holocron/docs/research/governance_principles/ai-governance-research-session.md

AI Governance Research — Session Audit Trail

Purpose: Auditability of the artifact creation process. Documents what was done, how, why, and what decisions were made or deferred. Not a task list — a process record.

Project: AI governance research and artifact creation for a software development, deployment, and infrastructure management context. Sessions: Three sessions, May 2026. Artifacts produced: See artifact registry below.

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Project Overview

The goal was to produce a governance foundation before building an AI-assisted software development repository. The author wanted principles grounded in research — not opinion — that would govern all future AI implementation and deployment.

The work was deliberately sequenced: research first, then distil into operative governance. The research must be independently challengeable; the governance must be applicable at both homelab and enterprise scale.

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Methodology

Research approach

• The instruction throughout was to use reliable web sources only, not training data. Whether this was fully achieved cannot be independently verified — some findings may reflect training data rather than sourced research, particularly in analytical or synthesis sections where citations are sparse.
• Research is conducted topic by topic so findings can be challenged before they harden into principles.
• All hypotheses established during scoping are provisional. Evidence drives conclusions; if research contradicts a prior stance, the constitution reflects the evidence, not the hypothesis.
• Bias flags were intended to reference limitations within the sourced research itself (vendor-funded studies, single-study findings, Western regulatory framing, etc.) — not the AI assistant's training data. However, the line between "synthesising sourced findings" and "drawing on training data" is not always clear, and some analytical conclusions may have training data influence that went undetected.

Output format per topic

Each topic in the research document follows: question being researched → findings → counterarguments and challenges → bias flag → provisional principles.

Distillation logic

Research document = full sourced reasoning (human reference, never in agent context). Constitution = concise principles derived from research (agent-readable, repo artifact). AGENTS.md = agent-actionable subset of the constitution, optimised for context window efficiency.

Standing integrity caveat

The instruction to use web sources only was given and followed to the best of the session's ability. However, AI-generated research cannot self-audit for training data influence with certainty. Analytical framing, the choice of which findings to emphasise, and the synthesis of sources into conclusions all carry some risk of undetected training data influence. The deep research session in Session 2 provided one independent cross-check. Where citations are absent or thin, treat findings with additional scepticism and verify independently before relying on them for high-stakes decisions. Any claim found to be unsourced during the session is corrected with a new web search before proceeding. Any claim refuted by subsequent research is retracted and corrected in the relevant artifacts.

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Scope Decisions

These decisions were made during an initial scoping/interview session before research began. Each shaped all subsequent work.

Decision	Conclusion	Rationale
Output form	Two artifacts: research doc + constitution	Separates reasoning from operative governance; keeps constitution context-efficient
Audience	Solo now, team-inheritable later	Every design choice must be legible without the author present
Provider / infra scope	Agnostic across tools and stacks	Will be used by others; cannot be tool-locked
Research depth	Thorough, sourced, with tradeoffs	Principles are only as strong as the evidence behind them
Constitution depth	Concise and opinionated	If it can't be read and acted on quickly, it won't be used
Format	Markdown for all artifacts	Portable, versionable, agent-readable, renders in GitHub
Data classification	Explicit rules in constitution AND tooling	Tooling enforces but does not replace governance
Automation philosophy	Automate as much as responsibly possible; humans own the code	Automation is the goal; comprehension is the constraint
Research sequencing	Topic by topic, user challenges before proceeding	Prevents hypotheses hardening before they are tested

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Provisional Hypotheses (established pre-research)

The following were the author's starting positions before research began. They were tested, not assumed.

Hypothesis	Outcome
Sycophancy is the root cause of hallucination	Partially confirmed. Sycophancy is a major and tractable cause (14.66% regressive, 78.5% persistence under pressure). Not the sole cause — training data gaps and decoding strategies are independent contributors.
AI is currently a net sustainability negative	Confirmed in societal terms. Reframed from business ROI to societal/global: environmental cost (415 TWh, 105M+ tonnes CO₂e), power concentration, epistemic harm at scale, deskilling, value extraction without consent.
Human code ownership is non-negotiable	Confirmed, standard refined. Line-by-line comprehension is not the right bar. The ACM/IEEE-CS standard is: intent-level + architectural + verifiable behaviour understanding.
Data classification belongs in governance, not just tooling	Confirmed. EDPB Opinion 28/2024 and CNIL guidance support explicit governance-layer classification rules independent of tooling.

