Originally published on AI Tech Connect.
What you need to know The window filling up is not the failure mode. Model accuracy degrades as context grows even when every relevant fact is still present. Reported degradations in the 2026 literature span roughly 13.9 percent to 85 percent depending on task and setup — a range wide enough to treat as a direction, not a number. Compaction is the answer the field converged on. Summarise the work so far, start a fresh window, continue. It rewrites the agent's logical context rather than its physical representation, which is why it works at the harness level regardless of who serves the model. The default triggers are both wrong. Reactive compaction fires at saturation, so the summarising model is already degraded when you ask it to do the most delicate job in the loop. Periodic compaction…
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