Overview
When the context approaches its limit, agent platforms use a process commonly described as compaction or compression.
Before compaction, the model may have access to the complete conversation history. Every message, tool call, output, and correction is present.
At a threshold below the absolute limit, the system compresses a large portion of the history. It summarizes and densifies the information so the conversation can continue with fewer tokens.
The process can be visualized as a press compressing a large block of context into a smaller block. Information that previously occupied most of the window may be reduced to a much smaller fraction.
Compaction makes long-running sessions possible, but it is not lossless. The system may:
- Remove detailed tool outputs.
- Collapse several messages into a summary.
- Omit a minor decision.
- Lose the wording of an important correction.
- Preserve the result while dropping the path used to reach it.
- Remove an earlier detail that becomes relevant later.
The problem is not that compaction always fails. The problem is that the user may not know which detail was lost.