Real-Time Context for AI Agents: Keep Inputs Fresh

What this post added

This post details the problem of stale context in multi-step AI agents, explaining how outdated information can lead to incorrect actions and policy violations. It identifies several failure modes, including cached answers, prompt caching, early errors becoming ground truth, and silent truncation. The post frames freshness as a context engineering problem, not just a RAG problem, and outlines four strategies: write, select, compress, and isolate. It then breaks down the six components of an agent's context that require real-time updates: working memory, session state, long-term memory, tool results, retrieved documents, and ML features. Finally, it stresses the importance of low-latency reads for the agent's read path, arguing that fast reads are essential for making freshness checks affordable on every step and preventing the agent from becoming a bottleneck.