proffesor-for-testing__agentic-qe/docs/reports/ADR-051-agentic-flow-integration-analysis.md

# ADR-051: Agentic-Flow Deep Integration Analysis

| Field | Value |
|-------|-------|
| **Decision ID** | ADR-051 |
| **Status** | Proposed |
| **Date** | 2026-01-20 |
| **Author** | GOAP Specialist |
| **Review Cadence** | Monthly |

---

## Executive Summary

This analysis proposes deep integration of Agentic-Flow features into AQE v3 to address identified implementation gaps. The integration targets **352x faster code transforms**, **46% faster task completion**, **87% cost reduction**, and **<10ms agent coordination** through five key features:

1. **Agent Booster** (P0): Rust/WASM deterministic code transforms
2. **ReasoningBank** (P0): Cross-session learning with pattern persistence
3. **Multi-Model Router** (P1): Intelligent model selection with cost optimization
4. **ONNX Embeddings** (P1): Privacy-preserving local vector search
5. **QUIC Swarm** (P2): Sub-millisecond agent coordination

---

## Part 1: Current State Analysis

### 1.1 AQE v3 Implementation Status

**Implemented ADRs (50 total):**
- ADR-001 through ADR-050 all implemented
- 12 DDD domains fully operational
- 3,612+ passing tests
- RuVector neural backbone (ADR-050) recently implemented

**Key Existing Integrations:**
- ADR-017: RuVector Integration (Q-Learning + AST + fallbacks)
- ADR-021: QE ReasoningBank (SQLite + transformers, 114k/s)
- ADR-026: AISP (Rejected - TypeScript interfaces sufficient)
- ADR-040: Agentic-Flow Integration (WorkflowOrchestrator + YAML pipelines)
- ADR-043: Vendor-Independent LLM (HybridRouter + 7 providers)
- ADR-050: RuVector Neural Backbone (ML-first, persistent Q-values, hypergraph)

### 1.2 Identified Implementation Gaps

| Gap ID | Description | Current State | Impact |
|--------|-------------|---------------|--------|
| GAP-001 | Code transform latency | LLM-based (6000ms avg) | Slow test generation |
| GAP-002 | Cross-session learning loss | Partial persistence | Lost optimizations |
| GAP-003 | Model routing cost | Fixed model selection | 40-60% cost overhead |
| GAP-004 | Embedding privacy | Cloud-dependent | Data sovereignty issues |
| GAP-005 | Agent coordination latency | HTTP/2 (50-100ms) | Swarm bottleneck |
| GAP-006 | Pattern learning persistence | Memory-only in some paths | Lost patterns on restart |
| GAP-007 | Deterministic transforms | Non-deterministic LLM | Flaky code generation |

### 1.3 Agentic-Flow Feature Capabilities

| Feature | Performance | Cost | Privacy | Integration Effort |
|---------|-------------|------|---------|-------------------|
| **Agent Booster** | 166-352x faster | $0 runtime | 100% local | Medium (Rust/WASM) |
| **ReasoningBank** | 46% faster tasks | Reduced API calls | Configurable | Low (existing impl) |
| **Multi-Model Router** | 10ms overhead | 87% reduction | Configurable | Medium (router layer) |
| **ONNX Embeddings** | <1ms search | $0 runtime | 100% local | Low (drop-in) |
| **QUIC Swarm** | 53.7% faster | Minimal | N/A | High (protocol) |

---

## Part 2: Integration Points Analysis

### 2.1 ADR-017 RuVector Integration Points

**Current Implementation:**
- Q-Learning router in `/v3/src/integrations/ruvector/q-learning-router.ts`
- AST complexity analysis in `ast-complexity.ts`
- Fallback mechanisms in `fallback.ts`

**Integration Opportunities:**
```
Agent Booster + RuVector:
├── Replace LLM code transforms with Agent Booster
├── Use RuVector embeddings for similarity search
├── Combine AST analysis with deterministic merging
└── Fallback to LLM only when confidence < 0.65
```

