The Infrastructure Problem Behind AI Agent Failures

The critical gap between demo success and production failure in AI agents points to an infrastructure problem most teams miss entirely. Unlike traditional ML where intelligence translates directly to performance, agentic systems create complex feedback loops where failures cascade unpredictably—success depends on MLOps discipline, not smarter models. With 91% of AI agents failing due to systems engineering gaps, understanding infrastructure-first design is now essential for anyone deploying agents at scale.