Reusable Evaluation Frameworks

From siloed AI evals to a unified framework for production-grade agentic products

Moving from ad-hoc AI agent evaluations to a unified, production-grade framework is the kind of transition most teams only talk about. Susan Chang shows how Elastic actually did it. She balances LLM-as-a-judge with deterministic rules and bridges Python data science evals with TypeScript production code. Deep tracing catches regressions across complex RAG and cybersecurity workloads. It is a practical, human-centered approach to keeping domain context intact. For more on building these skills, explore our five free courses on practical AI engineering.