mancitrus/system-prompts-and-models-of-ai-tools
1
0
Fork 0
mirror of https://github.com/x1xhlol/system-prompts-and-models-of-ai-tools.git synced 2026-06-17 23:09:35 +00:00
Code Issues Actions Packages Projects Releases Wiki Activity
fb6b34bcc0
system-prompts-and-models-o.../salesflow-saas/docs/ai-operating-model.md
Claude a319feb6d7
feat(dealix): complete Tier-1 Sovereign Enterprise Growth OS 
Governance layer (14 docs):
- MASTER_OPERATING_PROMPT.md — operating constitution (five planes, six tracks, policy classes)
- docs/ai-operating-model.md — five-plane architecture (Decision/Execution/Trust/Data/Operating)
- docs/dealix-six-tracks.md — six strategic tracks (Revenue/Intelligence/Compliance/Expansion/Operations/Trust)
- docs/governance/execution-fabric.md — OpenClaw execution plane deep dive
- docs/governance/trust-fabric.md — trust plane with contradiction engine + evidence packs
- docs/governance/saudi-compliance-and-ai-governance.md — PDPL/ZATCA/SDAIA/NCA live controls
- docs/governance/technology-radar-tier1.md — Core/Strong/Pilot/Watch/Hold classification
- docs/governance/partnership-os.md — alliance lifecycle management
- docs/governance/ma-os.md — M&A corporate development lifecycle
- docs/governance/expansion-os.md — geographic and vertical growth
- docs/governance/pmi-os.md — post-merger integration framework
- docs/governance/executive-board-os.md — executive decision surfaces
- docs/execution-matrix-90d-tier1.md — 90-day sprint execution plan
- docs/adr/0001-tier1-execution-policy-spikes.md — 8 architectural decisions

Backend (3 models, 6 services, 8 API routes):
- Contradiction Engine — detect/track system conflicts
- Evidence Pack System — tamper-evident audit proof with SHA256
- Saudi Compliance Matrix — live PDPL/ZATCA/SDAIA/NCA controls
- Executive Room — unified executive decision surface
- Connector Governance — integration health monitoring
- Model Routing Dashboard — LLM provider metrics
- Forecast Control Center — actual vs forecast across tracks
- Approval Center — enhanced approval queue with SLA

Frontend (9 components):
- Executive Room, Evidence Pack Viewer, Approval Center
- Connector Governance Board, Saudi Compliance Dashboard
- Actual vs Forecast Dashboard, Risk Heatmap
- Policy Violations Board, Partner Pipeline Board

Tooling:
- scripts/architecture_brief.py — preflight validation (40/40 checks pass)
- Updated CLAUDE.md and AGENTS.md with governance references

https://claude.ai/code/session_01W1rJthWDkasijTdXCfxVHs
2026-04-16 12:48:13 +00:00

8.7 KiB
Raw Blame History

Dealix AI Operating Model — Five-Plane Architecture

Parent: MASTER_OPERATING_PROMPT.md
Version: 1.0 | Status: Canonical
Tracks: All six tracks

Overview

Dealix separates concerns into five architectural planes. Each plane has a distinct responsibility, clear boundaries, and explicit contracts with adjacent planes.

┌─────────────────────────────────────────────────┐
│              DECISION PLANE                      │
│   Strategy · Forecasting · Memos · Evidence      │
├─────────────────────────────────────────────────┤
│              EXECUTION PLANE                     │
│   OpenClaw · Durable Flows · Agents · Celery     │
├─────────────────────────────────────────────────┤
│              TRUST PLANE                         │
│   Policy Gates · Approvals · Audit · Compliance  │
├─────────────────────────────────────────────────┤
│              DATA PLANE                          │
│   PostgreSQL · pgvector · Redis · Events · RAG   │
├─────────────────────────────────────────────────┤
│              OPERATING PLANE                     │
│   CI/CD · Monitoring · Self-Improvement · Flags  │
└─────────────────────────────────────────────────┘

1. Decision Plane

Purpose: Where strategic decisions are made, forecasts generated, and executive memos assembled.

Current State

Component File Status
Executive ROI Service services/executive_roi_service.py Live (basic)
Analytics Service services/analytics_service.py Live
Management Summary Agent ai-agents/prompts/management-summary-agent.md Live
Revenue Attribution Agent ai-agents/prompts/revenue-attribution-agent.md Live
Predictive Revenue services/predictive_revenue_service.py Live
Strategic Simulator services/strategic_deals/strategic_simulator.py Live
ROI Engine services/strategic_deals/roi_engine.py Live

Target State

Component Status
Executive Room (full aggregation) Building
Evidence Pack Assembly Building
Actual vs Forecast Control Center Building
Contradiction-aware decisioning Building
Board Pack Generator Planned

Structured Outputs

All Decision Plane outputs must be structured:

  • LeadScoreCard — qualification score + signals + recommendation
  • QualificationMemo — deal qualification with evidence
  • ProposalPack — pricing + terms + value proposition
  • ExecutiveSnapshot — KPIs + risks + pending decisions
  • EvidencePack — assembled proof for audit/board review
  • ForecastVariance — actual vs forecast with root causes

2. Execution Plane

Purpose: Where work gets done. Durable, checkpointed, retriable workflows.

