Unlocking the Data Layer for Agentic AI with Simba Khadder

AI agents are increasingly capable of reasoning and performing autonomous work over long periods. However, as agents take on more complex, longer horizon tasks, keeping them supplied with the right information becomes the core engineering challenge. The industry is moving away from preloading context upfront toward a model where agents dynamically navigate and retrieve the data they need when they need it. Redis is approaching context management using a context engine, which is an architecture built around four pillars, on demand context retrieval, data that is always current, fast retrieval, and a memory layer that improves over time. In practice, this means building materialized views of data with the semantic layer on top, rather than giving agents direct access to production databases. A memory system sits alongside this, extracting and compacting information asynchronously as the agent works. Simba Kotter leads AI strategy at Redis, and he previously cofounded the feature store platform FeatureForm, which was acquired by Redis in 2025. In this episode, Simba joins Kevin Ball to discuss why context has become the defining challenge in Agentic AI, how context engines differ from traditional RAG architectures, how materialized views underpin reliable agent data pipelines, how memory systems can improve through async extraction and compaction, and how engineering teams need to adapt their practices as AI driven development accelerates. Kevin Ball, or Kay Ball, is the vice president of engineering at Mento and an independent coach for engineers and engineering leaders. He cofounded and served as CTO for two companies, founded the San Diego JavaScript meetup, and organizes the AI in action discussion group through Latent Space. Check out the show notes to follow Keball on Twitter or LinkedIn or visit his website, keball.llc. Simba, welcome to the show. Thanks for having me. Yeah. Well, let's get started learning a little bit about you and your background, and then we can move into Redis and talking about that. So how do you explain yourself to folks? When I grow up, I wanna surf all day. But for today, I'm in AI. Okay. I mean, I could resonate with that. Surfing is a blast. Yeah. I started as a software engineer, then technical. When I started my career, I was at Google, a software engineer there. I solved a lot of really fun technical problems, worked with some super smart people, But I was always kinda itching to, like, go and learn on a slope that I felt like I had been learning on before. So I left Google. I started my first company, kind of did well there, started a company after, which was FeatureForm, which then was acquired by Redis. And I've always loved Redis as a product, so it's kind of awesome to become a huge part of the AI strategy here. But, yeah, that's kind of the the short. Alright. Well, and can you quickly describe FeatureForm and what it is so that we have that as well as background? Yeah. So FeatureForm, we were a feature …