Antigravity AgentKit 2.0 — 本番マルチエージェントオーケストレーション

// Manager Agent: OrderProcessingOrchestrator
const managerInstructions = `
You are the Order Processing Manager. Your role is to:
1. Parse incoming orders and validate structure
2. Delegate payment processing to PaymentSurface (parallel)
3. Delegate inventory management to InventorySurface (parallel)
4. Delegate notification to NotificationSurface (parallel)
5. Aggregate results and detect conflicts
6. If conflicts detected, trigger RollbackSurface
7. Return final order status with timestamp
 
Error handling:
- If any Surface returns timeout, delegate to TimeoutRecoveryWorker
- Log all delegations with parent_task_id for tracing
`;
 
// Implementation in AgentKit 2.0
const manager = new Manager({
  name: 'OrderProcessingOrchestrator',
  model: 'claude-opus',
  instructions: managerInstructions,
  surfaces: [
    'PaymentSurface',
    'InventorySurface',
    'NotificationSurface',
    'RollbackSurface'
  ],
  parallel_workers: 3, // Max concurrent Surface invocations
  timeout: 30000,      // 30 seconds total
});

// Surface Agent: DataAnalysisSurface
const surfaceInstructions = `
You are the Data Analysis Surface. Your responsibilities:
1. Receive analysis requests from Manager
2. Decompose into atomic analysis tasks:
   - StatisticalAnalysisWorker (descriptive stats, correlations)
   - AnomalyDetectionWorker (outlier identification)
   - TrendAnalysisWorker (time-series forecasting)
3. Execute Workers in parallel using delegation_ids
4. Validate consistency across results
5. Synthesize findings with confidence levels
 
For each Worker result:
- Check for data quality issues
- Map outputs to common schema
- Assign confidence scores
- Return integrated analysis report
`;
 
// Parallel Worker execution
const surface = new Surface({
  name: 'DataAnalysisSurface',
  model: 'claude-sonnet',
  instructions: surfaceInstructions,
  workers: {
    StatisticalAnalysisWorker: {
      role: 'Descriptive statistics and correlations',
      timeout: 15000
    },
    AnomalyDetectionWorker: {
      role: 'Outlier detection and validation',
      timeout: 15000
    },
    TrendAnalysisWorker: {
      role: 'Time-series forecasting',
      timeout: 15000
    }
  },
  max_parallel: 3,
});

// Worker Agent: DatabaseQueryWorker
const workerInstructions = `
You are the Database Query Worker. Execute with precision:
1. Receive query specification from parent Surface
2. Build parameterized SQL (using prepared statements)
3. Execute against READ_REPLICA for non-blocking reads
4. Transform results to standard JSON schema
5. Log execution metrics (rows returned, duration, memory)
 
Available tools:
- execute_read_query(sql, params, timeout=5000)
- validate_schema(data, schema_id)
- log_execution_metric(metric_name, value)
 
Security constraints:
- Maximum 10,000 rows per query
- Queries must match pre-approved patterns
- Log all parameters for audit trail
`;
 
// Tool Chain configuration
const toolChain = {
  tools: [
    {
      name: 'execute_read_query',
      description: 'Execute SELECT query on replica',
      input_schema: {
        sql: { type: 'string', description: 'Parameterized SQL' },
        params: { type: 'array', description: 'Query parameters' },
        timeout: { type: 'number', default: 5000 }
      }
    },
    {
      name: 'validate_schema',
      description: 'Validate result against schema',
      input_schema: {
        data: { type: 'object' },
        schema_id: { type: 'string' }
      }
    },
    {
      name: 'log_execution_metric',
      description: 'Log performance metrics',
      input_schema: {
        metric_name: { type: 'string' },
        value: { type: 'number' }
      }
    }
  ]
};
 
const worker = new Worker({
  name: 'DatabaseQueryWorker',
  model: 'claude-haiku', // Cost-optimized for simple tasks
  instructions: workerInstructions,
  tools: toolChain,
  execution_timeout: 30000,
});

// Manager-level timeout handling
const managerConfig = {
  surface_timeout: 30000,
  worker_timeout: 15000,
  max_retries: 2,
  backoff_strategy: 'exponential', // 1s, 2s, 4s
 
  error_handlers: {
    TIMEOUT: (context) => {
      // Escalate to faster fallback
      return context.delegateTo('FastPathSurface');
    },
    NETWORK_ERROR: (context) => {
      // Queue for retry with state preservation
      return context.deferWithState();
    }
  }
};

