AI systems demand more than accuracy they demand reliability, traceability, and observability. In this session, I’ll walk through building strong quality gates and observability layers for AI-powered microservices running on Kubernetes. We’ll explore how to embed sanity checks directly in GitHub workflows, apply SOLID principles for maintainable pipelines, and use monitoring stacks like Prometheus and Grafana for microservice health.
Beyond standard observability, I’ll dive into AI-specific insights tracking token usage, latency, and response consistency for LLMs with tools like Langfuse and Arize Phoenix, ensuring operational transparency in AI workflows. The talk wraps up with how feature flags (LaunchDarkly) and kill switches can enable safer rollbacks and faster recovery from failure. Expect practical takeaways that bridge AI reliability with cloud-native observability best practices.