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 meet agents as a user interface first. A chat box that can open a ticket, fetch a dashboard, or run a command. It looks like magic until the first time it touches production and nobody can explain what changed, why it changed, and whether the change helped. That is the moment the conversation needs to shift from models to skills. A skill is not a prompt. A skill is an operational capability with a contract. It has defined inputs, explicit tool access, guardrails, verification, stop rules, and an audit trail. Skills are the execution layer that connects AIOps…
Most teams meet AI agents as a UI trick first: a chat box that can run commands, open tickets, or change a dashboard state. It looks like magic until the first time it touches production and leaves you asking a new question: did the system change because the incident evolved, or because the agent did something plausible? SREs should treat agents differently. An agent is not a feature. It is a control loop with permission to change the world. AIOps has historically been about perception. It helps you notice, cluster, rank, and summarize. It shortens the search for a hypothesis.…
A production system rarely fails all at once. It fails by shifting constraints. On-call fails the same way. People do not suddenly burn out. The system quietly moves from sustainable to brittle, and you only notice when performance drops, mistakes increase, or the team starts bleeding experienced operators. If you treat on-call as a personal stamina problem, you will keep hiring tougher humans for a system that keeps getting worse. If you treat it as a system, you can measure it, model it, and change its inputs until it behaves. The central idea is simple. On-call load is demand arriving…
The first week after the AIOps rollout, paging felt better. The second week it felt haunted. Start here: More in AIOps. Alert volume dropped, but the remaining alerts were stranger. The model grouped symptoms differently than humans did. Escalations happened later, not earlier. By the time the on-call committed to a decision, the system had already moved on. The loop got quieter. The loop also got slower. AIOps does not usually fail because it is inaccurate. It fails because teams give it authority without boundaries and then measure the wrong thing. They celebrate fewer pages while decision latency rises, and…
The freeze decision was made twice. Once in the incident channel, and again in the executive debrief. The second one is the one that damaged the team. You recovered service. You did not recover trust. Product heard “operations is blocking delivery.” Operations heard “engineering keeps shipping risk into a service that is already bleeding.” Everyone had receipts. Nobody had a shared rule for what happens when reliability is already in deficit. Error budgets were supposed to be that rule. In most orgs they are not. They are a chart that gets screenshotted when it is convenient. IN THIS ARTICLE Table…
SRE Incident Assistant: A Complete Reference Executive Summary: The SRE Incident Assistant centralizes incident response by integrating Slack, Jira, Confluence, PagerDuty, and optionally Robusta or Prometheus (for alerting). This guide covers setup, best practices (like ephemeral Slack war rooms, slash commands, runbook automation), and postmortems. IN THIS ARTICLE Table of Contents Toggle 1. Prerequisites & Overview2. Architecture & Key Components3. Environment Requirements4. Quick-Start Flow5. Installation & Setup5.1 Build or Pull the Assistant5.2 Create a Configuration File5.3 Deploy the Assistant5.4 Validate Logs & Health6. Dev/Staging Environment Strategy7. Installing & Integrating Robusta (Optional)8. Integrating with Existing Tools9. Slack: War Rooms & Slash…
In today’s fast-paced digital landscape, achieving perfect observability isn’t just desirable—it’s essential. Enter Grafana, the visualization powerhouse that has revolutionized how Site Reliability Engineers (SREs) monitor and maintain systems. This guide will take you from Grafana beginner to seasoned expert, unlocking insights and strategies that ensure your team stays ahead of downtime, performance issues, and everything in between. Start here: More how-tos. IN THIS ARTICLE Table of Contents Toggle Section 1: Welcome to Grafana—Your Observability Companion1.1 The Power of Grafana1.2 Getting Started: Quick & Easy Installation1.3 First Steps: Your Gateway to GrafanaSection 2: Unlocking the Potential of Your DataSection 3:…
In a strategic initiative set to revolutionize IT operations, NetApp and NVIDIA have formed a groundbreaking partnership aimed at advancing Artificial Intelligence for IT Operations (AIOps) and Site Reliability Engineering (SRE). By aligning NetApp’s proven data management excellence with NVIDIA’s cutting-edge AI technologies, the partnership introduces robust solutions capable of significantly enhancing reliability, efficiency, and innovation in complex IT environments. The importance of this alliance is underscored by the increasing complexity and scale of enterprise IT infrastructure. Companies navigating rapid digital transformation demand powerful solutions capable of handling enormous datasets and sophisticated analytics. The combination of NetApp’s scalable data solutions…
The Artificial Intelligence for IT Operations (AIOps market size) is rapidly expanding, transforming how enterprises manage complex IT systems. Crucial to Site Reliability Engineering (SRE) teams, AIOps technology provides essential predictive and proactive solutions. This comprehensive guide delves deeply into the current AIOps market size, influential market trends, key growth drivers, and actionable insights, positioning your organization ahead of industry curves. IN THIS ARTICLE Table of Contents Toggle Understanding the AIOps Market Size and Current LandscapeKey Drivers Fueling Current AIOps Market ExpansionProjected AIOps Market Size: Explosive Growth Through 2029Accelerating Adoption of Microservices and ContainerizationAdvanced Predictive Analytics and Incident ResolutionCost Optimization…
Introduction: Unlocking AI’s Full Potential with Prompt Engineering Have you ever wondered why some AI-generated outputs are precise, insightful, and highly effective, while others miss the mark completely? The secret lies in prompt engineering—a critical, yet often overlooked, skill essential for maximizing AI capabilities in AIOps and Site Reliability Engineering (SRE). In this comprehensive guide, you’ll dive deep into prompt engineering, discovering how it can dramatically enhance operational effectiveness, reduce manual efforts, and improve decision-making processes. IN THIS ARTICLE Table of Contents Toggle Understanding Prompt EngineeringImportance of Prompt Engineering in AIOps & SRECore Techniques and Best Practices1. Clarity and Specificity2.…