Presentation: Building Reusable Evaluation Frameworks for Agentic AI Products
Susan Chang explains how Elastic transitioned from siloed, ad-hoc AI agent evaluations to a unified, production-grade framework. She discusses balancing LLM-as-a-judge with deterministic rules, bridging Python data science evals with TypeScript production
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What Happened
Susan Chang explains how Elastic transitioned from siloed, ad-hoc AI agent evaluations to a unified, production-grade framework. She discusses balancing LLM-as-a-judge with deterministic rules, bridging Python data science evals with TypeScript production code, and implementing deep tracing to catch regressions across complex RAG and cybersecurity workloads while preserving domain context. By Susan Chang
Why It Matters
This item was selected from the latest technology feeds because it is timely and relevant to AI, software, security, cloud, hardware, or digital products. The original publisher remains the primary source for the full reporting and any subsequent updates.
What To Watch
Watch for follow-up reporting, official statements, product documentation, or additional technical details as the story develops.