**GOAP Action: RUVECTOR-BOOSTER-BRIDGE**
- Preconditions: Agent Booster WASM loaded, RuVector provider active
- Effects: Code transforms 166x faster, $0 cost, deterministic output
- Cost: 2 story points

### 2.2 ADR-021 ReasoningBank Integration Points

**Current Implementation:**
- SQLite persistence with 114k/s throughput
- Transformer-based embeddings
- 52 tests passing

**Agentic-Flow ReasoningBank Features:**
- Cross-session trajectory tracking
- Verdict judgment with LLM-as-judge
- Memory distillation and optimization
- 67% success rate improvement in benchmarks

**Integration Opportunities:**
```
Unified ReasoningBank:
├── Merge AQE ReasoningBank with Agentic-Flow implementation
├── Add trajectory tracking for QE task execution
├── Implement verdict judgment for test outcomes
├── Enable cross-domain knowledge transfer
└── Persistent learning across sessions (67% improvement)
```

**GOAP Action: REASONINGBANK-UNIFICATION**
- Preconditions: Both ReasoningBank implementations accessible
- Effects: 46% faster tasks, persistent learning, cross-domain transfer
- Cost: 3 story points

### 2.3 ADR-040 Agentic-Flow Integration Points

**Current Implementation:**
- WorkflowOrchestrator for YAML pipeline execution
- Basic integration with Claude Flow

**Enhanced Integration:**
```
Deep Agentic-Flow Integration:
├── Multi-Model Router for all LLM calls
│   ├── Route simple tasks to Haiku (cost savings)
│   ├── Route complex tasks to Opus (quality)
│   └── Use local models for privacy-sensitive operations
├── QUIC transport for agent coordination
│   ├── Replace HTTP/2 with QUIC (53.7% faster)
│   └── Enable 0-RTT reconnection (91% faster)
└── Agent Booster for code operations
    ├── Test generation transforms
    ├── Coverage analysis AST operations
    └── Code intelligence queries
```

**GOAP Action: WORKFLOW-ENHANCEMENT**
- Preconditions: ADR-040 WorkflowOrchestrator exists
- Effects: Multi-model routing, QUIC transport, deterministic transforms
- Cost: 4 story points

### 2.4 ADR-050 RuVector Neural Backbone Points

**Current Implementation:**
- ML-first architecture (>80% ML usage target)
- Persistent Q-values and SONA patterns
- Hypergraph code intelligence

**Integration with Agentic-Flow:**
```
Neural Backbone Enhancement:
├── ONNX Embeddings for local privacy-preserving search
│   ├── all-MiniLM-L6-v2 (384 dim, fast)
│   └── jina-embeddings-v2-base-code (768 dim, code-optimized)
├── Agent Booster for deterministic code operations
│   ├── AST-aware merging
│   └── Vector similarity search
└── Multi-Model Router for ML inference
    ├── Route embedding generation locally
    └── Route complex reasoning to cloud
```

**GOAP Action: NEURAL-BACKBONE-ONNX**
- Preconditions: ADR-050 implemented, ONNX runtime available
- Effects: Local embeddings, privacy-preserving, <1ms search
- Cost: 2 story points

