Mastering AIOps: Operationalizing Generative AI and Intelligent IT Infrastructure

At 02:40 your phone shows forty notifications describing the same broken thing in four different vocabularies. Somewhere in that pile is the alert that tells you what actually happened. Finding it takes twenty minutes. Those twenty minutes are the tax you pay for tooling that was never designed to talk to itself.

That tax is the subject of this book.

Two things changed recently. In 2025 Gartner retired the "AIOps Platforms" category entirely and rebuilt it as Event Intelligence Solutions, drawing a hard boundary around what the technology is actually for. Then in March 2026 both major cloud providers shipped autonomous operations agents into general availability, three weeks apart. The question stopped being whether AI would participate in operations and became how much authority you are willing to give it, and what has to be true before you do.

What this book covers

• Part I — why alert noise is structural rather than a tuning failure, what the market rename actually changed, and why dependency accuracy quietly decides whether any of this works
• Part II — generative AI across the four phases of the incident lifecycle: prepare, respond, communicate, learn
• Part III — GenAIOps: running language models in production, including embeddings, prompt management, guardrails and evaluation
• Part IV — stateful agents, the Model Context Protocol, and an honest read of the two hyperscaler agents now shipping, including what their published numbers do and do not establish
• Part V — governance, regulatory exposure, measuring impact, and a ninety-day adoption roadmap

Plus a self-assessment with answer key, a glossary, and a prompt library you can lift straight into your own incident tooling.

Who it is for

Site reliability engineers, platform and infrastructure engineers, IT operations and service management leads, and the architects deciding what to buy. It assumes you understand distributed systems and incident response. It does not assume you have trained a model or read a vector database manual.

A note on honesty

Gartner expects more than half of enterprise agentic-AI initiatives to underperform on return by 2029, usually because agents were deployed on observability data that was not good enough to support them. That finding shapes this book. Where a capability is production-ready it is described that way. Where it is aspirational — and fully autonomous remediation still largely is — it is described that way too.

This is an independent publication, not affiliated with or endorsed by any vendor or analyst firm.