claude-flow-novice/deployment/csuite-decision-framework.md

Claude Flow Novice - Advanced orchestration platform for multi-agent AI workflows with CFN Loop architecture Includes Local RuVector Accelerator and all CFN skills for complete functionality.

# C-Suite Strategic Decision Framework

## Goal-Oriented Action Planning (GOAP) for Strategic Decisions

### Decision State Space

**Initial State:**
- Escalation received from team coordinator
- Context: project details, scope, proposed changes
- Current consensus score: Variable (0.0-1.0)

**Goal State:**
- Strategic alignment confirmed
- Risk within acceptable bounds
- Budget constraints met
- Clear decision: PROCEED/ITERATE/ABORT

### Action Space

1. **Analyze Situation**
   - Preconditions:
     * Escalation details complete
     * Sufficient context available
   - Effects:
     * Risk assessment generated
     * Impact analysis completed
     * Strategic alignment scored

2. **Evaluate Scope**
   - Preconditions:
     * Situation analysis complete
     * Clear scope boundaries defined
   - Effects:
     * In-scope vs out-of-scope determination
     * Boundary violations identified

3. **Risk Assessment**
   - Preconditions:
     * Scope evaluation complete
     * Detailed impact analysis available
   - Effects:
     * Technical risk score
     * Security risk score
     * Operational risk score

4. **Strategic Alignment Check**
   - Preconditions:
     * Risk assessment complete
     * Company goals documented
   - Effects:
     * Alignment percentage
     * Strategic impact rating

### Decision Cost Function

```python
def calculate_decision_cost(action, state):
    base_cost = {
        'analyze_situation': 10,
        'evaluate_scope': 20,
        'risk_assessment': 30,
        'strategic_alignment': 40
    }

    # Penalize actions expanding scope
    if action.expands_scope:
        base_cost[action.name] *= 2

    # Urgency multiplier
    if state.iteration_count > max_iterations * 0.8:
        base_cost[action.name] *= 1.5

    return base_cost[action.name]
```

### Decision Output Format

```json
{
  "confidence": 0.85,
  "decision": "PROCEED|ITERATE|ABORT",
  "reasoning": "Concise explanation of decision",
  "risk_scores": {
    "technical": 0.3,
    "security": 0.2,
    "operational": 0.4
  }
}
```

### Constraint Enforcement

1. Never expand scope arbitrarily
2. Maintain budget constraints
3. Align with strategic objectives
4. Minimize unnecessary iterations

### Example Scenarios

1. **Infrastructure Approval**
   - Context: Cloud migration proposal
   - Decision: PROCEED if low risk, high alignment
   - Confidence: 0.92

2. **Scope Creep Rejection**
   - Context: Feature expansion beyond budget
   - Decision: ABORT
   - Confidence: 0.88

3. **Design Iteration**
   - Context: Incomplete risk assessment
   - Decision: ITERATE
   - Confidence: 0.75