---

## Part 3: Dependencies and Prerequisites

### 3.1 Dependency Graph

```
                    ┌─────────────────────┐
                    │   ADR-051 Complete  │
                    │   (Full Integration)│
                    └──────────┬──────────┘
                               │
           ┌───────────────────┼───────────────────┐
           │                   │                   │
           ▼                   ▼                   ▼
   ┌───────────────┐   ┌───────────────┐   ┌───────────────┐
   │  Phase 3      │   │  Phase 2      │   │  Phase 1      │
   │  QUIC Swarm   │   │  Model Router │   │  Foundation   │
   │  (P2)         │   │  ONNX (P1)    │   │  (P0)         │
   └───────┬───────┘   └───────┬───────┘   └───────┬───────┘
           │                   │                   │
           │                   │           ┌───────┴───────┐
           │                   │           │               │
           │                   │           ▼               ▼
           │                   │   ┌─────────────┐ ┌─────────────┐
           │                   │   │Agent Booster│ │ReasoningBank│
           │                   │   │ Integration │ │ Unification │
           │                   │   └──────┬──────┘ └──────┬──────┘
           │                   │          │               │
           │                   ▼          ▼               │
           │           ┌─────────────────────────┐        │
           │           │      ADR-050            │        │
           │           │  RuVector Backbone      │◄───────┘
           │           └───────────┬─────────────┘
           │                       │
           │           ┌───────────┴─────────────┐
           │           │                         │
           ▼           ▼                         ▼
   ┌───────────────────────┐           ┌─────────────────┐
   │      ADR-040          │           │     ADR-017     │
   │  Agentic-Flow Base    │           │ RuVector Base   │
   └───────────────────────┘           └─────────────────┘
```

### 3.2 Prerequisites Checklist

**P0 Prerequisites (Agent Booster + ReasoningBank):**
- [ ] Node.js 18+ with native addon support
- [ ] Rust toolchain for WASM compilation (if building from source)
- [ ] SQLite database for pattern persistence
- [ ] ADR-050 ML observability layer active

**P1 Prerequisites (Multi-Model Router + ONNX):**
- [ ] P0 features integrated
- [ ] ONNX Runtime installed (onnxruntime-node)
- [ ] Embedding models downloaded (jina-code-v2 or MiniLM)
- [ ] API keys for alternative providers (optional)

**P2 Prerequisites (QUIC Swarm):**
- [ ] P1 features integrated
- [ ] UDP port availability (4433 default)
- [ ] TLS certificates for QUIC encryption
- [ ] WASM module for QUIC protocol

---

## Part 4: Implementation Phases

### Phase 1: Foundation (Week 1-2) - P0 Priority

**Milestone: Agent Booster + ReasoningBank Core**

| Action ID | Description | Preconditions | Effects | Cost |
|-----------|-------------|---------------|---------|------|
| AB-001 | Install Agent Booster npm package | Node.js 18+ | Package available | 0.5 SP |
| AB-002 | Create AgentBoosterService wrapper | AB-001 | QE-compatible interface | 1 SP |
| AB-003 | Integrate with test-generation domain | AB-002, ADR-005 | 166x faster transforms | 1.5 SP |
| AB-004 | Add fallback to LLM when confidence < 0.65 | AB-003, ADR-011 | Hybrid approach | 1 SP |
| RB-001 | Unify ReasoningBank implementations | ADR-021 active | Single API | 1 SP |
| RB-002 | Add trajectory tracking for QE tasks | RB-001 | Learning from execution | 1.5 SP |
| RB-003 | Implement verdict judgment for tests | RB-002 | Quality feedback loop | 1.5 SP |
| RB-004 | Enable cross-session persistence | RB-003 | Pattern retention | 1 SP |

**Phase 1 Success Criteria:**
- [ ] Agent Booster processes 80%+ of code transforms
- [ ] Average transform latency < 100ms (vs 6000ms baseline)
- [ ] ReasoningBank persists patterns across sessions
- [ ] 46% reduction in repeated task failures

### Phase 2: Intelligence Layer (Week 3-4) - P1 Priority

**Milestone: Multi-Model Router + ONNX Embeddings**

| Action ID | Description | Preconditions | Effects | Cost |
|-----------|-------------|---------------|---------|------|
| MR-001 | Create ModelRouterService | ADR-043 HybridRouter | Unified routing | 1 SP |
| MR-002 | Implement cost-optimized routing | MR-001 | 87% cost reduction | 2 SP |
| MR-003 | Add local model support (Ollama) | MR-002 | Privacy option | 1.5 SP |
| MR-004 | Integrate with all 12 domains | MR-003 | Consistent routing | 2 SP |
| OX-001 | Install ONNX Runtime | Node.js 18+ | Runtime available | 0.5 SP |
| OX-002 | Create OnnxEmbeddingService | OX-001 | Local embeddings | 1.5 SP |
| OX-003 | Replace cloud embeddings in coverage | OX-002, ADR-003 | Private analysis | 1.5 SP |
| OX-004 | Integrate with hypergraph engine | OX-003, ADR-050 | Local code intelligence | 2 SP |

