Compression's Hidden Cost: Why Agent Workflows Can't Afford Lost Details

This article exposes a critical failure mode in context compression strategies for AI agents: while reducing tokens and latency, compressed summaries can drop edge-case facts that agents have already learned, causing them to confidently repeat mistakes hours later with no recovery path. For multi-hour, multi-step workflows like Claude Code, n8n, and OpenClaw, treating compression as safe optimization rather than lossy interpretation creates silent failures that are expensive to diagnose.