The Four Gaps Between Demo Agents and Production Systems

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.