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Session 1 — Research and Initial Artifact Creation

What was done

1. Scoping session (grill-me): Established purpose, audience, methodology, and the scope decisions documented above. Three rounds of questions, answered one by one. Output: shared understanding of what to build and why.

2. Topic-by-topic research: Ten governance topics researched in sequence. Each topic was web-searched, synthesised, and reviewed before proceeding to the next. User challenged findings at each step.

3. Mid-session methodology correction: During Topics 1–3, bias flags incorrectly referenced "my training data" as a source. The user caught this. Corrected: bias flags now reference limitations within the sourced research itself. Affected sections in Topics 1, 2, and 3 were re-researched and corrected.

4. Topic 10 addition: After Topics 1–9 were complete and the constitution was drafted, the user requested an additional topic: preferring deterministic code execution over repeated AI inference. Topic 10 was researched and added to all artifacts.

5. Artifact distillation: Research document → constitution → AGENTS.md → agent instructions notes. Each layer is a deliberate reduction: research has full sourcing, constitution has principles, AGENTS.md has only what an agent can act on in the moment.

6. Artifact audit: All artifacts reviewed for internal consistency, topic count (nine → ten), cross-references, and missing information. Corrections made:

   • Methodology note in session doc corrected (training data reference)
   • Topic count updated throughout
   • AGENTS.md cross-referenced from constitution and handoff
   • Agent instructions notes created to document design decisions behind AGENTS.md

Topics researched

#	Topic	Key finding
1	Ethics	Accountability non-transferable; ethics washing documented; sycophancy is an ethical failure mode not just a quality one
2	Security	OWASP LLM Top 10 + Agentic Top 10 as baseline; secrets leakage 40% higher in AI-assisted repos; prompt injection actively exploited in CI/CD
3	Data protection & classification	GDPR + EU AI Act apply concurrently; context window is a data store; enterprise vs consumer tier is a hard distinction
4	Sustainability	⚠️ Requires re-research — see note below
5	Behaviour & sycophancy	14.66% regressive sycophancy; 78.5% persistence; confident language inversely correlated with accuracy
6	Human oversight	EDPS four conditions for genuine oversight; automation bias in 35 peer-reviewed studies; responsibility vacuum at scale
7	Transparency	EU AI Act logging requirements; agentic audit gap; prompt versioning as governance infrastructure
8	Intellectual property	No copyright without human authorship; licence contamination risk; dependency-hallucination as separate risk channel
9	Incident response	Deployment failure, not model failure, is the primary cause; CoSAI IR Framework v1.0; notification timelines
10	Deterministic execution	Break-even at ~17 invocations; EffiBench execution overhead; PAL/CodeAct as established patterns

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Session 2 — Deep Research and Corrections

What was done

1. Deep research session launched: An extended search task was used to independently challenge all 11 documented research challenges (10 topics + AGENTS.md reliability). The task searched for independent peer-reviewed sources to verify, refute, or nuance prior findings.

2. Deep research findings reviewed: Of 11 challenges: 2 confirmed, 6 partially confirmed, 3 refuted. Key corrections identified (see below).

3. Scope clarification — sustainability reframe: The user clarified that the "net sustainability negative" framing should be societal and global — not just business ROI. This reframe was agreed in principle but the societal dimensions (environmental externality, power concentration, epistemic harm at scale, deskilling, value extraction) were not independently researched in this session. They represent a direction for the next research pass, not verified findings.

4. Applicability requirement established: All artifacts must be applicable at homelab scale and enterprise scale. Nothing should require a compliance department, a committee, or enterprise tooling to act on.

5. Next-session handoff created: Rather than executing all corrections immediately in a session that was running long, a focused handoff document was created with three clear instructions for a fresh session.

6. Partial constitution rewrite abandoned: An attempt was made to execute corrections to the constitution during this session. This was cut off and the partial changes were reverted.

7. Constitution restored to v1.0: The constitution was reverted to the original research-based version. The backup was then deleted.

8. Cleanup: An over-detailed adjustment plan document created mid-session was deleted. All references to it and to the backup file were removed from other artifacts.

