A filling context window gauge with older content being compressed and summarized

Managing Context Windows for Long-Running Agent Tasks

A task that runs long enough eventually fills whatever context window it has, and what happens next determines whether the agent stays coherent or starts losing track of earlier decisions.

📝 Rolling Summarization

Periodically compressing older turns into a dense summary while keeping recent turns verbatim preserves the gist of earlier decisions without paying full token cost for them indefinitely — the standard approach for conversational tasks.

💾 Externalizing State Instead of Carrying It

For tasks tracking structured progress (a checklist, a set of decisions made), writing that state to a file or memory store the agent can re-read is more reliable than hoping it survives compaction inside the context itself.

⚠️ Where This Still Breaks Down

Tasks requiring precise recall of something said many turns ago, in exact wording, don't survive summarization well — if exact fidelity matters, that specific detail needs to be externalized explicitly, not left to compaction to preserve.