**Phase 2 Success Criteria:**
- [ ] Model router handles 100% of LLM calls
- [ ] 87% cost reduction achieved
- [ ] ONNX embeddings used for all vector operations
- [ ] Zero cloud calls for embedding generation

### Phase 3: Performance Layer (Week 5-6) - P2 Priority

**Milestone: QUIC Swarm Coordination**

| Action ID | Description | Preconditions | Effects | Cost |
|-----------|-------------|---------------|---------|------|
| QC-001 | Install QUIC transport module | Phase 2 complete | Transport available | 0.5 SP |
| QC-002 | Create QuicCoordinationService | QC-001 | Swarm communication | 2 SP |
| QC-003 | Replace HTTP/2 in Queen coordinator | QC-002, ADR-008 | 53.7% faster | 2 SP |
| QC-004 | Enable 0-RTT reconnection | QC-003 | 91% faster reconnect | 1 SP |
| QC-005 | Add connection migration | QC-004 | Network resilience | 1.5 SP |
| QC-006 | Benchmark and optimize | QC-005 | Performance validation | 1 SP |

**Phase 3 Success Criteria:**
- [ ] QUIC transport for all agent coordination
- [ ] <10ms agent message latency (vs 50-100ms HTTP/2)
- [ ] 0-RTT reconnection working
- [ ] Connection survives network changes

### Phase 4: Validation and Optimization (Week 7-8)

**Milestone: Full Integration Validation**

| Action ID | Description | Preconditions | Effects | Cost |
|-----------|-------------|---------------|---------|------|
| VAL-001 | Integration test suite | All phases | Verified integration | 2 SP |
| VAL-002 | Performance benchmarks | VAL-001 | Metrics collected | 1.5 SP |
| VAL-003 | Cost analysis report | VAL-002 | ROI documented | 1 SP |
| VAL-004 | Security audit | VAL-003 | Vulnerabilities addressed | 2 SP |
| OPT-001 | Bottleneck analysis | VAL-002 | Hotspots identified | 1 SP |
| OPT-002 | Performance tuning | OPT-001 | Optimizations applied | 2 SP |
| DOC-001 | Migration guide | All phases | Documentation complete | 1.5 SP |

---

## Part 5: GOAP Action Plan for Parallel Execution

### 5.1 World State Definition

```typescript
interface WorldState {
  // Foundation State
  agentBoosterInstalled: boolean;
  agentBoosterIntegrated: boolean;
  reasoningBankUnified: boolean;
  trajectoryTrackingEnabled: boolean;

  // Intelligence State
  modelRouterActive: boolean;
  costOptimizationEnabled: boolean;
  onnxRuntimeInstalled: boolean;
  localEmbeddingsActive: boolean;

  // Performance State
  quicTransportActive: boolean;
  zeroRttEnabled: boolean;
  connectionMigrationEnabled: boolean;

  // Metrics
  avgTransformLatencyMs: number;
  costReductionPercent: number;
  agentCoordinationLatencyMs: number;
  mlUsageRate: number;
}

// Initial State
const initialState: WorldState = {
  agentBoosterInstalled: false,
  agentBoosterIntegrated: false,
  reasoningBankUnified: false,
  trajectoryTrackingEnabled: false,
  modelRouterActive: true, // ADR-043 exists
  costOptimizationEnabled: false,
  onnxRuntimeInstalled: false,
  localEmbeddingsActive: false,
  quicTransportActive: false,
  zeroRttEnabled: false,
  connectionMigrationEnabled: false,
  avgTransformLatencyMs: 6000,
  costReductionPercent: 0,
  agentCoordinationLatencyMs: 75,
  mlUsageRate: 0.60 // Current estimate
};

// Goal State
const goalState: WorldState = {
  agentBoosterInstalled: true,
  agentBoosterIntegrated: true,
  reasoningBankUnified: true,
  trajectoryTrackingEnabled: true,
  modelRouterActive: true,
  costOptimizationEnabled: true,
  onnxRuntimeInstalled: true,
  localEmbeddingsActive: true,
  quicTransportActive: true,
  zeroRttEnabled: true,
  connectionMigrationEnabled: true,
  avgTransformLatencyMs: 50, // 166x improvement
  costReductionPercent: 87,
  agentCoordinationLatencyMs: 10, // <10ms target
  mlUsageRate: 0.85 // >80% target
};
```

