It's very common, but (like most things with LLMs) it's not as deterministic as you might imagine. A common technique for agents is to have them create a "handoff" document (usually markdown) that summarizes the previous session-- goals, important files/links, etc. There are dozens of proprietary ways of doing this, and Claude Code automates the process with its /compact command and even does auto-compaction as you reach your context limit. ChatGPT has been doing autocompaction since the beginning as it started out with a comically small context window.

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bathtub365 3 hours ago | parent [-]

The problem with auto compaction is that you aren’t given the opportunity to review its compacted understanding to confirm that it’s correct or doesn’t contain large omissions. I try to avoid letting it compact whenever possible and stick to plans that I review because it seems to get extremely dumb after an auto compaction.

	▲	jazzypants 3 hours ago \| parent [-]
		Yeah, I still find Opus to be pretty unreliable once you get past around 150K tokens, so I usually run a custom hand-off command at that point that extracts specific elements to specialized documents. The command contains a "Documentation Map" with single line summaries of each of those documents to help the agent sort everything out. Like most memory systems, it works pretty well around 80% of the time. I messed around with RAG and other complex solutions, and I never got much better results than my KISS system.