proffesor-for-testing__agentic-qe/docs/agentics

Agentic Plans & Analysis

This directory contains Goal-Oriented Action Plans (GOAP) and technical analysis for complex multi-agent tasks in the Agentic QE Fleet.

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📋 Active Plans

RuVector Integration Fix

• Plan: ruvector-integration-goap-plan.md
• Analysis: ruvector-technical-analysis.md
• Status: ⏳ Ready for Execution
• Priority: P0 (Critical - False claims + Dead code)
• Estimated Duration: 8-12 hours
• Agents Required: 8 (coder, tester, reviewer, documentation specialist)

Quick Start:

# Execute with Claude Code
claude "Execute the RuVector integration GOAP plan in docs/agentics/ruvector-integration-goap-plan.md"

# Or with claude-flow
npx claude-flow sparc tdd "Fix RuVector integration using GOAP plan"

Issues Addressed:

1. ❌ Dead code in CoverageAnalyzerAgent (43+ lines unreachable)
2. ❌ False performance claims in README ("150x faster" not working)
3. ❌ Docker service requirements completely undocumented
4. ❌ Integration tests disable RuVector to avoid external dependencies
5. ❌ QualityGateAgent claims integration but never uses it

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🎯 GOAP Methodology

What is GOAP?

Goal-Oriented Action Planning (GOAP) is an AI planning technique that:

1. Defines State Space: Current state → Goal state
2. Plans Action Sequences: Optimal path through state transitions
3. Considers Preconditions: What must be true before each action
4. Models Effects: How each action changes state
5. Optimizes Path: Finds shortest/cheapest route to goal

Why GOAP for Multi-Agent Systems?

Traditional Approach:

Task → Agent 1 → Agent 2 → Agent 3 → Done
(sequential, rigid, no adaptation)

GOAP Approach:

Goal → State Analysis → Action Planning → Agent Assignment → Execution → Verification
(parallel, adaptive, optimized)

Benefits:

• ✅ Parallel Execution: Actions run concurrently when dependencies allow
• ✅ Dynamic Replanning: Adapts to failures and changing conditions
• ✅ Optimal Paths: A* search finds efficient action sequences
• ✅ Clear Preconditions: Dependencies explicit, no hidden assumptions
• ✅ Verifiable Effects: Each action has measurable outcomes

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🏗️ Plan Structure

Each GOAP plan contains:

1. State Space Definition

CurrentState: {
  field: { status, metrics, state }
}
GoalState: {
  field: { target_status, target_metrics, desired_state }
}

2. Action Sequence

Action {
  name: string;
  preconditions: StateCondition[];
  effects: StateChange[];
  agent: AgentType;
  duration: number;
  complexity: 'low' | 'medium' | 'high';
  verification: VerificationCriteria[];
}

3. Agent Assignments

Task	Primary Agent	Backup Agent	Rationale
...	...	...	...

4. Verification Criteria

• Observable outcome
• Measurable metric
• Testable condition

5. Dependencies Graph

graph TD
  A[Action 1] --> C[Action 3]
  B[Action 2] --> C
  C --> D[Action 4]

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🚀 Execution Patterns

Pattern 1: Claude Code Direct

# Read plan and execute with Claude
claude "Read and execute the GOAP plan in docs/agentics/ruvector-integration-goap-plan.md"

Advantages:

• Single command execution
• Claude orchestrates all agents
• Natural language task descriptions

Pattern 2: Claude-Flow Orchestration

# Use claude-flow for structured execution
npx claude-flow sparc tdd "Fix RuVector integration using GOAP plan"

Advantages:

• SPARC methodology integration
• Progress tracking
• Automatic verification gates

Pattern 3: MCP Tools (Advanced)

// Initialize swarm
mcp__claude-flow__swarm_init({
  topology: "hierarchical",
  maxAgents: 8
})

// Spawn agents
mcp__claude-flow__agent_spawn({ type: "coder" })
mcp__claude-flow__agent_spawn({ type: "tester" })

// Orchestrate task
mcp__claude-flow__task_orchestrate({
  task: { type: "comprehensive-testing" },
  strategy: "adaptive"
})

Advantages:

• Fine-grained control
• Real-time monitoring
• Advanced coordination

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📊 Progress Tracking

Execution Status

Plan	Status	Progress	Duration	Last Updated
RuVector Integration	⏳ Ready	0%	0h / 12h	2025-12-18

Verification Gates

Each plan has verification criteria that must pass before proceeding:

Phase {
  name: string;
  verificationCriteria: Criterion[];
  gateCondition: 'all-pass' | 'critical-pass' | 'majority-pass';
}

Example:

• ✅ Phase 1 Complete (Investigation done)
• ⏳ Phase 2 In Progress (Code fixes ongoing)
• ⏸️ Phase 3 Blocked (Waiting for Phase 2)

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🔧 Creating New Plans

Template Structure

# GOAP Plan: [Task Name]

## State Space
- Current State: { ... }
- Goal State: { ... }

## Actions
1. Action Name
   - Preconditions: [...]
   - Effects: [...]
   - Agent: [agent-type]
   - Duration: [estimate]
   - Verification: [...]

## Agent Assignments
| Task | Agent | Rationale |
|------|-------|-----------|

## Dependencies
[Dependency graph]

## Verification
- [ ] Criterion 1
- [ ] Criterion 2

## Execution
```bash
# Command to execute

### Best Practices

1. **State-First Design:** Define states before actions
2. **Clear Preconditions:** No implicit dependencies
3. **Measurable Effects:** Observable outcomes only
4. **Agent Capabilities:** Match task to agent expertise
5. **Verification Gates:** Don't proceed on failures
6. **Rollback Plans:** Define failure recovery

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## 🧪 Testing Plans

Before executing:

### 1. Dry Run
```bash
# Validate plan structure
aqe validate-plan docs/agentics/ruvector-integration-goap-plan.md

2. Precondition Check

# Verify environment meets preconditions
aqe check-preconditions docs/agentics/ruvector-integration-goap-plan.md

3. Simulation

# Simulate execution (no actual changes)
aqe simulate-plan docs/agentics/ruvector-integration-goap-plan.md

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📈 Metrics & Analytics

Plan Effectiveness

Track these metrics for each plan:

• Completion Rate: % of actions completed successfully
• Time Accuracy: Actual duration vs estimated
• Replanning Events: How often plan needed adjustment
• Verification Pass Rate: % of gates passed first time
• Agent Utilization: Idle time vs active time

Success Criteria

A plan is successful when:

• ✅ All verification criteria passed
• ✅ Goal state achieved
• ✅ No critical failures
• ✅ Within 150% of estimated duration
• ✅ All agents terminated cleanly

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🤝 Contributing

Adding New Plans

1. Create plan in docs/agentics/[name]-goap-plan.md
2. Create analysis in docs/agentics/[name]-technical-analysis.md
3. Update this README with plan summary
4. Submit PR with:
   • Plan document
   • Technical analysis
   • Execution examples
   • Success criteria

Plan Review Checklist

• State space clearly defined
• All preconditions explicit
• Effects measurable
• Agent assignments justified
• Dependencies visualized
• Verification criteria testable
• Rollback plan exists
• Execution commands provided

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🔗 Related Documentation

• SPARC Methodology
• Agent Capabilities
• Claude-Flow Integration
• Swarm Coordination

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📞 Support

• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Chat: Discord

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Maintained By: Agentic QE Fleet Team Last Updated: 2025-12-18 Version: 1.0.0