Real-time context engine: Fresh context for better AI agents

This webinar will be delivered in Hebrew.

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.