
Artificial intelligence has moved from conversational assistants to autonomous agents that act on behalf of enterprises, introducing new reliability challenges. The article highlights three primary risks: unstable network connections, cascading dependency failures, and the non‑deterministic nature of model outputs. It explains how each risk can cause timeouts, silent errors, or token exhaustion. Proactive chaos‑engineering tests—such as Gremlin’s blackhole, latency, and packet‑loss experiments—are recommended to validate resilience and maintain continuous reliability.
Gremlin has introduced a Disaster Recovery Testing feature that lets organizations simulate catastrophic failures across all services with a few clicks. The tool builds on pre‑built test suites to establish baseline reliability scores, then supports regular weekly testing of individual...