Deep research verdict summary

Challenge	Verdict	Key correction
1. Sycophancy as root cause	Partially confirmed	Correct figure: 14.66% regressive (not 59%); venue AIES 2025 not AAAI; sycophancy is one of several RLHF failure modes, not the sole cause
2. AI as net sustainability negative	Refuted as stated (business framing)	Business ROI evidence is weak/vendor-sourced; societal framing is the correct and better-evidenced argument
3. Human code ownership	Refuted as stated	Line-by-line standard has no professional authority; ACM/IEEE-CS standard is intent + architecture + verifiable behaviour
4. EU AI Act applicability	Refuted as stated	August 2026 deadline is wrong (Omnibus May 2026 moved dates); coding assistants are minimal/limited risk; only Article 4 applies to developers using tools
5. Data classification sufficiency	Partially confirmed	Four tiers necessary but insufficient; add lifecycle stage, AI-risk class, data quality dimensions (ISO 42001/23894/5259)
6. OSS licence contamination	Partially confirmed	"17%" figure has no primary source — drop it; Ciniselli et al.: 0.1–10% verbatim clones; Doe v. GitHub on interlocutory appeal, not settled
7. Incident response playbooks	Partially confirmed	CoSAI v1.0 exists (Nov 2025); OWASP Agentic Top 10 (Dec 2025); notification timelines confirmed from EU AI Act Article 73
8. Transparency disclosure scope	Refuted as stated	Article 50 is provider-side, not deployer-side for coding assistant users; internal tooling has no Article 50 obligation
9. Model selection for sycophancy	Refuted as stated	Anthropic/Llama advantage is not confirmed by independent benchmarks; rankings flip across evaluations
10. Deterministic execution boundary	Partially confirmed	"57×" is a single vendor preprint; replace with break-even ~17 invocations; EffiBench gives exact overhead figures
11. AGENTS.md reliability	Partially confirmed	"Hard prohibitions most reliable" is refuted; Semantic Gravity Wells (2026): negative constraints fail via priming ~87.5% of the time; pair every "Never X" with "— instead do Y"

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Key Decisions Log

Decision	Why
Web research only — no training data	Training data is an uncontrolled source with unknown biases. Web research from named sources is auditable and challengeable.
Two artifacts (research + constitution), not one	A combined document would be too long for agent context. Separation also clarifies purpose: research is "why", constitution is "what to do".
AGENTS.md as a third artifact	The constitution is still too long for global agent context. A further distillation to agent-actionable-only reduces context cost without losing governance depth.
Single source of truth + thin adapters	Copying AGENTS.md content into CLAUDE.md, copilot-instructions.md etc. creates maintenance debt. One source, referenced by tool-specific files.
Topic-by-topic with user challenge	Prevents a batch of unchallenged principles from hardening into governance. User review at each step is the quality gate.
Topic 10 (deterministic execution) added after initial draft	The principle — use AI to write scripts, not to execute repeated tasks — is a meaningful governance decision that wasn't captured in the original nine topics. Worth its own section.
Deep research before applying corrections	Corrections based on a single research pass might introduce new errors. An independent challenge pass reduces that risk.
Constitution NOT updated in Session 2	The session was interrupted mid-execution. A partial update is worse than no update. Deferred to a clean next session with a clear handoff.
Societal/global framing for sustainability	Business ROI evidence is vendor-sourced and weak. The societal argument — environmental externality, power concentration, epistemic harm, deskilling — is stronger, more independent, and more principled.
Applicability at homelab and enterprise	The author works across both scales. A principle that only applies at enterprise scale is not a principle for this context — it is aspirational.

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Corrections and Integrity Notes

What was wrong	When caught	How corrected
Bias flags referenced "my training data" as a source	Session 1, during Topic 1–3 review	User caught it. Methodology instruction clarified: bias flags should reference limitations within the sourced research, not training data. Affected topics re-searched and rewritten. Whether training data influence was fully eliminated across all topics remains uncertain — the deep research pass in Session 2 was one independent check, but cannot guarantee it.
Topics 1–3 had unsourced claims in analysis sections	Session 1	Targeted web searches filled identifiable gaps. Unsourced sections were replaced with cited findings where the gap was visible. Unsourced influence that was not visibly identifiable may remain.
SycEval "59% regressive sycophancy, AAAI"	Session 2 deep research	Correct: 14.66% regressive, 78.5% persistence, AIES 2025. Not yet applied to artifacts — deferred to next session.
Larridin "72% destroying value" cited as evidence	Session 2 deep research	Identified as vendor-commissioned survey. Dropped. Not yet removed from artifacts — deferred.
EU AI Act "August 2026" deadline	Session 2 deep research	Omnibus May 2026 moved dates. Correct dates established. Not yet applied — deferred.
"Hard prohibitions are most reliable" in AGENTS.md	Session 2 deep research	Refuted by Semantic Gravity Wells (2026). Prohibitions must be paired with positive replacements. Not yet applied — deferred.
Anthropic/Llama sycophancy advantage	Session 2 deep research	Refuted by MASK benchmark and others. Not yet applied — deferred.
"17% OSS contamination" figure	Session 2 deep research	No primary source found. Drop and replace. Not yet applied — deferred.
"57× token reduction"	Session 2 deep research	Single vendor preprint. Replace with break-even ~17 invocations. Not yet applied — deferred.
Doe v. GitHub "settled"	Session 2 deep research	On interlocutory appeal; not settled. Not yet applied — deferred.
Constitution v1.1 partial rewrite	Session 2	Abandoned mid-execution; reverted to v1.0. Clean corrections deferred to next session.

