• Introducing the Redis Partner Network: a redesigned global program.
• AI agent benchmarks: where they fall short and why your infrastructure matters.
• Why your AI agent fails in production and how tracing helps.
• What is multicloud infrastructure? A guide for 2026.

As AI moves toward agentic systems, a new core challenge is emerging: getting the right data, at the right time.

Simba Khadder, Context Engine Lead at Redis, explores why solving for real-time, relevant data delivery is now central to AI success. As LLMs scale, teams need more than retrieval. They need systems that can manage memory, understand meaning, and deliver context dynamically. That’s where context engines come in, shifting teams from static pipelines to adaptive, production-ready architectures.

Good context gets noticed. Especially when it moves fast.

That’s why Fast Company named Redis one of the Most Innovative Companies of 2026, saying: “Redis created new storage technology to make interactions with AI tools, and the databases that power them, faster and more efficient.”

We’re building the context engine for AI apps and agents, spanning semantic caching, memory, and vector and hybrid search. And we’re not slowing down. We’re pushing forward so agents work from the right context, in real time.