Operations | Monitoring | ITSM | DevOps | Cloud

Runbooks are rarely missing because teams don't value them. They're usually missing because incident response, follow-up, and platform work compete for the same limited time. By the time an issue is resolved, the knowledge is fresh, but the window to document it is already closing. That gap creates familiar failure modes: over-reliance on senior engineers, slower handoffs, and less confidence for whoever is on call next.

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

Over the last year, I’ve talked to dozens of SRE teams about AI. The excitement is real, but conversations hit a wall when we get to production reality. How does an agent manage complex context without losing the plot? How does it avoid hallucinating relationships between signals? Who owns the orchestration logic that ties it all together? We realized the bottleneck wasn’t model intelligence. It was the lack of a reliable logic layer between the data and the model.

Grok structures logs. Context engineering connects systems. AI explains behavior. For years, Grok patterns have been the workhorse of the SRE world. Built on regular expressions, Grok helps teams extract structure from unstructured logs. As we explored in "Do You Grok It?", Grok is the key to turning messy log lines into usable fields. It's why our Grok Pattern Reference remains one of our most-visited resources — SREs are hungry for structure.

As budgets reset for 2026, engineering leaders are making a resolution: no more vendor lock-in. Here’s how to keep that promise by building on the technical foundations of data reliability and simplified collection. It’s January 2026, and if you’re like most engineering leaders, you’re staring at your observability vendor contracts with a mix of frustration and resignation.

A note from Lauren Nagel, Mezmo's VP of Product: At Mezmo, we believe the best observability tools aren't just built for users, they're built with them. Since the launch of Mezmo's AI SRE agent, we've listened and learned from our customers. The feedback and insights have been invaluable in helping our teams refine and enhance the experience. Today, we're excited to share our latest release, packed with improvements and powerful new capabilities that make our AI SRE even faster and more intuitive.

This is blog 2 in our New Year, New Resolution Series on OTel migrations. Read the first post, "New Year, New Telemetry: Resolve to Stop Breaking Dashboards", here. Most New Year’s resolutions fail because they require a "big bang" change. If your 2026 mandate is to migrate to OpenTelemetry (OTel), the traditional approach is the definition of friction.

It's 2026. Your New Year's resolution was to finally migrate to OpenTelemetry. But you're staring at dozens of dashboards that depend on your current data format, and that migration deadline is looming... Sound familiar? If you're an SRE or Platform Engineer facing a top-down OTel mandate, you're not alone. The challenge isn't just about adopting a new standard—it's about doing so without disrupting the observability systems your team depends on every day.

By Bill Balnave, VP of Customer Success at Mezmo The core promise of modern observability is simple: cut Mean Time To Resolution (MTTR). Yet, despite a boom in tooling and investment over the last four years, the data tells a sobering story: our industry is actually getting worse at finding and resolving issues. Dashboards, once our trusted guide, have become the starting point for a chaotic "dashboard hunt" that rarely leads to the definitive root cause.

For SREs juggling multiple services, third-party dependencies, and constant alerts, a critical service slowdown can quickly turn into chaos. APM Dashboards may show everything is fine, yet users are still experiencing problems. That gap—between application telemetry and real-world performance—can turn a five-minute fix into a two-hour war room. ‍