A state schema names fields, types, ownership, and update rules shared across a graph. It makes incompatible updates visible and supports safe persistence and replay.

State Schemas Preserve Meaning Across Nodes

Agents can coordinate through a shared, structured workspace instead of direct messages. Writes need schemas, authorship, versioning, conflict rules, and least-privilege access.

Blackboard Coordination Uses Shared Artifacts

A checkpoint persists a versioned, minimal state at a safe boundary. Resumption must verify code, schema, permissions, and whether external effects already occurred.

Checkpoints Make Runs Resumable

Constraint satisfaction represents variables, allowed values, and constraints linking them. Propagation removes impossible assignments before or during search.

Constraint Graphs Narrow Feasible Assignments

An event reports that something occurred; handlers update state and may emit new commands or events. Idempotency and ordering rules prevent duplicate or late events from corrupting progress.

Event-Driven Graphs React to Facts

A finite-state machine enumerates valid states and transitions. It is effective when control modes are bounded and illegal transitions must be rejected explicitly.

Finite-State Agent Control

Persisted state outlives code versions. A schema migration must transform old checkpoints deterministically, preserve invariants, and support rollback or quarantine when conversion fails.

Graph State Needs Versioned Migration

An ontology declares concepts, relations, constraints, and sometimes inference rules for a domain. It should be as small as the decisions it must support and evolve under version control.

Ontologies Define Shared Graph Meaning

In this situation—tracking query, evidence, confidence, approvals, and terminal status—which choice best applies “State Schemas Preserve Meaning Across Nodes”?

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