# ADR-038: Multi-Agent Neural Coordination Plugin

**Status:** Proposed
**Date:** 2026-01-24
**Category:** Cutting-Edge AI
**Author:** Plugin Architecture Team
**Version:** 1.0.0
**Deciders:** Plugin Architecture Team, AI Research Team
**Supersedes:** None

## Context

Multi-agent systems require sophisticated coordination mechanisms to achieve emergent collective intelligence. Current approaches often rely on simple message passing or centralized coordinators that become bottlenecks. Advanced neural coordination can enable agents to develop shared representations, negotiate efficiently, and achieve consensus while maintaining individual autonomy.

## Decision

Create a **Multi-Agent Neural Coordination Plugin** that leverages RuVector WASM packages for neural-based agent coordination, emergent communication protocols, and collective decision-making with support for heterogeneous agent populations.

## Plugin Name

`@claude-flow/plugin-neural-coordination`

## Description

A cutting-edge multi-agent coordination plugin combining the SONA self-optimizing neural architecture with graph neural networks for agent communication topology optimization. The plugin enables emergent protocol development, neural consensus mechanisms, collective memory formation, and adaptive swarm behavior while maintaining interpretability of agent interactions.

## Key WASM Packages

| Package | Purpose |
|---------|---------|
| `sona` | Self-Optimizing Neural Architecture for agent adaptation |
| `ruvector-gnn-wasm` | Communication graph optimization and message routing |
| `ruvector-nervous-system-wasm` | Neural coordination layer for collective behavior |
| `ruvector-attention-wasm` | Multi-head attention for agent-to-agent communication |
| `ruvector-learning-wasm` | Multi-agent reinforcement learning (MARL) |

## MCP Tools

### 1. `coordination/neural-consensus`

Achieve consensus through neural negotiation.

```typescript
{
  name: 'coordination/neural-consensus',
  description: 'Achieve agent consensus using neural negotiation protocol',
  inputSchema: {
    type: 'object',
    properties: {
      proposal: {
        type: 'object',
        description: 'Proposal to reach consensus on',
        properties: {
          topic: { type: 'string' },
          options: { type: 'array', items: { type: 'object' } },
          constraints: { type: 'object' }
        }
      },
      agents: {
        type: 'array',
        items: {
          type: 'object',
          properties: {
            id: { type: 'string' },
            preferences: { type: 'object' },
            constraints: { type: 'object' }
          }
        }
      },
      protocol: {
        type: 'string',
        enum: ['neural_voting', 'iterative_refinement', 'auction', 'contract_net'],
        default: 'iterative_refinement'
      },
      maxRounds: { type: 'number', default: 10 }
    },
    required: ['proposal', 'agents']
  }
}
```

### 2. `coordination/topology-optimize`

Optimize communication topology using GNN.

```typescript
{
  name: 'coordination/topology-optimize',
  description: 'Optimize agent communication topology for efficiency',
  inputSchema: {
    type: 'object',
    properties: {
      agents: {
        type: 'array',
        items: {
          type: 'object',
          properties: {
            id: { type: 'string' },
            capabilities: { type: 'array', items: { type: 'string' } },
            location: { type: 'object' }
          }
        }
      },
      objective: {
        type: 'string',
        enum: ['minimize_latency', 'maximize_throughput', 'minimize_hops', 'fault_tolerant'],
        default: 'minimize_latency'
      },
      constraints: {
        type: 'object',
        properties: {
          maxConnections: { type: 'number' },
          minRedundancy: { type: 'number' },
          preferredTopology: { type: 'string', enum: ['mesh', 'tree', 'ring', 'star', 'hybrid'] }
        }
      }
    },
    required: ['agents']
  }
}
```

### 3. `coordination/collective-memory`

Manage shared collective memory across agents.

```typescript
{
  name: 'coordination/collective-memory',
  description: 'Manage neural collective memory for agent swarm',
  inputSchema: {
    type: 'object',
    properties: {
      action: {
        type: 'string',
        enum: ['store', 'retrieve', 'consolidate', 'forget', 'synchronize']
      },
      memory: {
        type: 'object',
        properties: {
          key: { type: 'string' },
          value: {},
          importance: { type: 'number', default: 0.5 },
          expiry: { type: 'string' }
        }
      },
      scope: {
        type: 'string',
        enum: ['global', 'team', 'pair'],
        default: 'team'
      },
      consolidationStrategy: {
        type: 'string',
        enum: ['ewc', 'replay', 'distillation'],
        default: 'ewc'
      }
    },
    required: ['action']
  }
}
```

