analiza aiqbench

The panel focuses on how AI systems fail in production: they produce plausible but wrong outputs, and drift in embeddings or semantics can break behavior while infrastructure monitoring still reports healthy. The examples, including a financial compliance agent disrupted by an unannounced embedding model update, point to the need for continuous evaluation, tracing beyond uptime metrics, and clear ownership of deployment approvals. For practitioners, the practical takeaway is that agent reliability and data-privacy controls require monitoring of model outputs and retrieval behavior, not just system health.