There is no shortage of writing about applying AI to infrastructure. Most of it stops at the demo. You will find a great deal about wiring a language model to an alert manager, and very little about what happens on the day the model is confidently wrong at 3 a.m., while an auditor asks who authorized the action it took.
This book is about the gap between those two things. It is written for platforms operating in safety-critical and regulated industries — aviation, healthcare, finance, public sector — where self-healing is a design discipline rather than a marketing phrase, and where "the model
suggested it" is not an acceptable answer to a regulator.
What you will build. Seven working labs, not toy examples: a production MCP server with OAuth 2.1, per-tool RBAC and an immutable audit trail; alert enrichment that keeps paging when the model provider is down; an incident scribe that drafts a blameless postmortem from a Slack thread; a three-agent investigation system with a human review gate; a closed-loop remediator that acts behind a reversibility gate and rolls back when a probe says the world got worse; and a policy contract with per-action budgets, an approval queue and a kill switch. Every lab runs on a laptop — no corporate cloud account, no paid identity-provider tenant.
What makes it different. Every pattern is anchored to a named AWS or Azure Well-Architected reliability principle, then mapped against the regulation that constrains it: the FAA AI Safety Assurance Roadmap, the NIST AI Risk Management Framework, Singapore's 2026 agentic-AI framework, the EU AI Act and EASA guidance. Appendix A carries the full pattern-by-regulation matrix with a source link for every claim.
The book is also honest about its own limits. Chapter 5 argues against autonomous remediation on the production-alert surface, in a book whose title contains the word autonomous. Chapter 3 concedes that a fifty-line heuristic at one percent of the cost is a perfectly good baseline, and that AI is allowed to lose to a regular expression on the merits.
Who it is for. Platform engineers and cloud architects designing the surfaces AI tooling will run on; senior SREs handed AI tooling and asked to make it reliable; AI/ML infrastructure leads who have the model working and now need it to survive an audit. It assumes you have run something in production and been paged for it. It does not assume you know how a transformer works, and never requires it.
