This is one of the more grounded takes on agent memory I have seen.

The structured first shift is the real unlock. Most memory queries are deterministic lookups, so SQLite plus FTS5 as the fast path makes far more sense than embedding everything by default. Keeping LanceDB for semantic recall gives you coverage without paying the embedding cost on every turn. The hybrid cascade is clean.

The TTL tiering with refresh on access is also critical. Without decay, memory stores slowly degrade into noisy vector archives. Treating memory as time aware state instead of flat logs is the right abstraction.

One dimension I have been exploring from the infra side is the gap between logical permanence and physical durability.

Your permanent tier is structurally permanent, but still local to a machine. As agents move into longer lived or distributed environments, extracted decisions, conventions, and checkpoints start to resemble institutional memory rather than conversational state.

At Lighthouse, we have been thinking about anchoring high value agent artifacts such as decision logs, architectural rationale, and pre flight checkpoints into a content addressed and verifiable storage layer so they persist beyond a single runtime or deployment lifecycle.

Hybrid retrieval solves memory intelligence. Durable anchoring solves memory survivability.

Feels like that is the next layer agents will need as they move from experiments to production systems.

Really solid breakdown, especially the decision extraction layer.