AI Agent Context: What Goes Into the Window

This post details the critical role of context engineering in AI agent development, emphasizing that agent performance is limited by the information available in its context window at inference time. It breaks down the six key inputs competing for this finite budget: system instructions, goal specification, conversation memory (short-term and long-term), retrieved external knowledge (RAG), tool definitions, and execution state. The post highlights the infrastructure challenges of assembling these diverse data sources quickly, arguing that low-latency access to key-value stores, vector search, and session state is paramount. It positions Redis Iris as a solution for serving these varied access patterns with sub-millisecond performance, enabling agents to operate efficiently and make informed decisions.