Hit exactly this problem some time ago (preliminarily buried 5 or 6 versions of memory systems).

Generic summaries or RAG results often feel useless because models optimize for "what would be useful to explain to anyone" rather than "what was significant in this specific context."

What worked for me: separate semantic context (the "why are we here" layer) from structured tracking (decisions, blockers, dependencies). The semantic layer captures salience — what mattered emotionally or strategically — and the tracking layer handles facts or even snapshots of the latest state of the process.

The model does not have to guess what was important. The memory architecture can encode it.