🏛️ Council of High Intelligence: DSOM Adoption Proposal & Architectural Design
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.
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).
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:
- Full Mode (
--full): 4-phase adversarial protocol across all 18 members. Reserved for high-stakes, irreversible architectural choices. - Quick Mode (
--quick): Rapid 3-phase restate-analyze-stance sequence without an explicit cross-examination round. - Duo Mode (
--duo): Direct 2-member dialectic highlighting one core tension (e.g.Torvaldsvs.Meadows). - 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.
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
- Active State (MCP & Quick Query):
- Plans exposure of lightweight
run_counciltool capabilities (supporting--triad <domain>and--duoparameters) via FastMCP (tools/mcp/server.py). Target latency for localsearch_palacedispatches 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.
- 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. - Deep State (EOD Consolidation & Universal Ledger):
- Synthesises Full Council verdicts into spatial brain state files (
.agents/brain/task.mdand.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.pywithout 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
- Decision Field Notes: Saved under
.agents/brain/council_field_notes_<timestamp>.md. - Verdict Blueprints: Saved under
docs/governance/council_verdict_<timestamp>.mdor recorded in.agents/brain/walkthrough.md. - Outcome Ledger: Tracked in
HISTORY.mdand checked at scheduled EOD review dates.
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. |
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. |
+-----------------------------------------------------------------------------------+
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