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wifi-densepose/vendor/ruvector/docs/dag/11-AGENT-TASKS.md

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# Agent Task Assignments
## Overview
Task breakdown for 15-agent swarm implementing the Neural DAG Learning system. Each agent has specific responsibilities, dependencies, and deliverables.
## Swarm Topology
```
┌─────────────────────┐
│ QUEEN AGENT │
│ (Coordinator) │
│ Agent #0 │
└──────────┬──────────┘
┌──────────────────────┼──────────────────────┐
│ │ │
▼ ▼ ▼
┌───────────────┐ ┌───────────────┐ ┌───────────────┐
│ CORE TEAM │ │ POSTGRES TEAM │ │ QUDAG TEAM │
│ Agents 1-5 │ │ Agents 6-9 │ │ Agents 10-12 │
└───────────────┘ └───────────────┘ └───────────────┘
┌──────────┴──────────┐
▼ ▼
┌───────────────┐ ┌───────────────┐
│ TESTING TEAM │ │ DOCS TEAM │
│ Agents 13-14 │ │ Agent 15 │
└───────────────┘ └───────────────┘
```
## Agent Assignments
---
### Agent #0: Queen Coordinator
**Type**: `queen-coordinator`
**Role**: Central orchestration, dependency management, conflict resolution
**Responsibilities**:
- Monitor all agent progress via memory coordination
- Resolve cross-team dependencies and conflicts
- Manage swarm-wide configuration
- Aggregate status reports
- Make strategic decisions on implementation order
- Coordinate code reviews between teams
**Deliverables**:
- Swarm coordination logs
- Dependency resolution decisions
- Final integration verification
**Memory Keys**:
- `swarm/queen/status` - Overall swarm status
- `swarm/queen/decisions` - Strategic decisions log
- `swarm/queen/dependencies` - Cross-agent dependency tracking
**No direct code output** - Coordination only
---
### Agent #1: Core DAG Engine
**Type**: `coder`
**Role**: Core DAG data structures and algorithms
**Responsibilities**:
1. Implement `QueryDag` structure
2. Implement `OperatorNode` and `OperatorType`
3. Implement DAG traversal algorithms (topological sort, DFS, BFS)
4. Implement edge/node management
5. Implement DAG serialization/deserialization
**Files to Create/Modify**:
```
ruvector-dag/src/
├── lib.rs
├── dag/
│ ├── mod.rs
│ ├── query_dag.rs
│ ├── operator_node.rs
│ ├── traversal.rs
│ └── serialization.rs
```
**Dependencies**: None (foundational)
**Blocked By**: None
**Blocks**: Agents 2, 3, 4, 6
**Deliverables**:
- [ ] `QueryDag` struct with node/edge management
- [ ] `OperatorNode` with all operator types
- [ ] Topological sort implementation
- [ ] Cycle detection
- [ ] JSON/binary serialization
**Estimated Complexity**: Medium
---
### Agent #2: Attention Mechanisms (Basic)
**Type**: `coder`
**Role**: Implement first 4 attention mechanisms
**Responsibilities**:
1. Implement `DagAttention` trait
2. Implement `TopologicalAttention`
3. Implement `CausalConeAttention`
4. Implement `CriticalPathAttention`
5. Implement `MinCutGatedAttention`
**Files to Create/Modify**:
```
ruvector-dag/src/attention/
├── mod.rs
├── traits.rs
├── topological.rs
├── causal_cone.rs
├── critical_path.rs
└── mincut_gated.rs
```
**Dependencies**: Agent #1 (QueryDag)
**Blocked By**: Agent #1
**Blocks**: Agents 6, 13
**Deliverables**:
- [ ] `DagAttention` trait definition
- [ ] `TopologicalAttention` with decay
- [ ] `CausalConeAttention` with temporal awareness
- [ ] `CriticalPathAttention` with path computation
- [ ] `MinCutGatedAttention` with flow-based gating
**Estimated Complexity**: High
---
### Agent #3: Attention Mechanisms (Advanced)
**Type**: `coder`
**Role**: Implement advanced attention mechanisms
**Responsibilities**:
1. Implement `HierarchicalLorentzAttention`
2. Implement `ParallelBranchAttention`
3. Implement `TemporalBTSPAttention`
4. Implement `AttentionSelector` (UCB bandit)
