The AI SRE Handbook: Operating Production Systems in the Age of AI
AI didn't create production complexity, but it has made it grow much faster. Teams are shipping more code, faster, into systems with more dependencies and more probabilistic components. The human workflows that classic SRE was built on can't keep up. Organizations have more telemetry than ever and still lose 40 minutes per incident assembling evidence the system already had. The bottleneck is no longer visibility. It's reasoning.
The AI SRE Handbook is a practitioner's guide for engineering leaders, SREs, and platform teams running production at scale. It covers why observability and AIOps alone can't close the gap, what an AI SRE platform has to do to replace correlation with causal reasoning, and how teams at American Express, Capital One, and PepsiCo are changing how they handle incidents and on-call.
What you'll learn
- Why AI systems fail differently: how semantic failures pass every health check, and why RADES (Rate, Accuracy, Duration, Errors, Saturation) extends RED for AI-powered production.
- The L0–L5 maturity ladder: where your organization sits today, from manual troubleshooting to self-driving production, and what each next level requires.
- Five questions for evaluating any AI SRE platform: the architecture behind a credible platform, plus benchmark data showing why a frontier model wired to your observability stack isn't enough.
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