AI Agent Context: What Goes Into the Window

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This post details the six categories of input that compete for an AI agent's finite context window at each inference step: system instructions, goal specification, conversation memory (short-term and long-term), retrieved external knowledge, tool definitions, and execution state. It explains the engineering challenge of curating these inputs for optimal signal-to-noise ratio and discusses the infrastructure requirements for fast retrieval of these diverse data types, including key-value reads, vector/full-text search, and session state management. It highlights the latency budget constraints and the need for a memory-first architecture like Redis Iris to serve these access patterns efficiently.