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Discover how to build reliable AI systems that transition successfully from demo environments to production through comprehensive evaluation strategies in this 27-minute conference talk. Learn why evaluation serves as the cornerstone of trust in AI agents and explore how leading teams integrate testing and monitoring throughout their entire development lifecycle. Examine practical approaches for optimizing context using write, select, compress, and isolate strategies while understanding real-world implementation challenges faced by both startups and enterprises. Gain insights into designing scalable LLM evaluation workflows that maintain quality and reliability across different environments, with specific focus on closing the loop between data collection, decision-making, and user trust. Explore proven methodologies for integrating pre-launch experimentation with post-deployment monitoring to create robust AI agent evaluation pipelines that perform consistently in production settings.
