This post describes FROST, an open-source Python framework for multi-agent AI systems focused on governance and accountability. The core claim: existing frameworks (LangChain, CrewAI) handle capability and orchestration well but neglect the "governance vacuum"—tracking responsibility, preserving agent experience across lifecycles, and enforcing hierarchical constraints. The technical substance: four atomic primitives (Store for memory, Skill for stateless functions, Agent as a container, SOP as executable procedures) compose into a "family governance model" with three hierarchical tiers (Ancestor→Parent→Leaf). This enforces read-only inheritance of ancestor records, validates descendant SOPs against constitutional rules, and gates agent spawning depth. The source also mentions a V5.0 "five-dimensional metamodel" with registries for capabilities, tasks, events, platforms, and rules. A practitioner considering this would need to evaluate whether the hierarchical + inheritance design actually solves real multi-agent coordination pain points, or whether it's trading flexibility for governance that existing systems handle adequately via prompt constraints and logging. The post uses biological metaphors heavily but grounds them in code examples. No independent validation of the "197 test cases" claim is visible here, and the source doesn't explain failure modes or what "selective persistence" does concretely.