We've been running something conceptually similar but with a deliberately simpler memory model - flat markdown files (MEMORY.md for curated long-term recall, daily logs for raw session notes) instead of a graph structure.

The tradeoff we found: graph-based memory is more queryable but adds architectural complexity that breaks when the runtime crashes or the agent needs to be inspected by a human. Flat files are readable, git-diffable, and survive catastrophic failures better.

The loop you describe (reconstruct > reason > decide > execute > record) matches almost exactly what we landed on. The part that's still unsolved for us is "update memory" - specifically, who decides what's worth keeping long-term vs discarding. Right now it's the agent's judgment call, which works until it isn't.

Curious what your Memory Fusion Engine does differently at the curation layer - is it content-based similarity, recency weighting, or something else?