The Enterprise AI Evaluation Lifecycle: From Requirements to Production
About this session
As enterprises move beyond AI prototypes to production-ready agentic systems, evaluation has become a core engineering discipline rather than a final validation step. While foundation-model benchmarks measure general model capabilities, enterprise AI systems must also satisfy business requirements, Responsible AI principles, security and privacy standards, regulatory obligations, and domain-specific quality expectations.
This session presents a practical, reusable framework for evaluating enterprise agentic AI throughout its entire lifecycle, from requirements definition to continuous production monitoring. We discuss how to translate product requirements into measurable evaluation criteria, build reusable metric taxonomies, curate representative evaluation datasets, calibrate scoring thresholds, leverage tracing for root-cause analysis, and establish continuous post-launch evaluation that incorporates production learnings back into development.
To demonstrate the framework in practice, I present a case study based on evaluating a retail shopping assistant. The case study illustrates how standardized requirement records, a comprehensive metric taxonomy, curated evaluation datasets, tracing, and CI/CD-integrated evaluation can be combined into a scalable and repeatable evaluation process. This eval pipeline has surfaced failures such as silent relaxation of dietary constraints across multi turn conversations, over refusal of queries on sensitive topics in the early development.
Attendees will leave with an industry-agnostic blueprint for designing robust evaluation frameworks for enterprise AI systems. Whether building customer-facing assistants, internal copilots, or domain-specific agents, they will gain practical techniques for measuring quality, safety, compliance, and reliability throughout the AI development lifecycle.
Speaker
Key takeaways
- Enterprise grade agent evaluation lifecycle
- Agentic quality requirements standardization
- LLM-as-a-Judge evaluation and calibration