Council of High Intelligence: DSOM Adoption Proposal & Architectural Design

Architectural blueprint and governance proposal for adopting the 18-member Council of High Intelligence multi-perspective deliberation framework into th…

Entry Point 24: This document serves as the Master Governance Proposal for adopting structured multi-perspective deliberation into the Deep State of Mind (DSOM) framework. See START-HERE.md for the master onboarding roadmap.

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Executive Summary & Feasibility Affirmation

”Can we adopt this into DSOM?”

Yes, unequivocally. Adopting the Council of High Intelligence framework (0xnyk/council-of-high-intelligence) into Deep State of Mind (DSOM) is not only feasible, but represents a natural evolutionary step for DSOM’s Metacognition & Guardrails Subsystem and Tri-Phasic Mind Architecture.

While single-model outputs often suffer from overconfident hallucinations, vendor bias, and single-lens anchoring, DSOM is built on digital sovereignty, structured metacognition, and Git-native auditability. Incorporating an 18-member persona deliberation council directly enhances DSOM’s decision boundary for high-stakes architectural choices, infrastructure migrations, security posture reviews, and strategic trade-offs.

Furthermore, under OKF v0.2, council deliberations incorporate Attested Computations (type: Attested Computation), bridging definition and execution contracts via runtime bindings, parameters, and deterministic attesters across a 6-step lifecycle (Discover, Load, Parameterize, Execute, Attest, Gate).

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1. Deconstruction of Council of High Intelligence

The Council of High Intelligence is an open-source decision-making framework designed to replace single-model reasoning with structured, multi-perspective deliberation across multi-LLM provider backends.

+-----------------------------------------------------------------------------------+
|                        COUNCIL OF HIGH INTELLIGENCE                               |
|                                                                                   |
|  +------------------+   +-----------------------+   +--------------------------+  |
|  | Phase 1: Blind   |   | Phase 2: Cross-       |   | Phase 3: Final Stance    |  |
|  | Analysis         |-->| Examination & Dissent |-->| & Domain-Weighted Tally  |  |
|  +------------------+   +-----------------------+   +--------------------------+  |
|                                                                  |                |
|                                                                  v                |
|                                                     +--------------------------+  |
|                                                     | Phase 4: Verdict         |  |
|                                                     | Blueprint & Attestation  |  |
|                                                     | Ledger Entry             |  |
|                                                     +--------------------------+  |
+-----------------------------------------------------------------------------------+

1.1 The 18 Analytical Persona Lenses

Rather than broad impersonation, personas serve as grounded analytical instruments with specific methods, counterweights, and blind spots:

Member Persona	Primary Analytical Lens	Counterweight Lens	Key Operational Method
Aristotle	Categories & Formal Structure	Lao Tzu (Emergence)	Systematic classification & syllogism
Socrates	Premise & Assumption Destruction	Richard Feynman (First Principles)	Dialectic questioning & elenchus
Sun Tzu	Terrain, Positioning & Strategy	Marcus Aurelius (Moral Cost)	Strategic leverage & risk minimization
Ada Lovelace	Formal Systems & Abstraction	Machiavelli (Informal Power)	Mathematical limits & structural purity
Marcus Aurelius	Resilience, Duty & Moral Clarity	Sun Tzu (External Competition)	Stoic locus of control & duty
Machiavelli	Incentives, Power & Realpolitik	Ada Lovelace (Formal Consistency)	Unvarnished incentive analysis
Lao Tzu	Non-action, Emergence & Flow	Aristotle (Explicit Categories)	Frictionless systems & subtle dynamics
Richard Feynman	Empirical Debugging & Clarity	Socrates (Premise Questioning)	Plain explanation & physics-first principles
Linus Torvalds	Shipping, Pragmatism & Code Maintainability	Donella Meadows (System Dynamics)	Practical maintainability & pragmatic execution
Miyamoto Musashi	Timing, Precision & Decisive Action	Linus Torvalds (Early Action)	Timing, readiness & ruthless focus
Alan Watts	Reframing & False Dichotomies	Linus Torvalds (Concrete Code)	Unmasking false premises & double binds
Andrej Karpathy	Empirical ML Behaviour & Iteration	Ilya Sutskever (Frontier Safety)	Observation-driven empirical validation
Ilya Sutskever	Scaling Laws & Frontier AI Safety	Andrej Karpathy (Smooth Trends)	Tail risks in AI scaling & safety boundaries
Daniel Kahneman	Cognitive Bias & Heuristics	Richard Feynman (Causal Logic)	System 1 vs. System 2 bias detection
Donella Meadows	Feedback Loops & System Interventions	Linus Torvalds (Local Fixes)	Leverage points & systemic root causes
Charlie Munger	Inversion & Mental Model Lattices	Aristotle (Single Classification)	Latticework inversion & multi-disciplinary fit
Nassim Taleb	Antifragility, Tail Risk & Asymmetry	Andrej Karpathy (Empirical Trends)	Convex payoff analysis & fragility tests
Dieter Rams	User Clarity, Essentialism & Restraint	Ada Lovelace (Formal Abstraction)	Less, but better; clutter removal

