Micro-compaction technique eliminates long pauses in AI agent context management

Developers running long AI agent sessions often face multi-minute freezes when frameworks perform bulk context compaction near the 80% context threshold. A new approach called micro-compaction, first drafted by the Hermes AI agent for its own codebase, spreads this summarization work across every turn instead of batching it at the end. After each turn, the oldest unabsorbed exchange is folded into a rolling summary, keeping context usage stable rather than letting it climb toward the compaction trigger. In a real 3.5-hour code review session spanning roughly 75,000 tokens, zero batch compactions occurred and context occupancy stabilized at around 22% instead of rising toward 80%. The feature is opt-in in hermes-agent and intentionally never compacts user messages, preserving original intent throughout the session.