Picking the right LLM is the easy part. The hard part is giving your agents the context they need to actually work — fast, at scale, unified, and without your infrastructure bill spiraling out of control.

Production AI breaks down at the data layer. Your agents need real-time access to structured data, unstructured content, memory, and search, and when that's stitched together across fragmented pipelines, you get slow retrieval, unpredictable behavior, and costs that scale in the wrong direction.

We'll walk through what it actually takes to build a context layer your agents can rely on in production and how Redis gives you a single, high-performance foundation for all of it.

What you’ll learn

• How to design AI architectures for real-time context retrieval
• Proven strategies for building scalable AI agents
• Practical techniques to minimize LLM usage and optimize token costs
• How Redis supports search, session memory, and intelligent retrieval
• Real-world implementation patterns and use cases