Operations | Monitoring | ITSM | DevOps | Cloud

On paper, lift-and-shift VM migration to Kubernetes sounds simple. Compute can be moved. Storage can be remapped. But many migration projects stall at the network boundary. VM workloads are often tied to IP addresses, network segments, firewall rules, and routing models that already exist in the wider environment. That is where lift-and-shift becomes much harder than it first appears.

How platform and SRE teams are using Mezmo's open-core agent framework — with any LLM, any tools, any observability backend.

Elixir’s “let it crash” philosophy is one of the best ideas in modern software design. Supervisors restart failed processes, the system self-heals, and life goes on. It’s like having a really good immune system. The problem is that a really good immune system can also hide chronic conditions. A GenServer crashing and restarting is working as designed.

Here’s a scenario most IT teams know too well: a single error message lights up the monitoring dashboard at 2 a.m. Within seconds, calls are coming in from customers. Within minutes, the revenue meter is running. If your team is still figuring out who owns the incident while that meter ticks, you’ve already lost precious time. According to 2024 EMA Research, unplanned IT downtime now costs organizations an average of $14,056 per minute, rising to $23,750 per minute for large enterprises.

Seer is our AI agent that takes bugs and uses all of the context Sentry has to find the root cause and suggest a fix. We use it all the time to help us improve Sentry. Seer fixes Sentry. More recently, Seer has been helping us fix itself — Seer fixing Seer. An upstream outage triggered a bit of an avalanche, revealing a bug that had been hiding away for months. When it came time to fix it, Seer pointed us exactly where we needed to look.

Today, we’re excited to introduce the beta release of MicroCloud Cluster Manager, a new way to discover, organize, and operate your MicroCloud environments from a single, unified interface. MicroCloud is an open source cloud platform that makes it simple to create lightweight, resilient clusters anywhere. As teams scale from one cluster to many, visibility and coordination quickly become essential. Cluster Manager is built to solve exactly that.

Teams running modern infrastructure can’t afford slow incident response. On-call management software ensures the right person is alerted instantly, incidents are escalated intelligently, and downtime is minimized. This guide breaks down the best on-call management software for 2026, helping teams choose the right platform based on their specific use case, response requirements, and operational complexity.

Moving a large language model (LLM) application from a demo to a production‑scale service raises very different questions than the ones you ask when playing with an API key in a notebook. In production, you have to answer: How much is each model costing us? Are we keeping latency within our service‑level objectives? Are we accidentally returning hallucinations or toxic content? Is the system vulnerable to prompt‑injection attacks?

In another post in this series, we discussed how to instrument large language model (LLM) calls. This can be a good starting point, but generative AI workloads increasingly rely on agents, which are systems that plan, call tools, reason, and act autonomously. And their non‑deterministic behavior makes incidents harder to diagnose, in part, because the same prompt can trigger different tool sequences and costs.

Large language models don’t work in a vacuum. They often rely on Model Context Protocol (MCP) servers to fetch additional context from external tools or data sources. MCP provides a standard way for AI agents to talk to tool servers, but this extra layer introduces complexity. Without visibility, an MCP server becomes a black box: you send a request and hope a tool answers. When something breaks, it’s hard to tell if the agent, the server or the downstream API failed.