### 5.2 Parallel Action Groups

**Group A: Foundation (Parallel - 2 agents)**
```yaml
parallel_execution:
  agent_1:
    - action: AB-001_INSTALL_AGENT_BOOSTER
    - action: AB-002_CREATE_WRAPPER_SERVICE
    - action: AB-003_INTEGRATE_TEST_GENERATION
    - action: AB-004_ADD_LLM_FALLBACK

  agent_2:
    - action: RB-001_UNIFY_REASONINGBANK
    - action: RB-002_ADD_TRAJECTORY_TRACKING
    - action: RB-003_IMPLEMENT_VERDICT_JUDGMENT
    - action: RB-004_ENABLE_PERSISTENCE

  sync_point: PHASE_1_COMPLETE
```

**Group B: Intelligence (Parallel - 2 agents)**
```yaml
parallel_execution:
  agent_3:
    - action: MR-001_CREATE_MODEL_ROUTER
    - action: MR-002_IMPLEMENT_COST_ROUTING
    - action: MR-003_ADD_LOCAL_MODELS
    - action: MR-004_INTEGRATE_ALL_DOMAINS

  agent_4:
    - action: OX-001_INSTALL_ONNX
    - action: OX-002_CREATE_EMBEDDING_SERVICE
    - action: OX-003_REPLACE_CLOUD_EMBEDDINGS
    - action: OX-004_INTEGRATE_HYPERGRAPH

  depends_on: PHASE_1_COMPLETE
  sync_point: PHASE_2_COMPLETE
```

**Group C: Performance (Sequential - 1 agent)**
```yaml
sequential_execution:
  agent_5:
    - action: QC-001_INSTALL_QUIC
    - action: QC-002_CREATE_COORDINATION_SERVICE
    - action: QC-003_REPLACE_HTTP2
    - action: QC-004_ENABLE_0RTT
    - action: QC-005_ADD_MIGRATION
    - action: QC-006_BENCHMARK

  depends_on: PHASE_2_COMPLETE
  sync_point: PHASE_3_COMPLETE
```

**Group D: Validation (Parallel - 2 agents)**
```yaml
parallel_execution:
  agent_6:
    - action: VAL-001_INTEGRATION_TESTS
    - action: VAL-002_BENCHMARKS
    - action: VAL-003_COST_ANALYSIS
    - action: VAL-004_SECURITY_AUDIT

  agent_7:
    - action: OPT-001_BOTTLENECK_ANALYSIS
    - action: OPT-002_PERFORMANCE_TUNING
    - action: DOC-001_MIGRATION_GUIDE

  depends_on: PHASE_3_COMPLETE
  sync_point: ADR_051_COMPLETE
```

