Agents don't have an intelligence problem; they have a context problem.
Enterprise data is fragmented across dozens of systems, resulting in agents that fail in production because their context is stale, slow, and impossible to navigate. Introducing the real-time context engine, Redis Iris: the foundational layer that helps you build production-grade AI agents by turning scattered enterprise data into live, navigable, always-fresh context that gets better over time. Built on four core pillars: Redis Context Retriever, Redis Search, Redis Data Integration (RDI), and Agent memory.
Learn why context quality (not model quality) determines agent performance:
Navigable: Context Retriever exposes enterprise data through agent-native MCP endpoints
Fast: Redis Search provides the low-latency retrieval layer that makes the context engine production-ready
Fresh: RDI keeps the context layer continuously synced with upstream systems and ensures that context changes with the data source
Compounding: Memory captures personalization, durable interaction history, and relevant state that can accumulate and shape future agent behavior.
Join us for a live demo and see the product in action.
Speakers
Redis
Simba Khadder
Director of Engineering & Head of AI Product
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