[delayed]

Oh that's quite a nice idea - agentic context management (riffing on agentic memory management).

There's some challenges around the LLM having enough output tokens to easily specify what it wants its next input tokens to be, but "snips" should be able to be expressed concisely (i.e. the next input should include everything sent previously except the chunk that starts XXX and ends YYY). The upside is tighter context, the downside is it'll bust the prompt cache (perhaps the optimal trade-off is to batch the snips).

Yeah, the fact that we have treated context as immutable baffles me, it’s not like humans working memory keeps a perfect history of everything they’ve done over the last hour, it shouldn’t be that complicated to train a secondary model that just runs online compaction, eg: it runs a tool call, the model determines what’s Germaine to the conversion and prunes the rest, or some task gets completed, ok just leave a stub in the context that says completed x, with a tool available to see the details of x if it becomes relevant again.

i think something kinda easy for that could be to pretend that pruned output was actually done by a subagent. copy the detailed logs out, and replace it with a compacted summary.

> For example, if you’re working with a tool that dumps a lot of logged information into context

I've set up a hook that blocks directly running certain common tools and instead tells Claude to pipe the output to a temporary file and search that for relevant info. There's still some noise where it tries to run the tool once, gets blocked, then runs it the right way. But it's better than before.

That's exactly what context-mode does for tool outputs. Instead of dumping raw logs and snapshots into context, it runs them in a sandbox and only returns a summary. The full data stays in a local FTS5 index so you can search it later when you need specifics.