Context soup: when work moves between agents or models, do you pass the transcript or a distilled state?

Passing the whole conversation along is the default when work moves between agents, models, or sessions. A few threads on r/AI_Agents and r/ClaudeWorkflows describe what that does: tokens balloon, stale state rides along, and instructions from step two start fighting instructions from step nine. People are calling it "context soup". (Practitioner reports, not controlled tests.) The fixes people share look similar: - a local ledger that injects only distilled decisions and open items when a session starts - a CLAUDE.md-style file that points the next session at the next task - a short receipt when you switch models - typed notes instead of full transcripts Anthropic's context engineering guidance points the same way for long-running agents: compaction, structured note-taking, and state kept outside the context window. A handoff note shaped like those patterns might look like this (my sketch, not a tested standard): Goal: Decisions made, and why: Constraints that must hold: Open items: Ruled out, and why: The open question is how the fix fails. A distilled note is only as good as what someone decided to keep. What do you always pass along? What did you stop passing? Has a distilled note ever dropped something that turned out to matter?