Context engineering was supposed to solve AI’s scalability challenges. Teams are building RAG pipelines, crafting prompt templates, and implementing memory systems—each application starting from scratch. As AI scales organization-wide, these tactical approaches hit fundamental limits: fragmented systems, inconsistent outputs, and no organizational context intelligence.

This keynote explores the essential building blocks of an enterprise context platform and introduces context management as the emerging discipline that changes how organizations approach this challenge.

Key Takeaways:

• Understand the fundamental differences between metadata for humans and context for AI agents
• Learn why current context engineering approaches create compounding technical debt that kills enterprise AI scaling
• Discover the emerging architectural pattern that transforms context from bottleneck to competitive advantage
• Explore the transformative possibilities that context management will unlock for enterprise AI

Context engineering was supposed to solve AI’s scalability challenges. Teams are building RAG pipelines, crafting prompt templates, and implementing memory systems—each application starting from scratch. As AI scales organization-wide, these tactical approaches hit fundamental limits: fragmented systems, inconsistent outputs, and no organizational context intelligence.

This keynote explores the essential building blocks of an enterprise context platform and introduces context management as the emerging discipline that changes how organizations approach this challenge.

Key Takeaways:

• Understand the fundamental differences between metadata for humans and context for AI agents
• Learn why current context engineering approaches create compounding technical debt that kills enterprise AI scaling
• Discover the emerging architectural pattern that transforms context from bottleneck to competitive advantage
• Explore the transformative possibilities that context management will unlock for enterprise AI

Context engineering was supposed to solve AI’s scalability challenges. Teams are building RAG pipelines, crafting prompt templates, and implementing memory systems—each application starting from scratch. As AI scales organization-wide, these tactical approaches hit fundamental limits: fragmented systems, inconsistent outputs, and no organizational context intelligence.

This keynote explores the essential building blocks of an enterprise context platform and introduces context management as the emerging discipline that changes how organizations approach this challenge.

Key Takeaways:

• Understand the fundamental differences between metadata for humans and context for AI agents
• Learn why current context engineering approaches create compounding technical debt that kills enterprise AI scaling
• Discover the emerging architectural pattern that transforms context from bottleneck to competitive advantage
• Explore the transformative possibilities that context management will unlock for enterprise AI

Context engineering was supposed to solve AI’s scalability challenges. Teams are building RAG pipelines, crafting prompt templates, and implementing memory systems—each application starting from scratch. As AI scales organization-wide, these tactical approaches hit fundamental limits: fragmented systems, inconsistent outputs, and no organizational context intelligence.

This keynote explores the essential building blocks of an enterprise context platform and introduces context management as the emerging discipline that changes how organizations approach this challenge.

Key Takeaways:

• Understand the fundamental differences between metadata for humans and context for AI agents
• Learn why current context engineering approaches create compounding technical debt that kills enterprise AI scaling
• Discover the emerging architectural pattern that transforms context from bottleneck to competitive advantage
• Explore the transformative possibilities that context management will unlock for enterprise AI