AI brokers are more and more able to reasoning and performing autonomous work over lengthy intervals. Nonetheless, as brokers tackle extra advanced, longer-horizon duties, preserving them provided with the precise info turns into the core engineering problem. The business is transferring away from pre-loading context upfront towards a mannequin the place brokers dynamically navigate and retrieve the information they want, after they want it.
Redis is approaching context administration utilizing a context engine, which is an structure constructed round 4 pillars: on-demand context retrieval, knowledge that’s all the time present, quick retrieval, and a reminiscence layer that improves over time. In follow this implies constructing materialized views of knowledge with a semantic layer on prime, fairly than giving brokers direct entry to manufacturing databases. A reminiscence system sits alongside this, extracting and compacting info asynchronously because the agent works.
Simba Khadder leads AI technique at Redis, and he beforehand co-founded the characteristic retailer platform FeatureForm, which was acquired by Redis in 2025. On this episode, Simba joins Kevin Ball to debate why context has grow to be the defining problem in agentic AI, how context engines differ from conventional RAG architectures, how materialized views underpin dependable agent knowledge pipelines, how reminiscence programs can enhance by async extraction and compaction, and the way engineering groups must adapt their practices as AI-driven improvement accelerates.
Full Disclosure: This episode is sponsored by Redis.
Kevin Ball or KBall, is the vp of engineering at Mento and an impartial coach for engineers and engineering leaders. He co-founded and served as CTO for 2 firms, based the San Diego JavaScript meetup, and organizes the AI inaction dialogue group by Latent Area.
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