Context engineering for AI agents: the infrastructure behind every decision

Context engineering is a crucial aspect of AI agent operations, determining how information is curated for the agent's context window, which influences its decision-making process. Unlike prompt engineering, which focuses on task description, context engineering involves selecting, retrieving, filtering, compressing, and refreshing data to fit within the finite context window during each inference step. Six categories of input—system instructions, goal specifications, conversation memory, retrieved external knowledge, tool definitions, and execution state—compete for this limited space, making the assembly of context an infrastructure challenge. Effective context management requires a fast, integrated system to ensure agents operate on current, relevant data, avoiding the pitfalls of context failures that can lead to inefficient or incorrect agent actions. Redis Iris addresses this need by providing a context engine that integrates memory, retrieval, and real-time synchronization, enabling efficient data management across sessions to enhance the performance and reliability of AI agents.