FUNDING · CODING · #1528
Elastic presents a reusable, production-grade evaluation framework for agentic AI
At QCon AI, Susan Chang (Principal Data Scientist at Elastic) described how Elastic moved from siloed, ad-hoc agent evaluations to a unified production-grade evaluation framework that combines LLM-as-judge and deterministic rules, connects Python data-science evals with TypeScript production code, and uses deep tracing to detect regressions across RAG and cybersecurity workloads while preserving domain context.
KEY POINTS
- At QCon AI, Susan Chang (Principal Data Scientist at Elastic) described how Elastic moved from siloed, ad-hoc agent evaluations to a unified production-grade evaluation framework that combines LLM-as-judge and deterministic rules, connects Python data-science evals with TypeScript production code, and uses deep tracing to detect regressions across RAG and cybersecurity workloads while preserving domain context.
- A shared, production-grade evaluation framework and deep tracing help prevent regressions and reduce operational burden when deploying diverse agentic AI workloads across security and enterprise search.
- Presentation: Building Reusable Evaluation Frameworks for Agentic AI Products
WHY IT MATTERS
A shared, production-grade evaluation framework and deep tracing help prevent regressions and reduce operational burden when deploying diverse agentic AI workloads across security and enterprise search.