• Our CEO Rowan Trollope talks about what’s new with Redis Iris and more on TBPN.
• What’s new in two: May 2026 edition—with Talon Miller.
• RAG is reaching its limit. See what’s replacing it.
• You can now query Redis indexes with familiar SQL syntax.
• Connect to Azure Managed Redis with Redis Insight 3.2.0.

80% of AI projects stall because of gaps in data, governance, or infrastructure.

The problem isn’t model capability. It’s context. Agents need the right data, in real time, with the controls production environments require.

Our new “Context engineering maturity model” helps teams assess where their context layer stands today, identify what’s holding agents back, and prioritize the next steps toward a production-ready, real-time context engine.

Use it to spot the failure modes slowing deployment, understand what a production-ready context engine requires, and build a practical roadmap from AI experimentation to enterprise scale.

Your agents should be getting smarter. But that’s hard to do when the data they need is stale, scattered, or lost between sessions.

Meet Redis Iris, our new real-time context engine that turns fragmented data into live, agent-ready context. It combines governed retrieval, real-time operational state, agent memory, semantic caching, and fast search, so agents can find the right customer record, order status, policy, prior interaction, or business signal while the workflow is still running.

And for teams moving agents into production, it means simpler architecture, faster access to context, and agents that get more useful over time. If your agents are ready for better context, see Redis Iris in action.