Multi-Agent System Architecture¶

Overview¶

The AIDDDMAP platform implements a sophisticated multi-agent system that enables autonomous data management, processing, and marketplace interactions. This document outlines the core concepts, components, and interactions within the multi-agent ecosystem.

Core Components¶

1. Agent Types¶

Data Collection Agents¶

Monitor and collect data from various sources
Validate data quality and integrity
Generate ZK proofs for data authenticity
Handle data preprocessing and normalization

Processing Agents¶

Execute data transformations
Perform analytics and generate insights
Manage secure computation workflows
Handle batch and stream processing

Marketplace Agents¶

Facilitate data trading and exchange
Manage pricing and valuation
Handle transaction verification
Monitor market dynamics

Security Agents¶

Enforce access control policies
Monitor system security
Generate and verify cryptographic proofs
Manage key distribution

Agent Architecture¶

1. Base Agent Structure¶

interface BaseAgent {
  id: string;
  type: AgentType;
  capabilities: string[];
  status: AgentStatus;
  metadata: AgentMetadata;
}

interface AgentMetadata {
  created: number;
  lastActive: number;
  version: string;
  permissions: string[];
}

2. Agent Communication¶

interface AgentMessage {
  from: string;
  to: string;
  type: MessageType;
  payload: any;
  timestamp: number;
  signature?: string;
}

enum MessageType {
  REQUEST = "request",
  RESPONSE = "response",
  BROADCAST = "broadcast",
  ALERT = "alert",
}

Agent Coordination¶

1. Task Distribution¶

interface Task {
  id: string;
  type: TaskType;
  priority: number;
  requirements: TaskRequirement[];
  deadline?: number;
  status: TaskStatus;
}

interface TaskRequirement {
  capability: string;
  minAgents: number;
  preferences?: AgentPreference[];
}

2. Consensus Mechanism¶

interface ConsensusProposal {
  proposalId: string;
  proposer: string;
  action: ProposedAction;
  votes: AgentVote[];
  threshold: number;
  deadline: number;
}

interface AgentVote {
  agentId: string;
  vote: boolean;
  signature: string;
  timestamp: number;
}

Security Integration¶

1. Agent Authentication¶

interface AgentCredentials {
  id: string;
  publicKey: string;
  certificate: string;
  permissions: string[];
  signature: string;
}

2. Secure Communication¶

interface SecureChannel {
  id: string;
  participants: string[];
  encryptionKey: string;
  established: number;
  lastUsed: number;
}

Performance Optimization¶

1. Load Balancing¶

interface LoadMetrics {
  agentId: string;
  cpu: number;
  memory: number;
  taskCount: number;
  lastUpdated: number;
}

2. Resource Management¶

interface ResourceAllocation {
  agentId: string;
  resources: {
    cpu: number;
    memory: number;
    storage: number;
  };
  constraints: ResourceConstraint[];
}

Monitoring and Logging¶

1. Agent Metrics¶

interface AgentMetrics {
  id: string;
  timestamp: number;
  performance: {
    taskCompletion: number;
    responseTime: number;
    errorRate: number;
  };
  resources: {
    cpuUsage: number;
    memoryUsage: number;
    networkUsage: number;
  };
}

2. System Health¶

interface SystemHealth {
  timestamp: number;
  activeAgents: number;
  pendingTasks: number;
  resourceUtilization: number;
  alerts: SystemAlert[];
}

Integration Examples¶

1. Data Processing Pipeline¶

// Configure data processing workflow
const workflow = new AgentWorkflow({
  steps: [
    {
      type: "collection",
      agents: ["collector-1", "collector-2"],
      config: {
        source: "sensor-network",
        interval: 5000,
      },
    },
    {
      type: "processing",
      agents: ["processor-1"],
      config: {
        operations: ["normalize", "aggregate"],
        batch: {
          size: 1000,
          timeout: 30000,
        },
      },
    },
    {
      type: "marketplace",
      agents: ["market-maker-1"],
      config: {
        pricing: "dynamic",
        minValue: 100,
      },
    },
  ],
});

2. Security Workflow¶

// Configure security workflow
const securityWorkflow = new AgentWorkflow({
  steps: [
    {
      type: "security",
      agents: ["security-1"],
      config: {
        operations: ["generate-proof", "verify-access"],
        requirements: {
          proofType: "zk-snark",
          accessLevel: "confidential",
        },
      },
    },
    {
      type: "audit",
      agents: ["audit-1"],
      config: {
        logLevel: "detailed",
        retention: "30d",
      },
    },
  ],
});

Best Practices¶

1. Agent Design¶

Keep agents focused and specialized
Implement proper error handling
Use secure communication channels
Maintain audit logs

2. System Architecture¶

Design for scalability
Implement redundancy
Use proper authentication
Monitor system health

3. Performance¶

Optimize resource usage
Implement caching
Use efficient communication patterns
Regular performance testing

Future Enhancements¶

Planned Features
Advanced AI capabilities
Improved consensus mechanisms
Enhanced security features
Better resource optimization
Research Areas
Agent learning algorithms
Distributed consensus
Security protocols
Resource allocation strategies