Evaluator Agent

1. Evaluator Agent on Amazon SageMaker

• Runtime Environment: Amazon SageMaker managed ML instance

• Model Deployment: Containerized ML models with auto-scaling configuration

• Implementation Pattern: Real-time inference endpoint with batch processing capabilities

• Technical Stack:

  • Primary framework: TensorFlow/PyTorch for predictive modeling

  • Feature engineering pipeline with SageMaker Processing

  • Model registry integration for versioning

  • A/B testing configuration for evaluation strategy optimization

2. DynamoDB User Tranche

• Data Structure: Partition key on user_id with sort key on evaluation_timestamp

• Capacity Mode: On-demand with auto-scaling

• Access Pattern: Low-latency reads (<10ms) for real-time evaluation

• Data Model:

  {  user_id: String,  evaluation_timestamp: Number,  risk_profile: Map,  investment_history: List,  evaluation_results: Map,  confidence_metrics: Map}

• Indexing Strategy: GSI on evaluation_results for aggregate analytics

3. Message Control Point (MCP)

• Implementation: AWS Step Functions state machine

• Protocol Handling: Bidirectional message transformation

• Integration Pattern: Asynchronous event-driven communication

• Message Format: JSON with schema validation

• Error Handling: Dead-letter queue with retry policy

Data Flow Mechanics

1. Input Processing

   • User context and market data ingested from upstream systems

   • Feature vector generation through SageMaker preprocessing containers

   • Normalization and encoding of categorical investment variables

2. Evaluation Execution

   • Multi-model ensemble prediction using SageMaker inference pipelines

   • Risk assessment algorithms executed against current market conditions

   • Performance projection based on historical patterns and current holdings

3. Results Management

   • Evaluation outcomes persisted to DynamoDB User Tranche

   • Confidence scores and uncertainty metrics attached to all predictions

   • User-specific evaluation history maintained with TTL policies

4. System Integration

   • MCP handles protocol translation for downstream consumers

   • Event notifications published for significant evaluation state changes

   • Asynchronous callbacks to dependent systems via the MCP gateway

Technical Optimizations

• Compute Efficiency: GPU acceleration for complex evaluation models

• Caching Strategy: Two-tier caching with in-memory for frequent access patterns

• Batch Processing: Micro-batch processing for evaluation requests during high load

• Resource Management: Auto-scaling based on queue depth and CPU utilization

Monitoring & Observability

• Metrics Collection: CloudWatch custom metrics for evaluation performance

• Logging: Structured JSON logs with correlation IDs

• Tracing: X-Ray integration for end-to-end request tracking

• Alerts: Multi-threshold alerting based on error rates and latency

Deployment Approach

The Evaluator Agent is deployed through a CI/CD pipeline with canary releases, ensuring zero-downtime updates and automated rollback capabilities based on error rate thresholds.