The Four Gaps Between Demo Agents and Production Systems
Key Takeaways:- Understand the four critical gaps—validation, contextualization, latency, and decomposition—that separate demos from production systems.
- Learn why evaluation, orchestration, and real-world variability matter more than benchmark performance. 
- Discover best practices for building AI systems that are robust, scalable, and ready for real-world deployment.
Description
AI agents can look impressive in demos—but getting them to work reliably in production is a completely different challenge. The gap between a polished prototype and a robust system often determines whether an AI project succeeds or fails. Understanding these gaps is critical for anyone building real-world AI applications.
In this presentation webinar, Yuval Belfer, a Senior Developer Advocate at AI21 Labs, will unpack the four key gaps that prevent AI systems from making it to production: validation, contextualization, latency, and decomposition. You’ll learn why single-run performance is misleading, how real-world inputs differ from benchmark averages, and why orchestration—not just model quality—is the key to scaling AI systems. The session will also explore architectural patterns that help close these gaps and turn promising demos into reliable, production-grade systems.
Presenter Bio
Yuval helps clients understand how to use AI21 Labs's enterprise AI tools. He's also a Senior Lecturer in LLM development at Reichman Tech School, and an organizer of the AI Tinkerers community. Previously, Yubval was a System-on-Chip Design Engineer at AWS. He hosts YAAP (Yet Another AI Podcast).