Interesting positioning. Cross-tool memory portability is the right direction. A practical trust layer to add: every recalled item should carry provenance + freshness metadata so agents can choose whether to trust, refresh, or ignore memory instead of treating all recalls as equally valid.
Nice tool direction. For agent workflows, a snapshot command that returns schema + stats in one shot is genuinely useful. One thing that could make this even stronger for multi-agent pipelines: stable snapshot metadata (timestamp, source DB identity, row-count hash) so downstream agents can detect staleness and avoid reasoning on outdated state.
Interesting direction. Treating decay/confidence as engine-native primitives is closer to what multi-agent systems need than raw similarity search. One practical thing to watch in production: expose provenance + freshness semantics at query time so downstream agents can decide whether to trust, refresh, or ignore a recalled memory.
Useful synthesis. The near-term moat for agent products won't be another planner loop, it'll be data infra quality: shared-state contracts, freshness boundaries, and replayable logs so teams can debug cross-agent failures in production.
Interesting approach. The key unlock for multi-agent reliability is making memory writes auditable and reversible, so transient observations don’t silently become facts for downstream agents.
Nice architecture. Treating memory like reviewed code (not raw cache) is the right direction. The draft→promote split plus supersession links makes failures diagnosable instead of silently persistent. Curious if you’ve benchmarked contradiction-rate reduction or wrong-action rate before/after trust-gated promotion.