Context compaction must preserve the next step

Context compaction for AI coding agents should be evaluated by whether an agent can accurately continue work after a long session, rather than by summary length or token reduction alone. Factory’s December 2025 study of 36,611 software-engineering messages compared retention across recall, artifact tracking, continuation, and decisions, reporting overall scores of 3.70 for Factory, 3.44 for Anthropic, and 3.35 for OpenAI, while noting that artifact tracking was the weakest area for all methods. The findings suggest that structured, persistent summaries can preserve technical details, but summaries should not replace repository evidence such as the working tree, patches, and test results. A Chainguard case study of a two-week, six-repository coding session illustrates the value of continuity but does not independently establish compaction’s cost or performance effects. Effective evaluation should record constraints, modified files, validation results, rejected approaches, and next steps before compression, then test whether the agent can resume correctly, incorporate revised decisions, and avoid redundant rereading or human intervention.

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