The Context Paradox: Why AI Agents Deteriorate as Conversations Grow

As AI agents process longer conversations, they paradoxically degrade in performance despite having access to more information—a phenomenon that threatens production reliability. The author reveals this isn't a model limitation but a context management failure, where critical information gets buried under accumulated context. Understanding strategic compaction—the selective, lossy compression of conversation history—emerges as essential for developers building long-running agents that maintain consistent performance.