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Artifact Registry

Current state of all artifacts as of end of Session 3.

File	Version	Purpose	Status
ai-constitution.md	1.1	Governance principles derived from research. Repo artifact, agent-readable.	Complete. All instructions applied.
AGENTS.md	1.1	Operative agent instructions. Concise, agent-actionable distillation of the constitution.	Complete.
ai-governance-research.md	1.1	Full research document. Sourced findings, counterarguments, bias flags, provisional principles for all 10 topics.	Complete. Preamble, table, cross-refs, Topic 4 rewrite applied.
ai-agent-instructions-notes.md	1.0	Design rationale for AGENTS.md. Instruction following research, deployment pattern, update policy.	Priming failure finding (Semantic Gravity Wells) incorporated
ai-governance-research-challenges.md	1.0	11 deep research challenges with verdicts and recommended adjustments.	Reference only. No changes needed.
ai-governance-research-session.md	3.0	This document. Audit trail of the creation process.	Current
next-session-handoff.md	2.0	Instruction status tracker. Instruction 1 complete; 2 and 3 pending.	Current

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Session 3 — Apply Instruction 1 Corrections

What was done

Applied all critical factual corrections from the deep research pass (Session 2) across constitution, AGENTS.md, research doc, and agent notes. Then executed Instruction 2 (sustainability research and reframe) and Instruction 3 (operationalisation assessment) in the same session.

Instruction 1 changes applied per artifact:

ai-constitution.md (→ v1.1)

• EU AI Act dates corrected throughout (Omnibus May 2026): Art. 50 → Dec 2, 2026; Art. 4 (AI literacy) live since Feb 2025; scope clarified to coding assistant users = minimal/limited risk.
• Sycophancy model selection: removed unconfirmed Anthropic/Llama advantage; replaced with portfolio benchmark approach (MASK, SYCON-Bench, SycEval) and run-your-own-test guidance.
• Human code ownership: replaced line-by-line comprehension standard with ACM/IEEE-CS standard (intent + architecture + verifiable behaviour).
• Deterministic execution: removed unsourced 2.5–68× slower claim; replaced with EffiBench reference and correct framing.
• DPIA: homelab exemption note added then removed (Instruction 3 decision — scale-agnostic treatment).

AGENTS.md (→ v1.1)

• Every "Never X" rule paired with "— instead do Y" throughout, per Semantic Gravity Wells (2026) priming failure finding.

ai-governance-research.md

• SycEval: 59% → 14.66% regressive, AAAI → AIES 2025.
• Larridin 72% dropped; replaced with S&P Global and MIT NANDA figures.
• METR 19% slower updated with Feb 2026 correction.
• EU AI Act dates corrected in both instances.
• 17% OSS contamination dropped; replaced with Ciniselli et al. 0.1–10%.
• 57× token reduction flagged as single vendor preprint; break-even ~17 invocations elevated.
• Anthropic/Llama sycophancy advantage removed.

ai-agent-instructions-notes.md

• Semantic Gravity Wells finding incorporated; design decision updated.

Instruction 2 changes:

ai-governance-research.md

• Topic 4 fully rewritten as "Sustainability & Societal Cost" with independent web research (May 2026).
• IEA April 2025/2026 as primary energy source. Inequality paradox (Chen & Meng 2026). Epistemic harm confirmed. Deskilling evidence. Labour effects early and contested.
• Core reframe: AI costs are externalised to non-users; governance is an obligation to those who bear them.

ai-constitution.md

• Section 6 rewritten with societal/externality framing. ⚠️ warning removed.

