How to prevent AI inference stack silent failures

Silent failures in AI inference stacks occur when an API layer between the client and inference engine incorrectly passes tool schemas, drops state, or handles fields inconsistently — causing accuracy loss with no visible errors. The only reliable way to catch this is end-to-end benchmarking using something like the Berkeley Function-Calling Leaderboard (BFCL). Testing OGX and vLLM across OpenShift AI 3.3 and 3.4 revealed that upgrading OGX alone actually regressed multi-turn tool-calling accuracy, while upgrading both OGX and vLLM together yielded a 6.6 percentage point gain (44.8% to 51.4%). The key lesson: infrastructure components must be tested and upgraded together, not in isolation.