Context is all you need

Learn why context — not intelligence — is becoming the defining challenge for AI agents, and how Redis helps developers build real-time context engines that make agents faster, more accurate, and more reliable. Overview AI agents are improving at an incredible pace. Modern reasoning models can independently work through complex tasks, call tools, and interact with systems in ways that increasingly resemble human workflows. But as agents become more capable, one challenge becomes impossible to ignore: context. In this video, we explore why context engineering is emerging as one of the most important new disciplines in AI development. You’ll learn why traditional RAG architectures are no longer enough for long-running agent workflows, how stale or poorly structured context leads to hallucinations, and why agents need systems that are dynamically navigable, real-time, and continuously improving. We also dive into how Redis powers a real-time context engine with semantic retrieval, agent memory, vector search, semantic caching, and operational data pipelines — giving AI agents the context infrastructure they need to reason effectively at scale. Key topics covered - Why AI agents need a real-time context engine - The evolution of agentic reasoning and tool calling - The four pillars of effective context systems - Why traditional RAG architectures fall short for agents - Dynamically navigable context for AI workflows - Preventing hallucinations with fresh, structured context - Agent memory and learning from past decisions - Semantic caching for faster, lower-cost AI responses - Redis vector search, HNSW indexing, and time-series capabilities - Building scalable AI infrastructure with Redis Resources & links - Learn more about Redis Iris: https://redis.io/iris/ - Redis Cloud: https://redis.io/cloud/ - Redis Vector Search Docs: https://redis.io/docs/latest/develop/interact/search-and-query/ - Redis LangCache: https://redis.io/docs/latest/develop/ai/langcache/ - Redis Agent Memory: https://redis.io/docs/latest/develop/ai/context-engine/agent-memory/ Have questions? Drop them in the comments — we’re here to help. Subscribe for more Redis content: https://www.youtube.com/@Redisinc?sub_confirmation=1 #Redis #AI #AIAgents #LLM #RAG #VectorSearch #AgentMemory #SemanticCaching #GenAI #MachineLearning About Redis We’re the world’s fastest in-memory database. From our open source origins in 2011 to becoming the #1 cited brand for caching solutions, we’ve helped more than 10,000 customers build, scale, and deploy the apps our world runs on. With cloud and on-prem databases for caching, vector search, and more, Redis helps developers build faster, more responsive AI applications.