Getting AI systems to work in a demo is the easy part. Making them fast, consistent, and scalable enough to trust in production is where most teams hit a wall; juggling separate systems for retrieval, memory, and agent state that each need to be built, maintained, and kept in sync. This session shows you how to power RAG, long-term memory, and agent workflows from a single, real-time context engine on Redis, using reference architectures and practical patterns you can apply to your own stack.

• One context engine for RAG, memory, and agents; consolidate retrieval, conversation history, and agent state into a single system
• Fast retrieval that holds up in production; reference architectures for low-latency vector search that performs consistently under real workloads
• Persistent memory that makes agents more useful over time; patterns for storing and retrieving long-term conversation history so your AI systems build on prior context
• Scalable agent workflows you can actually ship; move beyond proof-of-concept with architectures designed for the consistency and durability production demands