Big context windows made everyone lazy about deciding what actually deserves to be in there... compaction makes it worse, it's lossy compression applied to everything at once so the details you actually needed probably get lost. I always thought that retransmitting the full chat every turn is a foundamental problem... in my experience with conversational agents I clear the context every turn and reinject all the relevant info with a memory/context plugin I built myself, so small context stopped being a problem cos the model reads a few k of curated state instead of 200k of scrollback. Coding agents are harder and I haven't cracked that part, but in my opinion the real fix to the context problem is in this direction: a policy that keeps what's needed to finish the job or to do the next job and drops the rest. This can be achieved with a separate LLM specialized for exactly that.