1.2 Deliberation Modes & Domain Triads

To balance decision depth against latency and token consumption, the framework provides four modes:

1. Full Mode (--full): 4-phase adversarial protocol across all 18 members. Reserved for high-stakes, irreversible architectural choices.
2. Quick Mode (--quick): Rapid 3-phase restate-analyze-stance sequence without an explicit cross-examination round.
3. Duo Mode (--duo): Direct 2-member dialectic highlighting one core tension (e.g. Torvalds vs. Meadows).
4. Triad Mode (--triad <domain>): Specialized 3-member sub-panels routed dynamically across 17 domain presets:
   • architecture, strategy, ethics, debugging, risk, shipping, product, founder, ai, ai-product, ai-safety, decision, systems, uncertainty, design, economics, bias.

1.3 Protocol Protections & Output Standards

• Blind First Phase: Members evaluate the problem independently to eliminate first-speaker bias and groupthink anchoring.
• Forced Dissent & Cross-Examination: Protocol checks look for premature agreement, repeated claims, and unsupported confidence.
• Evidence Labeling Standard:
  • FACT: Direct evidence present in codebase or verified data.
  • INFERENCE: Deductive conclusion following from facts.
  • ASSUMPTION: Required premise currently unverified.
  • UNKNOWN: Missing information that could alter the verdict.
• Decision Field Notes: Mandatory pre-deliberation document separating knowns, assumptions, and reversibility boundaries before convening personas.
• Verdict Blueprint: Output leads with unresolved questions, followed by recommendations, kill criteria, acceptable compromises, and concrete next actions.
• Outcome Ledger Checkpoint: Post-decision predictions are recorded with review dates to audit outcome validity (confirmed, revised, reversed, inconclusive).
• Multi-Provider Seat Routing: Seats are assigned across available CLI backends (Claude Code, OpenAI Codex, Gemini CLI, Ollama, NVIDIA NIM, Cursor) so opposing polarities do not reside on the same model family.

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2. Deep State of Mind (DSOM) Integration Architecture

Integrating the Council framework into DSOM harmonises multi-perspective deliberation with DSOM’s core pillars:

+-----------------------------------------------------------------------------------+
|                        DSOM TRI-PHASIC MIND INTEGRATION                           |
|                                                                                   |
|  +---------------------------+   +---------------------------+                    |
|  | Active State (MCP Server) |-->| Fast Triad Queries &      |                    |
|  | (Target Latency < 50ms)   |   | Quick Decision Prompts    |                    |
|  +---------------------------+   +---------------------------+                    |
|                                                |                                  |
|                                                v                                  |
|  +---------------------------+   +---------------------------+                    |
|  | Twilight State            |-->| Linter, Token Gate &      |                    |
|  | (Verification & Audits)   |   | Pre-Commit Interception   |                    |
|  +---------------------------+   +---------------------------+                    |
|                                                |                                  |
|                                                v                                  |
|  +---------------------------+   +---------------------------+                    |
|  | Deep State                |-->| Full 18-Member Deliberation|                    |
|  | (EOD Consolidation)       |   | & Outcome Ledger Sync     |                    |
|  +---------------------------+   +---------------------------+                    |
+-----------------------------------------------------------------------------------+

2.1 Alignment with the Tri-Phasic Mind & Attested Computations

1. Active State (MCP & Quick Query):
   • Plans exposure of lightweight run_council tool capabilities (supporting --triad <domain> and --duo parameters) via FastMCP (tools/mcp/server.py). Target latency for local search_palace dispatches is under 50ms for standard local payloads.
   • Allows AI IDEs (Cursor, Claude Desktop, Jules) to execute rapid multi-angle sanity checks during active coding sessions.
2. Twilight State (Safety & Verification):
   • Intercepts council executions via DSOM Guardrails & Byte-Capped Execution framework to prevent token runaway.
   • Enforces evidence labeling (FACT, INFERENCE, ASSUMPTION, UNKNOWN) before accepting conclusions.
3. Deep State (EOD Consolidation & Universal Ledger):
   • Synthesises Full Council verdicts into spatial brain state files (.agents/brain/task.md and .agents/brain/walkthrough.md).
   • Records universal audit trail milestones in project ledgers (HISTORY.md, CHANGELOG.md, README.md).