5. Implement attention caching
**Files to Create/Modify**:
```
ruvector-dag/src/attention/
├── hierarchical_lorentz.rs
├── parallel_branch.rs
├── temporal_btsp.rs
├── selector.rs
└── cache.rs
```
**Dependencies**: Agent #1 (QueryDag), Agent #2 (DagAttention trait)
**Blocked By**: Agents #1, #2
**Blocks**: Agents 6, 13
**Deliverables**:
- [ ] `HierarchicalLorentzAttention` with hyperbolic ops
- [ ] `ParallelBranchAttention` with branch detection
- [ ] `TemporalBTSPAttention` with eligibility traces
- [ ] `AttentionSelector` with UCB selection
- [ ] LRU attention cache
**Estimated Complexity**: Very High
---
### Agent #4: SONA Integration
**Type**: `coder`
**Role**: Self-Optimizing Neural Architecture integration
**Responsibilities**:
1. Implement `DagSonaEngine`
2. Implement `MicroLoRA` adaptation
3. Implement `DagTrajectoryBuffer`
4. Implement `DagReasoningBank`
5. Implement `EwcPlusPlus` constraints
**Files to Create/Modify**:
```
ruvector-dag/src/sona/
├── mod.rs
├── engine.rs
├── micro_lora.rs
├── trajectory.rs
├── reasoning_bank.rs
└── ewc.rs
```
**Dependencies**: Agent #1 (QueryDag)
**Blocked By**: Agent #1
**Blocks**: Agents 6, 7, 13
**Deliverables**:
- [ ] `DagSonaEngine` orchestration
- [ ] `MicroLoRA` rank-2 adaptation (<100μs)
- [ ] Lock-free trajectory buffer
- [ ] K-means++ clustering for patterns
- [ ] EWC++ with Fisher information
**Estimated Complexity**: Very High
---
### Agent #5: MinCut Optimization
**Type**: `coder`
**Role**: Subpolynomial min-cut algorithms
**Responsibilities**:
1. Implement `DagMinCutEngine`
2. Implement `LocalKCut` oracle
3. Implement dynamic update algorithms
4. Implement bottleneck detection
5. Implement redundancy suggestions
**Files to Create/Modify**:
```
ruvector-dag/src/mincut/
├── mod.rs
├── engine.rs
├── local_kcut.rs
├── dynamic_updates.rs
├── bottleneck.rs
└── redundancy.rs
```
**Dependencies**: Agent #1 (QueryDag)
**Blocked By**: Agent #1
**Blocks**: Agent #2 (MinCutGatedAttention), Agent #6
**Deliverables**:
- [ ] `DagMinCutEngine` with O(n^0.12) updates
- [ ] `LocalKCut` oracle implementation
- [ ] Hierarchical decomposition
- [ ] Bottleneck scoring algorithm
- [ ] Redundancy recommendation engine
**Estimated Complexity**: Very High
---
### Agent #6: PostgreSQL Core Integration
**Type**: `backend-dev`
**Role**: Core PostgreSQL extension integration
**Responsibilities**:
1. Set up pgrx extension structure
2. Implement GUC variables
3. Implement global state management
4. Implement query hooks (planner, executor)
5. Implement background worker registration
**Files to Create/Modify**:
```
ruvector-postgres/src/dag/
├── mod.rs
├── extension.rs
├── guc.rs
├── state.rs
├── hooks.rs
└── worker.rs
```
**Dependencies**: Agents #1-5 (all core components)
**Blocked By**: Agents #1, #2, #3, #4, #5
**Blocks**: Agents #7, #8, #9
**Deliverables**:
- [ ] Extension scaffolding with pgrx
- [ ] All GUC variables from spec
- [ ] Thread-safe global state (DashMap)
- [ ] Planner hook for DAG analysis
- [ ] Executor hooks for trajectory capture
- [ ] Background worker main loop
**Estimated Complexity**: High
---
### Agent #7: PostgreSQL SQL Functions (Part 1)
**Type**: `backend-dev`
**Role**: Core SQL function implementations
**Responsibilities**:
1. Configuration functions
2. Query analysis functions
3. Attention functions
4. Basic status/health functions
**Files to Create/Modify**:
```
ruvector-postgres/src/dag/
├── functions/
│ ├── mod.rs
│ ├── config.rs
│ ├── analysis.rs
│ ├── attention.rs
│ └── status.rs
```
**SQL Functions**:
- `dag_set_enabled`
- `dag_set_learning_rate`
- `dag_set_attention`
- `dag_configure_sona`
- `dag_config`
- `dag_status`
- `dag_analyze_plan`
- `dag_critical_path`
