I was inspired by this blog from and wanted to dive deeper into how context management works in…

I was inspired by this blog from and wanted to dive deeper into how context management works in always on / persistent / long horizon agents like @bot @Muse and Instinct Unlike coding agents, persistent agents don't have the notion of 1 thread = 1 task. It's just 1 = N tasks. You can switch tasks midway, leave things unfinished, come back weeks later and pick up older tasks. It can get messy. - each turn, the model sees a short profile of you, your open tasks, relevant context from earlier sessions, and the recent messages verbatim (similar to the supermemory article) - when the context widow reaches some limit (say 70%) this triggers a compaction. The oldest turns get folded into the "running recap" and some window of k latest messages stay verbatum. - when the chat goes quiet / on a fixed timer, a background job is triggered to distill the conversation into episodic + long term memory. The running recap can be disacarded since it's been distilled into memory. - when you come…