### 4. `coordination/emergent-protocol`

Develop emergent communication protocols.

```typescript
{
  name: 'coordination/emergent-protocol',
  description: 'Develop emergent communication protocol through MARL',
  inputSchema: {
    type: 'object',
    properties: {
      task: {
        type: 'object',
        description: 'Cooperative task requiring communication',
        properties: {
          type: { type: 'string' },
          objectives: { type: 'array' },
          constraints: { type: 'object' }
        }
      },
      communicationBudget: {
        type: 'object',
        properties: {
          symbolsPerMessage: { type: 'number', default: 10 },
          messagesPerRound: { type: 'number', default: 3 }
        }
      },
      trainingEpisodes: { type: 'number', default: 1000 },
      interpretability: { type: 'boolean', default: true }
    },
    required: ['task']
  }
}
```

### 5. `coordination/swarm-behavior`

Orchestrate emergent swarm behaviors.

```typescript
{
  name: 'coordination/swarm-behavior',
  description: 'Orchestrate emergent swarm behavior using neural coordination',
  inputSchema: {
    type: 'object',
    properties: {
      behavior: {
        type: 'string',
        enum: [
          'flocking', 'foraging', 'formation', 'task_allocation',
          'exploration', 'aggregation', 'dispersion'
        ]
      },
      parameters: {
        type: 'object',
        description: 'Behavior-specific parameters'
      },
      adaptiveRules: {
        type: 'boolean',
        default: true,
        description: 'Allow neural adaptation of behavior rules'
      },
      observability: {
        type: 'object',
        properties: {
          recordTrajectories: { type: 'boolean' },
          measureEmergence: { type: 'boolean' }
        }
      }
    },
    required: ['behavior']
  }
}
```

## Use Cases

1. **Distributed Problem Solving**: Coordinate agents to solve complex decomposed problems
2. **Negotiation Systems**: Multi-party negotiation with optimal outcomes
3. **Swarm Robotics**: Emergent collective behaviors for physical agents
4. **Federated Learning**: Coordinate model training across distributed agents
5. **Market Simulation**: Agent-based modeling with realistic interactions

## Architecture

```
+------------------+     +----------------------+     +------------------+
| Agent Population |---->| Neural Coordination  |---->| Collective       |
| (Heterogeneous)  |     | (SONA + GNN)         |     | Decisions        |
+------------------+     +----------------------+     +------------------+
                                   |
                         +---------+---------+
                         |         |         |
                    +----+---+ +---+----+ +--+-----+
                    | SONA   | |Nervous | |Attention|
                    |Adapt   | |System  | |Comms    |
                    +--------+ +--------+ +---------+
                                   |
                              +----+----+
                              |  MARL   |
                              |Learning |
                              +---------+
```

## Neural Coordination Protocol

```
Agent State --> SONA Encoding --> Attention Routing --> GNN Propagation
      |              |                  |                     |
      v              v                  v                     v
[observations]  [neural repr]    [relevant agents]    [collective repr]
[beliefs]       [compressed]     [message weights]    [consensus signal]
```

## Consensus Mechanisms

| Mechanism | Description | Use Case |
|-----------|-------------|----------|
| Neural Voting | Attention-weighted voting | Quick decisions |
| Iterative Refinement | Multi-round negotiation | Complex trade-offs |
| Contract Net | Task allocation protocol | Resource assignment |
| Auction | Market-based allocation | Competitive scenarios |

## Performance Targets

| Metric | Target | Baseline (Traditional) | Improvement |
|--------|--------|------------------------|-------------|
| Consensus convergence | <100 rounds for 100 agents | ~1000 rounds (naive) | 10x |
| Communication overhead | <10% of total compute | ~30% (broadcast) | 3x |
| Topology optimization | <1s for 1000 nodes | ~1min (static config) | 60x |
| Memory synchronization | <100ms eventual consistency | ~1s (distributed DB) | 10x |
| Emergent protocol training | <1 hour for basic tasks | N/A (hand-designed) | Novel |