// Saga Pattern implementation in Manager
const managerOrchestration = `
Execute compensation transactions if any Surface fails:
 
Primary flow:
1. PaymentSurface -> charge_id returned
2. InventorySurface -> reserved_items returned
3. ShippingSurface -> shipment_id returned
 
If InventorySurface fails after PaymentSurface succeeded:
1. Trigger PaymentSurface.refund(charge_id)
2. Wait for refund confirmation
3. Log failed order with rollback_reason
4. Notify user with specific failure details
 
Use idempotency keys for all operations:
- payment_idempotency_key = hash(order_id + 'payment')
- inventory_idempotency_key = hash(order_id + 'inventory')
`;

// AgentKit 2.0 Context Management
const contextSchema = {
  request_id: 'uuid',
  user_id: 'string',
  session_token: 'encrypted_jwt',
  auth_scope: ['read:data', 'write:orders'],
  tenant_id: 'string',
  execution_deadline: 'timestamp',
 
  // Propagate through all delegations
  propagate_to_children: true,
 
  // Audit trail
  agent_chain: ['Manager', 'Surface', 'Worker']
};
 
// Automatic context injection
manager.delegate(surface, task, {
  context: requestContext,
  preserve_context: true, // Auto-propagate child calls
  audit_log: true
});

// Tier cost models by task complexity
const agentModelConfig = {
  Manager: 'claude-opus',          // 高コスト、高精度
  Surface: 'claude-sonnet',        // 中コスト、中精度
  Worker: 'claude-haiku',          // 低コスト、軽量
};
 
// Dynamic model selection based on task
if (task.complexity === 'simple') {
  worker.model = 'claude-haiku';
} else if (task.complexity === 'complex') {
  worker.model = 'claude-sonnet';
}

// Leverage AgentKit's prompt caching
const cachedSurface = {
  name: 'DataAnalysisSurface',
  system_prompt_cache: true, // Cache surface instructions
  worker_instructions_cache: true,
 
  // Long context cached rarely changes
  cached_context: {
    company_taxonomy: LARGE_JSON,
    metric_definitions: LARGE_JSON,
  }
};
 
// Cache hit results in 90% cost reduction for follow-up calls

// Batch multiple worker tasks
const batchConfig = {
  batch_size: 50,
  batch_deadline: 60000, // 1 minute
 
  // Process queue asynchronously
  workers_per_batch: 3,
 
  // Cost reduction: ~40% via batch API
  batch_api_enabled: true
};

// AgentKit 2.0 Sandbox Configuration
const sandboxPolicy = {
  worker_capabilities: {
    DatabaseQueryWorker: {
      allowed_tables: ['orders', 'products'],
      blocked_tables: ['users_pii', 'admin_logs'],
      max_rows: 10000,
      read_only: true
    },
    NotificationWorker: {
      allowed_channels: ['email', 'slack'],
      blocked_channels: ['sms'], // Expensive, requires explicit approval
      rate_limit: 1000 // per minute
    }
  },
 
  // Prevent prompt injection via input validation
  input_validation: {
    user_query: (input) => {
      // Tokenize and check for injection patterns
      return sanitizeInput(input);
    }
  },
 
  // Audit all tool invocations
  tool_invocation_logging: true
};

const productionSLO = {
  availability: 0.999, // 99.9% uptime
  p99_latency: 5000,   // 5 seconds
  error_rate: 0.001,   // 0.1%
 
  // Agent-specific metrics
  manager_p99: 4000,
  surface_p99: 2000,
  worker_p99: 1000,
 
  // Cost metrics
  max_cost_per_request: 0.05, // USD
  monthly_budget: 10000
};

// Traffic splitting configuration
const deploymentConfig = {
  v1_system: { weight: 0.99, agentkit: false },
  v2_agentkit: { weight: 0.01, agentkit: true },
 
  // Monitor divergence in results
  divergence_tolerance: 0.05, // 5% difference acceptable
 
  // Automatic rollback if divergence exceeds threshold
  auto_rollback: true
};