MICA v0.1.5 Formalizes Governance Schema for AI Context Management
This article discusses the evolution of the MICA (Memory Invocation & Context Archive) governance schema for AI context management, from v0.1.0 to v0.1.5. It highlights how earlier versions made the schema more implementable, but lacked the ability to inspect the governance details.
Why it matters
Formalizing governance within the AI context management schema is crucial for transparency, auditability, and ensuring the integrity of the system.
Key Points
- 1MICA defines how AI context should be structured, trusted, scored, and handed off across sessions
- 2Earlier versions (v0.1.0 to v0.1.4) addressed issues like scoring semantics and encoding constraints, but governance details remained outside the schema
- 3v0.1.5 introduced new concepts like Provenance Registry, Deviation Log, and Semantic Validation Policy to formalize governance within the schema
Details
The article discusses how earlier versions of MICA (v0.1.0 to v0.1.4) made the schema more implementable by addressing issues like scoring semantics and encoding constraints. However, the governance details were still kept outside the schema, with things like who approved a change, under what conditions, and with what rationale being documented in READMEs, review checklists, and comments rather than being part of the schema itself. The author refers to this as the 'stake nobody pulled' - a metaphor for an assumption that governance lives outside the schema, even though it is not an immovable constraint. v0.1.5 of MICA introduced new concepts like the Provenance Registry, Deviation Log, and Semantic Validation Policy to formalize governance within the schema, making it inspectable and machine-evaluable.
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