Probabilistic Resilience: Redefining SRE Frameworks for Nondeterministic AI Microservices

Traditional Site Reliability Engineering is founded on deterministic principles where service level indicators directly reflect binary states of success or failure. However, the widespread integration of Large Language Models and Agentic AI into enterprise microservices introduces unprecedented nondeterminism. This paper proposes a novel framework for Probabilistic Resilience. We explore how to define error budgets when outputs are subjective, how to architect semantic circuit breakers that halt anomalous AI reasoning loops, and how to implement temporal state unwinding to reverse the actions of rogue autonomous agents. This research provides a foundational blueprint for maintaining enterprise grade reliability in systems that think rather than just execute.