The Architecture Behind AI Agents: Why Context Matters More Than Prompts

Most AI agent failures stem not from poor prompts but from inadequate context—missing data, incorrect information, or inaccessible context when needed. Context engineering, which optimizes information architecture, retrieval quality, and state management around agents, is emerging as the critical skill for building reliable AI workflows. This represents a fundamental shift from optimizing how we ask questions to optimizing the conditions under which those questions are answered.