- `dag_bottlenecks`
- `dag_attention_scores`
- `dag_attention_matrix`
**Dependencies**: Agent #6 (PostgreSQL core)
**Blocked By**: Agent #6
**Blocks**: Agent #13
**Deliverables**:
- [ ] All configuration SQL functions
- [ ] Query analysis functions
- [ ] Attention computation functions
- [ ] Status reporting functions
**Estimated Complexity**: Medium
---
### Agent #8: PostgreSQL SQL Functions (Part 2)
**Type**: `backend-dev`
**Role**: Learning and pattern SQL functions
**Responsibilities**:
1. Pattern management functions
2. Trajectory functions
3. Learning control functions
4. Self-healing functions
**Files to Create/Modify**:
```
ruvector-postgres/src/dag/
├── functions/
│ ├── patterns.rs
│ ├── trajectories.rs
│ ├── learning.rs
│ └── healing.rs
```
**SQL Functions**:
- `dag_store_pattern`
- `dag_query_patterns`
- `dag_pattern_clusters`
- `dag_consolidate_patterns`
- `dag_record_trajectory`
- `dag_trajectory_history`
- `dag_learn_now`
- `dag_reset_learning`
- `dag_health_report`
- `dag_anomalies`
- `dag_auto_repair`
**Dependencies**: Agent #6 (PostgreSQL core), Agent #4 (SONA)
**Blocked By**: Agents #4, #6
**Blocks**: Agent #13
**Deliverables**:
- [ ] Pattern CRUD functions
- [ ] Trajectory management functions
- [ ] Learning control functions
- [ ] Health and healing functions
**Estimated Complexity**: Medium
---
### Agent #9: Self-Healing System
**Type**: `coder`
**Role**: Autonomous self-healing implementation
**Responsibilities**:
1. Implement `AnomalyDetector`
2. Implement `IndexHealthChecker`
3. Implement `LearningDriftDetector`
4. Implement repair strategies
5. Implement healing orchestrator
**Files to Create/Modify**:
```
ruvector-dag/src/healing/
├── mod.rs
├── anomaly.rs
├── index_health.rs
├── drift_detector.rs
├── strategies.rs
└── orchestrator.rs
```
**Dependencies**: Agent #4 (SONA), Agent #6 (PostgreSQL hooks)
**Blocked By**: Agents #4, #6
**Blocks**: Agent #8 (healing SQL functions), Agent #13
**Deliverables**:
- [ ] Z-score anomaly detection
- [ ] HNSW/IVFFlat health monitoring
- [ ] Pattern drift detection
- [ ] Repair strategy implementations
- [ ] Healing loop orchestration
**Estimated Complexity**: High
---
### Agent #10: QuDAG Client
**Type**: `coder`
**Role**: QuDAG network client implementation
**Responsibilities**:
1. Implement `QuDagClient`
2. Implement network communication
3. Implement pattern proposal flow
4. Implement consensus validation
5. Implement pattern synchronization
**Files to Create/Modify**:
```
ruvector-dag/src/qudag/
├── mod.rs
├── client.rs
├── network.rs
├── proposal.rs
├── consensus.rs
└── sync.rs
```
**Dependencies**: Agent #4 (patterns to propose)
**Blocked By**: Agent #4
**Blocks**: Agents #11, #12
**Deliverables**:
- [ ] QuDAG network client
- [ ] Async communication layer
- [ ] Pattern proposal protocol
- [ ] Consensus status tracking
- [ ] Pattern sync mechanism
**Estimated Complexity**: High
---
### Agent #11: QuDAG Cryptography
**Type**: `security-manager`
**Role**: Quantum-resistant cryptography
**Responsibilities**:
1. Implement ML-KEM-768 wrapper
2. Implement ML-DSA signature wrapper
3. Implement identity management
4. Implement secure key storage
5. Implement differential privacy for patterns
**Files to Create/Modify**:
```
ruvector-dag/src/qudag/
├── crypto/
│ ├── mod.rs
│ ├── ml_kem.rs
│ ├── ml_dsa.rs
│ ├── identity.rs
│ ├── keystore.rs
│ └── differential_privacy.rs
```
**Dependencies**: Agent #10 (QuDAG client)
**Blocked By**: Agent #10
**Blocks**: Agent #12
**Deliverables**:
- [ ] ML-KEM-768 encrypt/decrypt
- [ ] ML-DSA sign/verify
- [ ] Identity keypair management
- [ ] Secure keystore (zeroize)
- [ ] Laplace noise for DP
**Estimated Complexity**: High
---
