0010 — Governance supporting docs #33

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Claude · 2026-06-28T17:11:09Z

Claude commented

2026-06-28 17:11:09 +00:00

What to build

Two supporting documentation tasks that can run in parallel with issue 0009:

1. Move governance reference documents to docs/

Move docs/research/governance_principles/ai-constitution.md and docs/research/governance_principles/HUMANS.md to docs/. These are human-facing reference documents — the full evidence base and the practitioner checklist — not agent instructions. They belong alongside VISION.md and ROADMAP.md, not in the research folder.

Update any cross-references between these files and the remaining research files (ai-governance-research.md, ai-governance-research-challenges.md, ai-governance-research-session.md, ai-agent-instructions-notes.md) to reflect their new paths. The research files stay in docs/research/governance_principles/ as the audit trail for the constitution.

2. Add governance domain language to CONTEXT.md

Add the following terms to the CONTEXT.md glossary so future chunks (skills, workflows, agent roles) resolve them consistently:

HITL (human-in-the-loop) — agent pauses before a consequential action; human approves before execution. Required for irreversible or high-stakes actions.
HOTL (human-on-the-loop) — agent acts; human monitors and can intervene after the fact. Acceptable for low-stakes, bounded, reversible actions.
Symbolic oversight — oversight implemented as a gesture (assigning a reviewer) rather than a functional safeguard. The documented failure mode: a reviewer without the information, time, agency, or intent to evaluate is not oversight.
Data classification tiers — the four-tier framework governing what data may enter AI context: Public (no restrictions), Internal (enterprise AI tools only), Confidential (enterprise AI with data-not-trained commitment), Restricted (never enters AI context — hard architectural prohibition).
Sycophancy — the failure mode where RLHF-trained models prioritise approval over accuracy. Treated as a first-class reliability risk: models change correct answers to wrong ones under user pressure and persist in the wrong answer. Designing against sycophancy is an explicit obligation, not a quality-of-life concern.

Acceptance criteria

docs/ai-constitution.md exists (moved from research folder)
docs/HUMANS.md exists (moved from research folder)
Neither file remains in docs/research/governance_principles/
Cross-references within the moved files point to their new paths
CONTEXT.md glossary contains entries for HITL, HOTL, symbolic oversight, data classification tiers, and sycophancy
Each glossary entry is precise and consistent with the definitions in docs/ai-constitution.md

Blocked by

None — can start immediately.

## What to build Two supporting documentation tasks that can run in parallel with issue 0009: **1. Move governance reference documents to `docs/`** Move `docs/research/governance_principles/ai-constitution.md` and `docs/research/governance_principles/HUMANS.md` to `docs/`. These are human-facing reference documents — the full evidence base and the practitioner checklist — not agent instructions. They belong alongside VISION.md and ROADMAP.md, not in the research folder. Update any cross-references between these files and the remaining research files (`ai-governance-research.md`, `ai-governance-research-challenges.md`, `ai-governance-research-session.md`, `ai-agent-instructions-notes.md`) to reflect their new paths. The research files stay in `docs/research/governance_principles/` as the audit trail for the constitution. **2. Add governance domain language to `CONTEXT.md`** Add the following terms to the `CONTEXT.md` glossary so future chunks (skills, workflows, agent roles) resolve them consistently: - **HITL** (human-in-the-loop) — agent pauses before a consequential action; human approves before execution. Required for irreversible or high-stakes actions. - **HOTL** (human-on-the-loop) — agent acts; human monitors and can intervene after the fact. Acceptable for low-stakes, bounded, reversible actions. - **Symbolic oversight** — oversight implemented as a gesture (assigning a reviewer) rather than a functional safeguard. The documented failure mode: a reviewer without the information, time, agency, or intent to evaluate is not oversight. - **Data classification tiers** — the four-tier framework governing what data may enter AI context: Public (no restrictions), Internal (enterprise AI tools only), Confidential (enterprise AI with data-not-trained commitment), Restricted (never enters AI context — hard architectural prohibition). - **Sycophancy** — the failure mode where RLHF-trained models prioritise approval over accuracy. Treated as a first-class reliability risk: models change correct answers to wrong ones under user pressure and persist in the wrong answer. Designing against sycophancy is an explicit obligation, not a quality-of-life concern. ## Acceptance criteria - [x] `docs/ai-constitution.md` exists (moved from research folder) - [x] `docs/HUMANS.md` exists (moved from research folder) - [x] Neither file remains in `docs/research/governance_principles/` - [x] Cross-references within the moved files point to their new paths - [x] `CONTEXT.md` glossary contains entries for HITL, HOTL, symbolic oversight, data classification tiers, and sycophancy - [x] Each glossary entry is precise and consistent with the definitions in `docs/ai-constitution.md` ## Blocked by None — can start immediately.

Claude added this to the Legacy / Triage milestone 2026-06-28 17:11:09 +00:00

Claude closed this issue

2026-06-28 17:11:13 +00:00

Claude referenced this issue

2026-06-28 17:14:45 +00:00

refactor: align repo with marketplace/plugin model — migrate docs and issues to Gitea, rewrite specs, clean up stale artifacts #15

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Reference: Defame1297/holocron#33