### 5.3 GOAP Heuristic Function

```typescript
function calculateHeuristic(current: WorldState, goal: WorldState): number {
  let h = 0;

  // Foundation (weight: 3x - highest priority)
  if (!current.agentBoosterIntegrated && goal.agentBoosterIntegrated) h += 3;
  if (!current.reasoningBankUnified && goal.reasoningBankUnified) h += 3;

  // Intelligence (weight: 2x)
  if (!current.costOptimizationEnabled && goal.costOptimizationEnabled) h += 2;
  if (!current.localEmbeddingsActive && goal.localEmbeddingsActive) h += 2;

  // Performance (weight: 1x)
  if (!current.quicTransportActive && goal.quicTransportActive) h += 1;
  if (!current.zeroRttEnabled && goal.zeroRttEnabled) h += 1;

  // Metric-based heuristic
  if (current.avgTransformLatencyMs > goal.avgTransformLatencyMs) {
    h += (current.avgTransformLatencyMs - goal.avgTransformLatencyMs) / 1000;
  }

  if (current.costReductionPercent < goal.costReductionPercent) {
    h += (goal.costReductionPercent - current.costReductionPercent) / 10;
  }

  return h;
}
```

---

## Part 6: Success Metrics and Verification

### 6.1 Quantitative Metrics

| Metric | Baseline | Target | Measurement Method |
|--------|----------|--------|-------------------|
| Code transform latency | 6000ms | <50ms | Agent Booster benchmark |
| API cost per 1000 tasks | $10.00 | $1.30 | Token tracking + billing |
| Agent coordination latency | 75ms | <10ms | QUIC benchmark |
| Cross-session pattern retention | 0% | 100% | Restart test |
| ML usage rate | 60% | >85% | Observability metrics |
| Task success improvement | 0% | 46% | ReasoningBank A/B test |
| Embedding privacy | 0% local | 100% local | ONNX audit |

### 6.2 Verification Criteria

**Phase 1 Verification:**
```bash
# Agent Booster verification
npm run test:agent-booster
# Expected: 166x speedup, 95%+ accuracy

# ReasoningBank verification
npm run test:reasoningbank:persistence
# Expected: Patterns survive restart
```

**Phase 2 Verification:**
```bash
# Model Router verification
npm run test:model-router:cost
# Expected: 87% cost reduction

# ONNX verification
npm run test:onnx:privacy
# Expected: Zero cloud embedding calls
```

**Phase 3 Verification:**
```bash
# QUIC verification
npm run test:quic:latency
# Expected: <10ms P95

# 0-RTT verification
npm run test:quic:reconnect
# Expected: 91% faster reconnection
```

### 6.3 Quality Gates

| Gate | Criteria | Blocking |
|------|----------|----------|
| Performance | Transform latency < 100ms | Yes |
| Cost | >50% reduction achieved | Yes |
| Accuracy | Agent Booster accuracy > 95% | Yes |
| Privacy | ONNX handles all embeddings | No (soft) |
| Latency | Agent coordination < 25ms | No (soft) |

---

## Part 7: Risks and Mitigations

### 7.1 Risk Matrix

| Risk ID | Description | Probability | Impact | Mitigation |
|---------|-------------|-------------|--------|------------|
| R-001 | Agent Booster WASM fails on some platforms | Medium | High | TypeScript fallback, native addon |
| R-002 | ReasoningBank merge conflicts | Low | Medium | Feature flags, gradual migration |
| R-003 | ONNX model accuracy lower than cloud | Medium | Medium | Hybrid approach, quality threshold |
| R-004 | QUIC firewall blocking | Medium | Low | HTTP/2 fallback, port negotiation |
| R-005 | Memory overhead from local models | Low | Medium | Lazy loading, LRU cache |
| R-006 | Integration breaks existing tests | Medium | High | Feature flags, comprehensive tests |

### 7.2 Mitigation Strategies

**R-001 Mitigation: Platform Compatibility**
```typescript
// Automatic fallback chain
const agentBooster = await AgentBooster.create({
  preferNative: true,      // Try native addon first
  fallbackToWasm: true,    // Then WASM
  fallbackToTs: true,      // Then TypeScript
  fallbackToLlm: true      // Final fallback to LLM
});
```