## Security Considerations

### Input Validation (CRITICAL)

All MCP tool inputs MUST be validated using Zod schemas:

```typescript
// coordination/neural-consensus input validation
const NeuralConsensusSchema = z.object({
  proposal: z.object({
    topic: z.string().max(1000),
    options: z.array(z.object({
      id: z.string().max(100),
      value: z.unknown()
    })).min(2).max(100),
    constraints: z.record(z.string(), z.unknown()).optional()
  }),
  agents: z.array(z.object({
    id: z.string().max(100),
    preferences: z.record(z.string(), z.number().min(-1).max(1)).optional(),
    constraints: z.record(z.string(), z.unknown()).optional()
  })).min(2).max(1000),
  protocol: z.enum(['neural_voting', 'iterative_refinement', 'auction', 'contract_net']).default('iterative_refinement'),
  maxRounds: z.number().int().min(1).max(1000).default(10)
});

// coordination/collective-memory input validation
const CollectiveMemorySchema = z.object({
  action: z.enum(['store', 'retrieve', 'consolidate', 'forget', 'synchronize']),
  memory: z.object({
    key: z.string().max(500).optional(),
    value: z.unknown().optional(),
    importance: z.number().min(0).max(1).default(0.5),
    expiry: z.string().datetime().optional()
  }).optional(),
  scope: z.enum(['global', 'team', 'pair']).default('team'),
  consolidationStrategy: z.enum(['ewc', 'replay', 'distillation']).default('ewc')
});

// coordination/swarm-behavior input validation
const SwarmBehaviorSchema = z.object({
  behavior: z.enum([
    'flocking', 'foraging', 'formation', 'task_allocation',
    'exploration', 'aggregation', 'dispersion'
  ]),
  parameters: z.record(z.string(), z.unknown()).optional(),
  adaptiveRules: z.boolean().default(true),
  observability: z.object({
    recordTrajectories: z.boolean().optional(),
    measureEmergence: z.boolean().optional()
  }).optional()
});
```

### WASM Security Constraints

| Constraint | Value | Rationale |
|------------|-------|-----------|
| Memory Limit | 1GB max per agent | Prevent memory exhaustion |
| CPU Time Limit | 60 seconds per round | Prevent infinite consensus loops |
| Agent Count Limit | 1000 max | Prevent resource exhaustion |
| Message Size Limit | 1MB per message | Prevent memory bombs |
| No External Communication | WASM sandbox enforced | Prevent unauthorized coordination |

### Agent Authentication (CRITICAL)

```typescript
// Every agent MUST be authenticated before joining coordination
interface AgentCredentials {
  agentId: string;
  publicKey: string;        // For message signing
  capabilities: string[];   // Authorized capabilities
  issuer: string;           // Who created this agent
  expiry: string;           // Credential expiration
  signature: string;        // Signed by coordination service
}

// Verify agent before allowing coordination
async function verifyAgent(credentials: AgentCredentials): Promise<boolean> {
  // Verify signature
  if (!verifySignature(credentials, COORDINATION_PUBLIC_KEY)) {
    throw new SecurityError('INVALID_CREDENTIALS', 'Agent credentials invalid');
  }

  // Check expiry
  if (new Date(credentials.expiry) < new Date()) {
    throw new SecurityError('EXPIRED_CREDENTIALS', 'Agent credentials expired');
  }

  // Verify capabilities
  return true;
}
```

### Message Security

```typescript
// All inter-agent messages MUST be signed
interface SecureMessage {
  senderId: string;
  recipientIds: string[];
  payload: unknown;
  timestamp: string;
  nonce: string;           // Prevent replay attacks
  signature: string;       // Signed by sender
}

// Message validation
function validateMessage(message: SecureMessage): boolean {
  // Verify signature
  // Check timestamp freshness (within 5 minutes)
  // Verify nonce not reused
  // Validate sender is authorized
}
```

