How AI Agents Should Prioritize What to Drop When Context Windows Fill Up

As AI agents handle longer tasks, their context windows fill up — largely due to accumulated tool results — making smart memory management essential. A tiered retention strategy classifies content into four categories: pinned items like the original task, irreversible facts moved to structured state, compressible reasoning, and evictable bulk tool outputs. Rather than simply deleting old tool results, developers are advised to replace them with lightweight summaries that record what happened and allow retrieval via a recall tool if needed. Compaction of mid-history reasoning should be triggered infrequently and aggressively, around 65% window usage, to preserve prompt caching benefits. The most robust long-term fix, however, is designing tools to store large outputs externally from the start and return only handles and excerpts to the agent.