Post by Redis

What most teams think is a data problem is actually a context problem. Customer data lives in one system and policies live in another. Product info, conversation history, and operational signals are all scattered across APIs, databases, and SaaS tools. The real bottleneck isn’t the model; it’s getting the right information at the right moment. That's the discipline behind context engineering: giving agents fresh data, relevant memory, trusted knowledge, and real-time signals instead of a single slice of the picture. It's also why RAG alone doesn't solve this anymore. Production agents need chunk-based retrieval for documents, MCP-style tools for structured data, and agentic RAG to decide what to fetch and when. Memory and semantic caching are part of the same system, and teams routinely mix them up. Memory remembers what's true about a user, account, or project. Semantic caching skips a redundant LLM call when a similar question has already been answered safely. One builds continuity. The other cuts cost and latency. Confusing them is how a cache ends up serving the wrong answer to the wrong account. Simba Khadder built out an FAQ that answers common questions about building better AI agents with real-time context, memory, RAG, and semantic caching. Full FAQ here: https://lnkd.in/eN72FNtX