Author: Nate Reuck
Nate Reuck is a Senior SRE and Incident Management leader with deep experience operating large-scale cloud platforms and distributed systems. He specializes in reliability engineering, incident response, on-call operations, and building durable operating models that scale. Nate's focus is reducing toil, improving MTTR, and turning incidents into repeatable learning through strong runbooks, automation, and clear ownership. He works closely with engineering, product, and partner teams to align reliability with real business outcomes, and believes strong systems, clear decision paths, and empowered teams win over heroics. Nate is also an author, builder, and lifelong learner with a passion for technology, systems thinking, and continuous improvement.
Most teams are not measuring detection. They are measuring when someone finally reacts. That gap is where outages grow teeth. Here is how to fix it.
AI agents are acting in production. Learn the new failure modes and the Agent SRE operating model: guardrails, decision tracing, semantic incidents.
OpenTelemetry unifies traces, metrics, and logs into a single vendor-neutral standard. Learn what it is, how it evolved, and why it fundamentally changes how AIOps and SRE teams observe and operate distributed systems.
Most organizations have both SRE and Platform Engineering but cannot clearly explain where one ends and the other begins. This is not a naming problem. It is an ownership problem. Here is where the line actually is.
Postmortems don’t prevent incidents from repeating. A risk registry does. Learn how to shift from tracking action items to managing failure modes with a structured, scoreable, and always-active reliability system.
Why AI token usage matters for AIOps and SRE teams. Tokens determine cost, latency, and system limits in every production AI workflow — yet most teams only discover this after things break.
A practical way to use the 5 Whys in postmortems without turning it into blame or a satisfying story. Keep answers mechanistic, branch when the system branches, and end in controls you can implement.
How to use Google NotebookLM for AIOps and SRE without roulette prompts: build source-bound incident dossiers, decision memos, and postmortem gap checks that improve reliability.
AI reliability is constrained by physics, not software AI systems are starting to miss SLOs for reasons your cluster cannot explain. You can have clean deploys, stable error rates, and a model server that never goes down, while tail latency drifts upward and throughput softens. The platform looks healthy because the software is healthy. The service is not. When that happens on dense GPU fleets, the cause is often not orchestration. It is constraint binding. Power limits, thermal headroom, and energy volatility are now first-order reliability dependencies. If your reliability practice stops at the cluster boundary, you are treating symptoms…
Claude Opus 4.6 is an unusually relevant model release for operators. Anthropic is not just claiming higher benchmark scores. They are emphasizing longer agentic work, more careful planning, better reliability in large codebases, and a 1M token context window in beta. They also shipped the controls you actually need if you want to run an agent for more than a short chat: effort levels, adaptive thinking, and context compaction. This is the kind of upgrade that can reduce real on-call load, but only if you evaluate it like an SRE evaluates any new control surface. Do not ask whether it…