Presentation: Building Reusable Evaluation Frameworks for Agentic AI
Susan Chang explains how Elastic transitioned from siloed, ad-hoc AI agent evaluations to a unified, production-grade framework.
By Dillip Chowdary • Oct 08, 2026 • Source: InfoQ
Building Reusable Evaluation Frameworks: what actually changed
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InfoQ reports: 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 code, and implementing deep tracing to catch regressions across…
Building Reusable Evaluation Frameworks: why it matters now
For primary quotes and complete technical detail, see InfoQ's original report linked above.
Developer Action Items
- ☐ Verify the claim on the official Framework / Python page (or InfoQ), not from this recap alone.
- ☐ Name the surface that moved — API, policy, model, hardware, or commercial terms — before you Slack the thread.
- ☐ Assign one owner a day to read the primary material and decide: this-sprint, this-quarter, or noise.
- ☐ Do not change production on day-one coverage. Watch the vendor changelog and one independent write-up first.
Author
Dillip Chowdary
Writes Tech Bytes coverage of AI, engineering, and the tools that actually ship. Editor of Tech Pulse Daily.
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