Instruction 3 changes (grilling session):

ai-constitution.md

• DPIA homelab exemption removed — consistent scale-agnostic treatment across all principles
• Governance section: brief example mapping added (secrets principle → 4-layer enforcement stack)
• Section 9, Section 8, GDPR notification: left as-is (scale-agnostic; tool-agnostic; technically correct)

ai-governance-research.md (→ v1.1)

• Preamble rewritten: "how to use" framing distinguishing it from the constitution
• Summary table added: Topic → Core finding → Principle count → Constitution section
• Cross-reference lines added to all 10 topics' provisional principles

ai-agent-instructions-notes.md (→ v1.1)

• Deployment section expanded: global vs per-repo trade-offs, path handling, @import syntax, symlink approach, Copilot/Cursor limitation noted
• Claude Code adapter updated to use @AGENTS.md import pattern

AGENTS.md, ai-governance-research-challenges.md: No changes in Instruction 3

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Post-Instruction-3 Gap Analysis — Same Session

After completing all three instructions, a cross-reference check of the constitution and AGENTS.md against the sustainability research findings identified three gaps the instructions hadn't addressed:

Gap 1 — Deskilling / cognitive dependency (Constitution §5 added) Evidence (Kosmyna 2025 neural disengagement, medical domain atrophy, ACM FAccT 2026 "Brainrot" paper) was solid enough to warrant an explicit principle. Added to Section 5: "AI assistance must augment human capability, not replace it — governance must include mechanisms to detect skill atrophy in AI-assisted roles." Added as Principle 7 to Topic 6 provisional principles in research doc.

Gap 2 — Homogenisation / diverse perspectives (Constitution §4 added) AI systems suppress annotator disagreements, producing majority-weighted outputs on contested questions (arxiv 2505.07772). Added to Section 4: prompting principle for explicitly requesting multiple viewpoints and dissenting positions on contested or values-laden questions. Added as Principle 7 to Topic 5 provisional principles in research doc. Topic index table updated (5 and 6 now 7 principles each).

Gap 3 — AGENTS.md Restricted table row (already resolved) The "instead do Y" pointer was already present from an earlier edit. No change needed.

Agent notes updated: "Excluded" section now explicitly records why deskilling monitoring and diverse perspectives prompting are in the constitution but not AGENTS.md, with design rationale for each.

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Next Session

All instructions complete and gap analysis done. The artifact set is in its final researched, corrected, and operationalised state.

If continuing: consider whether any new research has emerged that warrants updating specific topics, or whether the constitution is ready to be committed to a repository and put into active use.

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Post-Gap-Analysis Updates — Same Session

After the post-Instruction-3 gap analysis, CONTROLS.md was created and gaps in AGENTS.md and HUMANS.md were resolved.

CONTROLS.md created (new artifact) Deterministic enforcement layer specifying pre-commit hooks, CI gates, licence scanning, audit logging, AI agent permission scoping, and ongoing verification cadences. Structured by setup phase (day-one / per-repo / ongoing). Tool-agnostic declarative requirements. Covers the enforcement layer that neither agent instructions nor human practitioner rules can provide.

AGENTS.md updated:

• Data minimisation in agent context added to Data Classification section: limit scope to what the task requires when accessing files or data
• Token efficiency added to Prompt and model hygiene: use minimum tokens necessary
• "What This File Does Not Govern" updated to reference CONTROLS.md as the deterministic enforcement layer
• Footer updated to reference HUMANS.md and CONTROLS.md

HUMANS.md updated:

• AI-generated crypto added to Hard Limits: never use AI-generated passwords/keys/secrets
• Data minimisation added to Before section: send only what the task requires
• Deterministic execution added to During section: for repeatable tasks, generate a script; ~17-invocation break-even noted
• Output volume limit added to During section: manage throughput to what you can genuinely evaluate
• Provider IP terms review added to After section: verify before commercial use
• "What This File Does Not Govern" updated to reference CONTROLS.md
• Footer updated to reference all three operative files

Constitution footer updated: now references AGENTS.md, HUMANS.md, and CONTROLS.md as the three operative files.

Agent notes updated: CONTROLS.md added as third item in "Excluded" section with design rationale.

Final artifact set: eight files ai-constitution.md | AGENTS.md | HUMANS.md | CONTROLS.md | ai-governance-research.md | ai-agent-instructions-notes.md | ai-governance-research-challenges.md | ai-governance-research-session.md