### Agent #12: QuDAG Token Integration
**Type**: `backend-dev`
**Role**: rUv token operations
**Responsibilities**:
1. Implement staking interface
2. Implement reward claiming
3. Implement balance tracking
4. Implement token SQL functions
5. Implement governance participation
**Files to Create/Modify**:
```
ruvector-dag/src/qudag/
├── tokens/
│ ├── mod.rs
│ ├── staking.rs
│ ├── rewards.rs
│ └── governance.rs
ruvector-postgres/src/dag/functions/
├── qudag.rs (SQL functions for QuDAG)
```
**Dependencies**: Agent #10 (QuDAG client), Agent #11 (crypto)
**Blocked By**: Agents #10, #11
**Blocks**: Agent #13
**Deliverables**:
- [ ] Staking operations
- [ ] Reward computation
- [ ] Balance queries
- [ ] QuDAG SQL functions
- [ ] Governance voting
**Estimated Complexity**: Medium
---
### Agent #13: Test Suite Developer
**Type**: `tester`
**Role**: Comprehensive test implementation
**Responsibilities**:
1. Unit tests for all modules
2. Integration tests
3. Property-based tests
4. Benchmark tests
5. CI pipeline setup
**Files to Create/Modify**:
```
ruvector-dag/tests/
├── unit/
│ ├── attention/
│ ├── sona/
│ ├── mincut/
│ ├── healing/
│ └── qudag/
├── integration/
│ ├── postgres/
│ └── qudag/
├── property/
└── fixtures/
ruvector-dag/benches/
├── attention_bench.rs
├── sona_bench.rs
└── mincut_bench.rs
.github/workflows/
└── dag-tests.yml
```
**Dependencies**: All code agents (1-12)
**Blocked By**: Agents #1-12 (tests require implementations)
**Blocks**: None (can test incrementally)
**Deliverables**:
- [ ] >80% unit test coverage
- [ ] All integration tests passing
- [ ] Property tests (1000+ cases)
- [ ] Benchmarks meeting performance targets
- [ ] CI/CD pipeline
**Estimated Complexity**: High
---
### Agent #14: Test Data & Fixtures
**Type**: `tester`
**Role**: Test data generation and fixtures
**Responsibilities**:
1. Generate realistic query DAGs
2. Generate synthetic patterns
3. Generate trajectory data
4. Create mock QuDAG server
5. Create test databases
**Files to Create/Modify**:
```
ruvector-dag/tests/
├── fixtures/
│ ├── dag_generator.rs
│ ├── pattern_generator.rs
│ ├── trajectory_generator.rs
│ └── mock_qudag.rs
├── data/
│ ├── sample_dags.json
│ ├── sample_patterns.bin
│ └── sample_trajectories.json
```
**Dependencies**: Agent #1 (DAG structure definitions)
**Blocked By**: Agent #1
**Blocks**: Agent #13 (needs fixtures)
**Deliverables**:
- [ ] DAG generator for all complexity levels
- [ ] Pattern generator for learning tests
- [ ] Mock QuDAG server for network tests
- [ ] Sample data files
- [ ] Test database setup scripts
**Estimated Complexity**: Medium
---
### Agent #15: Documentation & Examples
**Type**: `api-docs`
**Role**: API documentation and usage examples
**Responsibilities**:
1. Rust API documentation
2. SQL API documentation
3. Usage examples
4. Integration guides
5. Troubleshooting guides
**Files to Create/Modify**:
```
ruvector-dag/
├── README.md
├── examples/
│ ├── basic_usage.rs
│ ├── attention_selection.rs
│ ├── learning_workflow.rs
│ └── qudag_integration.rs
docs/dag/
├── USAGE.md
├── TROUBLESHOOTING.md
└── EXAMPLES.md
```
**Dependencies**: All code agents (1-12)
**Blocked By**: None (can document spec first, update with impl)
**Blocks**: None
**Deliverables**:
- [ ] Complete rustdoc for all public APIs
- [ ] SQL function documentation
- [ ] Working code examples
- [ ] Integration guide
- [ ] Troubleshooting guide
**Estimated Complexity**: Medium
---
## Task Dependencies Graph
```
┌─────┐
│ 0 │ Queen
└──┬──┘
┌─────────────┼─────────────┐
│ │ │
┌──┴──┐ ┌──┴──┐ ┌──┴──┐
│ 1 │ │ 14 │ │ 15 │
└──┬──┘ └──┬──┘ └─────┘
│ │
┌────┼────┬───────┤
│ │ │ │
┌──┴─┐┌─┴──┐┌┴──┐┌───┴───┐
│ 2 ││ 4 ││ 5 ││ (13) │