2.2 Adopted DSOM Skill Component (.agents/skills/council-of-high-intelligence/SKILL.md)

An OKF v0.2 compliant execution manual is established under .agents/skills/council-of-high-intelligence/SKILL.md:

---
okf_version: 0.2
type: agent_skill
title: "Council of High Intelligence Multi-Agent Consensus Engine"
timestamp: "2026-09-19T00:00:00Z"
description: "Executes a multi-perspective deliberation protocol using specialised AI domain personas..."
topics: ["council", "multi-agent", "consensus", "deliberation", "antigravity"]
name: council-of-high-intelligence
spec_version: "0.2"
status: stable
stale_after: "2027-01-01"
---

2.3 Planned Zero-Binary Python Emulator (tools/council_emulator.py)

In compliance with Rule 27 (Native OpenWiki Emulator & Zero-Binary Mandate), DSOM plans to implement a native, zero-dependency Python utility tools/council_emulator.py:

• Pure Python Execution: Runs via uv run python tools/council_emulator.py without requiring external Node.js packages or UAC elevation.
• Provider Auto-Detection: Checks local CLI tools (gemini, codex, ollama) and API keys (ANTHROPIC_API_KEY, OPENAI_API_KEY, GEMINI_API_KEY, NVIDIA_API_KEY).
• Proposed CLI Invocations:
  # Run a full 18-member council deliberation
  uv run python tools/council_emulator.py --full "Should we migrate from REST to FastMCP?"
  
  # Run a domain triad deliberation
  uv run python tools/council_emulator.py --triad architecture "Should we adopt Quadlets or Ansible?"
  
  # Run a two-member dialectic
  uv run python tools/council_emulator.py --duo torvalds meadows "Is this abstraction layer worth maintaining?"
  

2.4 Proposed FastMCP Server Tool Binding (tools/mcp/server.py)

The Council engine is planned for direct exposure as an MCP tool in tools/mcp/server.py:

@mcp.tool()
def run_council(question: str, mode: str = "quick", triad: str = None, members: str = None) -> str:
    """Executes multi-perspective AI deliberation using Council of High Intelligence personas."""
    # Invokes internal Council Deliberation Engine

2.5 Spatial Memory & Ledger Sync

1. Decision Field Notes: Saved under .agents/brain/council_field_notes_<timestamp>.md.
2. Verdict Blueprints: Saved under docs/governance/council_verdict_<timestamp>.md or recorded in .agents/brain/walkthrough.md.
3. Outcome Ledger: Tracked in HISTORY.md and checked at scheduled EOD review dates.

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3. Token Efficiency, Trade-Offs & Mitigation Strategies

Running 18 distinct persona evaluations across multiple LLM calls introduces significant context and token overhead. DSOM mitigates this through three token-conservation layers:

Challenge / Risk	Impact	DSOM Mitigation Mechanism
Token Inflation (18x Overhead)	Heavy prompt token consumption in --full mode	Default to --triad <domain> (3 members) for daily development; limit --full to major architecture decision records (ADRs).
Local Latency	Sequential LLM API calls delay execution	Asynchronous concurrency via Python asyncio across multi-provider endpoints + local Ollama acceleration.
Vendor Anchoring	Single model provider dominates both sides	Multi-provider routing splits polarity pairs across distinct LLM families (Claude vs. Gemini vs. OpenAI).
Context Window Rot	Verbose raw discussion clutters chat context	Summary compaction via proposed tools/council_emulator.py returning only the structured Verdict Blueprint to the active agent window.

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4. Implementation Roadmap & Discussion Points

+-----------------------------------------------------------------------------------+
|                            IMPLEMENTATION ROADMAP                                 |
|                                                                                   |
|  Phase 1: Governance & Proposal Review [COMPLETED]                                |
|  - Approve architectural design & DSOM integration strategy.                      |
|                                                                                   |
|  Phase 2: Antigravity Skill Specification [COMPLETED]                             |
|  - Create `.agents/skills/council-of-high-intelligence/SKILL.md`.                 |
|                                                                                   |
|  Phase 3: Native Python Engine Development [PLANNED]                              |
|  - Implement `tools/council_emulator.py` zero-dependency runner.                  |
|                                                                                   |
|  Phase 4: FastMCP & OpenWiki Integration [PLANNED]                                |
|  - Add `run_council` tool to `tools/mcp/server.py`.                              |
|  - Integrate Council verdicts into OpenWiki knowledge graph.                      |
|                                                                                   |
|  Phase 5: Unit Testing & CI Verification [PLANNED]                                |
|  - Add test suite `tests/test_council_emulator.py`.                               |
|  - Verify navigation, links, and OKF frontmatter compliance.                      |
+-----------------------------------------------------------------------------------+

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Deep State of Mind (DSOM) For My AI Protocol | Harisfazillah Jamel (LinuxMalaysia) | 2026-09-19 Standard: UK English | DBP-standard Bahasa Melayu Malaysia (Piawai) | GNU General Public License v3.0