations | Monitoring | ITSM | DevOps | Cloud

Not long ago, AI was largely experimental, but today it’s a strategic imperative. Enterprise AI adoption has surged to record levels, with over three-quarters of organizations now using AI in at least one function . In Deloitte’s latest global study, almost all organizations reported measurable ROI from AI, and 74% said their most advanced AI initiatives meet or exceed ROI expectations .

Seems like you can’t throw a rock without hitting an announcement about a Model Context Protocol server release from your favorite application or developer tool. While I could just write a couple hundred words about the Honeycomb MCP server, I’d rather walk you through the experience of building it, some of the challenges and successes we’ve seen while building and using it, and talk through what’s next. It should be pretty exciting, so strap in!

Traditional monitoring approaches have served IT operations for decades, providing basic visibility into system health through predefined metrics and thresholds. However, these conventional methods face significant limitations when confronted with modern, complex environments: Static Thresholds and Rules Traditional monitoring relies heavily on manually defined thresholds and rules.

If 2023 was the year AI entered the enterprise conversation and 2024 was the year of AI overhype, 2025 is the year it takes action. “Agentic AI” has quickly become the banner term for next-gen systems that aren’t limited to generating responses—they operate, decide, and resolve. The shift from passive chatbots to autonomous agents is underway, and for IT operations teams, the implications are massive.

In the AI world, there’s a lot of buzz about creating custom large language models (LLMs) tailored for specific domains, perhaps for better security, context, expertise, or accuracy. It’s an appealing idea: What better way to solve your niche challenges than with a bespoke AI designed just for you? But here’s the thing — building a great LLM isn’t just challenging; it’s prohibitively expensive and resource-intensive.

AI can be a transformative tool in network operations — but only when it’s tied to clear, measurable outcomes. Rather than chasing hype, IT and NetOps teams should focus on solving specific operational challenges like reducing MTTR, cutting costs, and stabilizing infrastructure. AI has real potential when strategically applied, and when aligned with business goals, it becomes a powerful ally in modern network operations.

Written by author, adapted by AI GitKraken Desktop 11.0 is here, and it’s more than just a version bump. We’re introducing AI-powered features designed to accompany your workflow and help you stay focused on what matters most. It’s changed how I approach commits, and I’m sure it will help y’all too!

It started with promise: deploy a chatbot, cut service desk costs, and deliver instant support to employees anytime, anywhere. And many large organizations bought in. But several years into this so-called “chatbot revolution,” the results tell a different story—one of inflated expectations and underwhelming outcomes. It’s time to face a hard truth: AI that only talks is no longer enough.

We’re excited to share that Honeycomb has completed our first-ever acquisition: we’re joining forces with Grit, bringing on board not only a strong team, but also compelling technology that supercharges our ability to deliver on our mission: to bring observability to every software engineer. This is a strategic move that will help us deepen the value we deliver to customers and accelerate our vision for what modern observability can and should be.