### Identified Security Risks

| Risk ID | Severity | Description | Mitigation |
|---------|----------|-------------|------------|
| COORD-SEC-001 | **CRITICAL** | Rogue agent influencing consensus | Agent authentication, Byzantine fault tolerance |
| COORD-SEC-002 | **HIGH** | Sybil attack (fake agent multiplication) | Agent credential verification, rate limiting |
| COORD-SEC-003 | **HIGH** | Denial of service via protocol abuse | Round limits, timeout enforcement |
| COORD-SEC-004 | **MEDIUM** | Information leakage via collective memory | Memory access controls, encryption |
| COORD-SEC-005 | **MEDIUM** | Emergent malicious behavior | Behavior bounds, kill switches |

### Byzantine Fault Tolerance

```typescript
// Coordination MUST tolerate malicious agents
// Using BFT consensus: tolerates f < n/3 faulty nodes

interface BFTConsensus {
  requiredVotes: number;     // 2f + 1 for n = 3f + 1
  timeoutMs: number;         // Max time to wait for consensus
  viewChangeThreshold: number; // Trigger view change after failures

  // Detect and isolate malicious agents
  detectMalicious(agentId: string, evidence: Evidence[]): boolean;
  isolateAgent(agentId: string): void;
}
```

### Rate Limiting

```typescript
const CoordinationRateLimits = {
  'coordination/neural-consensus': { requestsPerMinute: 10, maxConcurrent: 2 },
  'coordination/topology-optimize': { requestsPerMinute: 5, maxConcurrent: 1 },
  'coordination/collective-memory': { requestsPerMinute: 100, maxConcurrent: 10 },
  'coordination/emergent-protocol': { requestsPerMinute: 1, maxConcurrent: 1 },  // Very expensive
  'coordination/swarm-behavior': { requestsPerMinute: 10, maxConcurrent: 2 }
};
```

## Risk Assessment

| Risk | Likelihood | Impact | Mitigation |
|------|------------|--------|------------|
| Emergent behavior instability | Medium | Medium | Fallback to rule-based, behavior bounds |
| Coordination deadlock | Low | High | Timeout mechanisms, leader election |
| Training compute costs | High | Medium | Pretrained protocols, transfer learning |
| Interpretability gaps | Medium | Medium | Protocol visualization, logging |

## Emergent Communication

The plugin supports developing emergent communication protocols:

1. **Symbol Grounding**: Agents develop shared vocabulary
2. **Compositionality**: Complex messages from simple symbols
3. **Pragmatics**: Context-aware communication
4. **Interpretability**: Human-readable protocol analysis

## Implementation Notes

### Phase 1: Core Coordination
- Basic consensus mechanisms
- Message routing with attention
- Collective memory store

### Phase 2: Neural Enhancement
- SONA integration for adaptation
- GNN topology optimization
- Nervous system integration

### Phase 3: Emergent Behavior
- MARL protocol training
- Swarm behavior primitives
- Interpretability tools

## Dependencies

```json
{
  "dependencies": {
    "sona": "^0.1.0",
    "ruvector-gnn-wasm": "^0.1.0",
    "ruvector-nervous-system-wasm": "^0.1.0",
    "ruvector-attention-wasm": "^0.1.0",
    "ruvector-learning-wasm": "^0.1.0"
  }
}
```

## Consequences

### Positive
- Enables sophisticated multi-agent coordination
- Emergent protocols can outperform hand-designed ones
- Adaptive behavior handles novel situations

### Negative
- Complex debugging for emergent behaviors
- Training requires significant compute
- Interpretability challenges for learned protocols

### Neutral
- Can fallback to rule-based coordination when needed

## Related ADRs

| ADR | Relationship |
|-----|--------------|
| ADR-004: Plugin Architecture | Foundation - Defines plugin structure |
| ADR-017: RuVector Integration | Dependency - Provides WASM packages |
| ADR-039: Cognitive Kernel | Related - Agent cognitive capabilities |
| ADR-040: Quantum Optimizer | Related - Resource allocation optimization |
| ADR-041: Hyperbolic Reasoning | Related - Agent hierarchy embeddings |

## References

- Multi-Agent RL Survey: https://arxiv.org/abs/1911.10635
- Emergent Communication: https://arxiv.org/abs/1612.07182
- ADR-017: RuVector Integration
- ADR-004: Plugin Architecture

---

**Last Updated:** 2026-01-24