Current State

Component File Status
OpenClaw Gateway openclaw/gateway.py Live
Durable Task Flow openclaw/durable_flow.py Live
Task Router openclaw/task_router.py Live
Policy Engine openclaw/policy.py Live
Approval Bridge openclaw/approval_bridge.py Live
Observability Bridge openclaw/observability_bridge.py Live
Hooks openclaw/hooks.py Live
Canary Context openclaw/canary_context.py Live
Plugins (5) openclaw/plugins/ Live
Agent Executor services/agents/ Live
Celery Workers workers/ Live
Sequence Engine services/sequence_engine.py Live

Execution Flow

Request → OpenClaw Gateway
  → Policy Gate (policy.py: A/B/C classification)
  → Observability (start run, trace)
  → Approval Bridge (if Class B: check approval_token)
  → Canary Context (if canary enforcement: tenant check)
  → Task Router (dispatch to registered handler)
  → Durable Flow (checkpoint state)
  → Agent Executor / Celery Task
  → Action Handler (DB write, message send, etc.)
  → Observability (finish run)

Target State

Component Status
Temporal for long-running workflows Watch
Compensation policies (rollback) Planned
Idempotency keys for all writes Planned
Dead letter queue with alerting Planned

3. Trust Plane

Purpose: Where governance is enforced. No sensitive action bypasses this plane.

Current State

Component File Status
Policy Classes (A/B/C) openclaw/policy.py Live
Approval Bridge openclaw/approval_bridge.py Live
Trust Score Service services/trust_score_service.py Live
Security Gate services/security_gate.py Live
Shannon Security services/shannon_security.py Live
PDPL Consent Manager services/pdpl/consent_manager.py Live
PDPL Data Rights services/pdpl/data_rights.py Live
Audit Service services/audit_service.py Live
Audit Log Model models/audit_log.py Live
Outbound Governance services/outbound_governance.py Live
Tool Verification services/tool_verification.py Live
Tool Receipts services/tool_receipts.py Live
SLA Escalation Alerts services/sla_escalation_alerts.py Live
Skill Governance services/skill_governance.py Live

Target State

Component Status
Contradiction Engine Building
Saudi Compliance Matrix (live controls) Building
OPA policy engine Watch
OpenFGA authorization graph Watch
Vault secrets governance Watch

4. Data Plane

Purpose: Where data lives, moves, and is enriched.

Current State

Component Status
PostgreSQL 16 + asyncpg Live
pgvector embeddings Live
Redis 7 (cache + broker) Live
Multi-tenant data isolation Live
Alembic migrations Live
Knowledge Service (RAG) Live
Domain Events Live
Integration Sync State Live
30+ SQLAlchemy models Live
Mem0 memory engine Live

Data Governance Rules

  1. All tables include tenant_id (via TenantModel base)
  2. Money fields use Numeric(12,2), never Float
  3. Timezone is Asia/Riyadh (UTC+3)
  4. Currency defaults to SAR
  5. Soft deletes via deleted_at field
  6. PII never stored in logs
  7. pgvector kept updated (security patches)
  8. No external RAG SaaS — PostgreSQL + pgvector + KnowledgeService only

Target State

Component Status
CloudEvents for event schema Planned
AsyncAPI for event documentation Planned
Data quality automated checks Planned
Lineage/catalog layer Watch

5. Operating Plane

Purpose: Where the system monitors, improves, and governs itself.

Current State

Component File Status
Observability services/observability.py Live
Self-Improvement Loop services/self_improvement.py Live
Feature Flags services/feature_flags.py Live
Go-Live Matrix services/go_live_matrix.py Live
Operations Hub services/operations_hub.py Live
GitHub Actions CI .github/workflows/dealix-ci.yml Live
Claude Commands .claude/commands/ Live
Claude Hooks .claude/hooks/ Live

Target State

Component Status
Architecture Brief preflight Building
Connector Governance Board Building
Model Routing Dashboard Building
OIDC authentication Planned
Artifact attestations Planned
Audit log external streaming Planned
Protected branch rulesets Planned

Plane Interaction Rules

  1. Decision → Execution: Decision Plane emits structured directives; Execution Plane processes them as tasks.
  2. Execution → Trust: Every execution step checks Trust Plane before performing sensitive actions.
  3. Trust → Data: Trust Plane reads audit logs and compliance state from Data Plane.
  4. Data → Operating: Operating Plane monitors Data Plane health and triggers alerts.
  5. Operating → All: Operating Plane can pause, resume, or rollback any plane component.

No plane bypasses Trust for Class B or C actions. This is enforced at the OpenClaw Gateway level.