Skip to content

Latest commit

 

History

History
881 lines (673 loc) · 21.4 KB

11-AGENT-TASKS.md

File metadata and controls

881 lines (673 loc) · 21.4 KB

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:

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:

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