JayCee — this framing is sharp. Your MRL schema maps closely to several governance constraints I’ve been working through operationally, especially around provenance, memory state, and authorization boundaries.

The user story below is the direction I’ve been developing. It effectively becomes part of the upstream data contract for MRLINPUT — particularly provenancestate and memory_binding — if admissibility decisions are going to be reliable enough for downstream action.

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I often write what I want in an agile user story. Here’s a draft aligned to your schema:

As a human operator building on frontier consumer agentic AI systems,

I want outputs to include structured provenance and context attribution,

so I can evaluate the reliability, admissibility, and governance posture of the response before acting on it.

The system should identify whether the output was influenced by:

- base model priors

- current session context

- persistent cross‑session memory

- personalized memory

- external retrieval sources

- uploaded documents

- tool calls and external APIs

- any additional memory or state not explicitly surfaced

The system should expose:

- provenance classification metadata

- declared memory posture

- externally cited vs internally inferred context

- memory‑derived influence vs probabilistic synthesis

Human‑readable auditability.

No proprietary model internals required.

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Feels like these systems eventually need to move from “confidence signaling” toward explicit admissibility and authority models. Hopeful thinking — but hope is not a governance framework.