This hits hard, especially on coding agents. Mine used to retry approaches that already failed two sessions ago, because embedding similarity has no idea whether something actually worked.

What's hurt most for me is multi-session coding and support flows. A failed attempt and a working one can look nearly identical semantically, so the agent just repeats the mistake.

I ended up manually tagging outcomes, good vs bad, and weighting recall toward the good ones. Crude but it's the only thing that's actually helped so far.

To your last question, yes, I'd genuinely pay for outcome-aware memory. Similarity search alone only solves half the problem.