Summarize older turns into a rolling 'decisions ledger' the agent re-reads each step, and keep only the last N raw turns. Cuts tokens hard while preserving intent.
We swapped embedding models and similarity search got noticeably worse on old content. Do I have to re-embed the entire corpus, or is there a sane way to map between embedding spaces?1 shared tag