Structured Cognition for Agent Fleets

Temporal is more than a timestamp. Every memory in Meaning Memory carries three time signals: when it was observed, when it becomes valid, and when it stops mattering.

Decay curves are operator-configurable per playbook. Some memories decay fast (today's traffic numbers). Some stay forever (a customer's allergy). Some don't decay but expire on a deadline (a paused deploy that resumes after security review). Meaning Memory tracks both decay and validity windows independently.

KV caches solve freshness with TTL. Vector stores solve recency with score boosts. Neither captures the truth that some memories fade, some expire, and some stay forever, and which is which is a governance question, not a heuristic.

Asymmetry, also called Attribution, is the dimension most memory systems fake with post-hoc filtering. In Meaning Memory, every memory carries explicit attribution and trust gradients native to the data model.

In multi-agent deployments this matters enormously. The same statement ("our churn rate is 4.2%") from the CFO and from a customer comment should not carry the same retrieval weight. Asymmetry encodes the trust gradient: asymmetry_modifiers is a JSONB field per entry, and the asymmetry scale multiplies into retrieval ranking.

Enterprise multi-agent fleets are political ecosystems. Marketing's agent sees one version of "true." Engineering's sees another. Legal's sees a third. Without attribution as a first-class primitive, cross-agent memory becomes either dangerously homogenized or frustratingly siloed. Asymmetry lets you have shared facts with trust-aware retrieval.

Relational turns memories into a typed graph. Every memory can connect to other memories, agents, documents, customers, projects, people, concepts, events, or commitments. Nine bounded target types ship today, plus a custom:* namespace for domain-specific extensions.

Multi-hop traversal lets agents reason across the graph: "show me everything connected to this customer ticket within two hops." Recursive CTE queries on Postgres return path-explain answers, with a configurable depth cap and a statement-level timeout so a misconfigured query can never freeze the request path.

Vector similarity surfaces "memories that look like this." Relational edges surface "memories that matter to this." The first is retrieval. The second is reasoning. Production agent fleets need both.

Episodic clusters memories into narrative arcs. A customer conversation isn't a list of facts, it's an episode with a beginning, a turning point, and a resolution. Most memory systems flatten that structure. Meaning Memory preserves it.

Every memory can carry an episode_id. The mm_episodes summary table maintains per-episode metadata. Agents can query "summarize this episode" without loading every underlying memory.

The unit of recall in human memory is not the fact. It's the episode. When you remember a conversation, you don't reconstruct it token by token, you replay the arc. Agent memory built on the same principle composes better, retrieves better, and audits better than fact-shaped retrieval.