Observability in the AI-Native Era: Leveraging AIOps to build, observe, and operate resilient systems

Endorsements “Observability in the AI-Native Era is a practical, timely guide for DevOps, SRE, platform engineering, and technology leaders looking into the adoption of AI in their teams. It connects observability fundamentals with AIOps, agentic platforms, trust, governance, cost, and security, without treating AI as magic.”- Pini Reznik, CEO & Co-Founder, re:cinq “This book provides a great, insightful exploration of observability, SRE, and AI, demonstrating how AI can enhance collaboration, accelerate troubleshooting, and effectively summarize events to improve operational tasks.”-Alexandra Franz, GEO Lead at Dynatrace Key Features Bridges observability and AI into a unified operational approach rather than treating them as separate domains Uses a continuous case study to connect concepts across chapters and reflect real-world engineering scenarios Focuses on evolving operational maturity from reactive to proactive and preventive systems Purchase of the print or Kindle book includes a free PDF eBook Book Description Observability is mandatory for building and operating cloud-native distributed systems. Tools like OpenTelemetry have standardized how observability data is sourced, and AI now transforms how we extract value from the vast amounts of observability data generated by modern systems. This book guides you in implementing scalable observability, improving engineering efficiency with AI, and integrating observability throughout the Software Development Lifecycle (SDLC) via modern self-service internal developer platforms. You'll start with observability basics and learn how AIOps enhances signal correlation, anomaly detection, and root-cause analysis. Using real-world examples, the book demonstrates how to implement AIOps, build proactive detection pipelines, and automate diagnostics and remediation. You'll explore best practices for expanding observability using OpenTelemetry, Prometheus, Grafana, Dynatrace, Datadog, and New Relic alongside machine learning models, ensuring your systems are accurate, efficient, and secure.You'll also learn how to benchmark, measure, and secure your AIOps implementation, and gain a practical understanding of software compliance and how it applies to your systems. By the end of this book, you'll be ready to design and deliver AIOps-enabled observability solutions that make cloud-native systems more resilient, efficient, and secure. Table of Contents Observability: The Art of Turning Data into Insights The Elephant in the Room: Artificial Intelligence From Observability to AIOps and the Use Cases it Solves Today4. ACME Financial Services: Implementing AIOps Democratizing Observability: A Primer to Self-Service Platforms The Observability Agent: Real-Life Use Cases ACME Financial Services: How to Move from AIOps to Agentic Platforms Evolving Operations: Proactive > Preventive > Self-Driven Architecture No Future Without Challenges ACME Financial Services: How Will the AI Future Shape Our Company?