AI systems can fail with every request returning 200. This guide covers detection signals, containment, provider failover, release rollback, and recovery verification, built around one question: which effects can be undone and which cannot.
A cheaper model call does not mean a cheaper result. This guide defines cost per successful task, models what retries, caching, routing, and human review do to it, and shows a simulation where cost per request falls while cost per success rises.
On a shared AI platform, tenant identity gets lost between the gateway, the index, the cache, and tool calls. This guide shows where, how to fix each path, and how to test isolation.
A healthy dashboard can sit above a model that gives wrong answers. This guide defines latency, availability, and task-success indicators, error budgets, and degradation for AI services.
AI debugging wants prompts and tool results, which are the most sensitive data a request holds. This guide shows what to record by default, how far redaction can be trusted, and how to control access.