Operations | Monitoring | ITSM | DevOps | Cloud

Organizations across industries are accelerating their investments in AI for operations, yet the path to meaningful impact is proving far more complex than early expectations suggested. Analysts at Gartner, Forrester, Deloitte, and McKinsey continue to highlight the same structural barrier. AI cannot produce accurate predictions or safe automation when the operational data feeding it is fragmented, incomplete, or inconsistent.

Since releasing our hosted MCP server last year, we've been thrilled to see customers not just adopt it but build Honeycomb deeply into their agentic development and observability workflows. Users have embraced it, leveraging Honeycomb to stay in conversation with their code and understand how it runs in production.

I semi-recently bought a used Roborock S5 Max robot vacuum, and installed Valetudo on it. The installation process involves rooting the robot, and gaining SSH access to it. Which got me thinking, could I get the robot to ping Healthchecks.io at regular intervals? When the robot runs into a problem (closes a door after itself, gets stuck, chokes on a loose wire) and cannot return to the base, it eventually shuts down.

While AI have usage has been growing steadily for the last several years, the LLM models noticeably improved around the end of 2025. Specifically, they become more viable for software development. We are seeing the results. The feature and product delivery has picked up. One way to visualize this is by looking at the number of pull requests for your organization / software development teams. This chart shows the number of Github pull requests created by a team. Can you spot when AI usage increased?

MRO stands for maintenance, repair, and operations. It refers to the activities, supplies, and services that keep equipment, facilities, and infrastructure running safely and efficiently. Every industry that relies on physical assets depends on MRO, whether that means replacing a worn bearing on a production line, restocking safety gloves in a warehouse, or servicing an HVAC system in a hospital.

APIs sit at the core of modern digital infrastructure. From e-commerce checkouts and payment processing to SaaS platforms and mobile applications, APIs move the data that keeps systems running. But APIs do not operate as a single unit. They are made up of individual endpoints, and each endpoint represents a specific function or resource that users depend on. As organizations shift toward microservices, cloud native applications, and third party integrations, the number of endpoints increases rapidly.

APIs power modern applications. Every login request, product search, payment authorization, and mobile app refresh depends on an API responding quickly and reliably. When latency increases, users feel it immediately. Pages stall. Transactions hang. Confidence drops. Most engineering teams measure API latency. Fewer truly monitor it. There is a difference. Many teams track average latency in dashboards and assume performance is healthy.