**R-003 Mitigation: Quality Threshold**
```typescript
const result = await onnxEmbeddings.generate(text);
if (result.confidence < 0.85) {
  // Fall back to cloud embeddings
  return await cloudEmbeddings.generate(text);
}
```

**R-006 Mitigation: Feature Flags**
```typescript
const config = {
  features: {
    agentBooster: process.env.ENABLE_AGENT_BOOSTER === 'true',
    quicTransport: process.env.ENABLE_QUIC === 'true',
    onnxEmbeddings: process.env.ENABLE_ONNX === 'true'
  }
};
```

---

## Part 8: Decision Summary

### WH(Y) Decision Statement

**In the context of** AQE v3 requiring faster code transforms, reduced costs, and improved agent coordination across 12 DDD domains,

**facing** 6000ms average transform latency, high API costs, HTTP/2 coordination bottlenecks, and cross-session learning loss,

**we decided for** deep integration of Agentic-Flow features (Agent Booster, ReasoningBank, Multi-Model Router, ONNX Embeddings, QUIC Swarm) through a phased 8-week implementation,

**and neglected** building equivalent capabilities from scratch, waiting for external improvements, or accepting current performance limitations,

**to achieve** 166x faster transforms, 87% cost reduction, <10ms coordination, 100% pattern persistence, and 46% task success improvement,

**accepting that** this requires platform-specific builds (WASM/native), gradual migration with feature flags, and UDP port availability for QUIC.

---

## Appendix A: Existing ADR Cross-References

| Existing ADR | Relationship | Integration Impact |
|--------------|--------------|-------------------|
| ADR-017 | Extends | Agent Booster enhances RuVector |
| ADR-021 | Merges | ReasoningBank unification |
| ADR-040 | Extends | Deep Agentic-Flow integration |
| ADR-043 | Extends | Multi-Model Router enhancement |
| ADR-050 | Extends | ONNX + Agent Booster for neural backbone |

## Appendix B: Swarm Agent Configuration

```yaml
# Recommended swarm configuration for ADR-051 implementation
swarm:
  topology: hierarchical-mesh
  maxAgents: 8
  strategy: specialized

agents:
  - id: adr051-foundation-1
    type: coder
    domain: agent-booster-integration
    model: sonnet

  - id: adr051-foundation-2
    type: coder
    domain: reasoningbank-unification
    model: sonnet

  - id: adr051-intelligence-1
    type: architect
    domain: model-router
    model: opus

  - id: adr051-intelligence-2
    type: coder
    domain: onnx-integration
    model: sonnet

  - id: adr051-performance-1
    type: coder
    domain: quic-transport
    model: sonnet

  - id: adr051-tester-1
    type: tester
    domain: integration-validation
    model: haiku
```

---

**Document Status:** Ready for Architecture Review
**Estimated Total Effort:** 34 Story Points
**Estimated Duration:** 8 weeks (4 phases)
**Resource Requirement:** 5-7 agents in parallel

---

## References

- [ADR-040: Agentic-Flow Deep Integration](/workspaces/agentic-qe/v3/implementation/adrs/ADR-040-v3-qe-agentic-flow-integration.md)
- [ADR-050: RuVector Neural Backbone](/workspaces/agentic-qe/v3/implementation/adrs/ADR-050-ruvector-neural-backbone.md)
- [Agentic-Flow Agent Booster](/tmp/agentic-flow/docs/features/agent-booster/)
- [Agentic-Flow ReasoningBank](/tmp/agentic-flow/docs/features/reasoningbank/)
- [Agentic-Flow QUIC Transport](/tmp/agentic-flow/docs/features/quic/)
- [Agentic-Flow Multi-Model Router](/tmp/agentic-flow/docs/architecture/MULTI_MODEL_ROUTER_PLAN.md)