└──┬─┘└─┬──┘└─┬─┘└───────┘
│ │ │
┌──┴─┐ │ │
│ 3 │ │ │
└──┬─┘ │ │
│ │ │
└────┼─────┘
┌──┴──┐
│ 6 │ PostgreSQL Core
└──┬──┘
┌────┼────┬────┐
│ │ │ │
┌──┴─┐┌─┴──┐│ ┌──┴──┐
│ 7 ││ 8 ││ │ 9 │
└────┘└────┘│ └─────┘
┌──┴──┐
│ 10 │ QuDAG Client
└──┬──┘
┌──┴──┐
│ 11 │ QuDAG Crypto
└──┬──┘
┌──┴──┐
│ 12 │ QuDAG Tokens
└──┬──┘
┌──┴──┐
│ 13 │ Tests
└─────┘
```
## Execution Phases
### Phase 1: Foundation (Agents 1, 14, 15)
- Agent #1: Core DAG Engine
- Agent #14: Test fixtures (parallel)
- Agent #15: Documentation skeleton (parallel)
**Duration**: Can start immediately
**Milestone**: QueryDag and OperatorNode complete
### Phase 2: Core Features (Agents 2, 3, 4, 5)
- Agent #2: Basic Attention
- Agent #3: Advanced Attention (after Agent #2)
- Agent #4: SONA Integration
- Agent #5: MinCut Optimization
**Duration**: After Phase 1 foundation
**Milestone**: All attention mechanisms and learning components
### Phase 3: PostgreSQL Integration (Agents 6, 7, 8, 9)
- Agent #6: PostgreSQL Core
- Agent #7: SQL Functions Part 1 (after Agent #6)
- Agent #8: SQL Functions Part 2 (after Agent #6)
- Agent #9: Self-Healing (after Agent #6)
**Duration**: After Phase 2 core features
**Milestone**: Full PostgreSQL extension functional
### Phase 4: QuDAG Integration (Agents 10, 11, 12)
- Agent #10: QuDAG Client
- Agent #11: QuDAG Crypto (after Agent #10)
- Agent #12: QuDAG Tokens (after Agent #11)
**Duration**: Can start after Agent #4 (SONA)
**Milestone**: Distributed pattern learning operational
### Phase 5: Testing & Validation (Agent 13)
- Agent #13: Full test suite
- Integration testing
- Performance validation
**Duration**: Ongoing throughout, intensive at end
**Milestone**: All tests passing, benchmarks met
## Coordination Protocol
### Memory Keys for Cross-Agent Communication
```
swarm/dag/
├── status/
│ ├── agent_{N}_status # Individual agent status
│ ├── phase_status # Current phase
│ └── blockers # Active blockers
├── artifacts/
│ ├── agent_{N}_files # Files created/modified
│ ├── interfaces # Shared interface definitions
│ └── schemas # Data schemas
├── decisions/
│ ├── api_decisions # API design decisions
│ ├── implementation # Implementation choices
│ └── conflicts # Resolved conflicts
└── metrics/
├── progress # Completion percentages
├── performance # Performance measurements
└── issues # Known issues
```
### Communication Hooks
Each agent MUST run before work:
```bash
npx claude-flow@alpha hooks pre-task --description "Agent #{N}: {task}"
npx claude-flow@alpha hooks session-restore --session-id "swarm-dag"
```
Each agent MUST run after work:
```bash
npx claude-flow@alpha hooks post-edit --file "{file}" --memory-key "swarm/dag/artifacts/agent_{N}_files"
npx claude-flow@alpha hooks post-task --task-id "agent_{N}_{task}"
```
## Success Criteria
| Agent | Must Complete | Performance Target |
|-------|---------------|-------------------|
| #1 | QueryDag, traversals, serialization | - |
| #2 | 4 attention mechanisms | <100μs per mechanism |
| #3 | 3 attention mechanisms + selector | <200μs per mechanism |
| #4 | SONA engine, MicroLoRA, ReasoningBank | <100μs adaptation |
| #5 | MinCut engine, dynamic updates | O(n^0.12) amortized |
| #6 | Extension scaffold, hooks, worker | - |
| #7 | 11 SQL functions | <5ms per function |
| #8 | 11 SQL functions | <5ms per function |
| #9 | Healing system | <1s detection latency |
| #10 | QuDAG client, sync | <500ms network ops |
| #11 | ML-KEM, ML-DSA | <10ms crypto ops |
| #12 | Token operations | <100ms token ops |
| #13 | >80% coverage, all benchmarks | - |
| #14 | All fixtures, mock server | - |
| #15 | Complete docs, examples | - |
---
*Document: 11-AGENT-TASKS.md | Version: 1.0 | Last Updated: 2025-01-XX*