Define evaluation criteria by mapping business requirements to specific FM capabilities, including reasoning depth, knowledge breadth, multilingual support, and specialized functions.2
Set up systematic benchmarking using Model Evaluation to compare multiple FMs across standardized tasks relevant to your use case.3
Analyze performance metrics across dimensions, including accuracy, latency, throughput, and cost, to identify optimal model candidates for specific business applications.4
Conduct limitation analysis by testing edge cases, identifying knowledge cutoff impacts, and evaluating hallucination tendencies to understand potential risks.5
Perform cost-benefit analysis by calculating total cost of ownership (TCO), including inference costs, integration complexity, and maintenance requirements for different foundation models.6
Document model selection rationale with quantitative benchmarks and qualitative assessments to support decision-making and enable future reevaluation.
Design an abstraction layer using Lambda functions that separates business logic from model-specific implementation details.
Implement standardized request and response formats in API Gateway to help ensure consistent interfaces, regardless of the underlying FM.
Configure AWS AppConfig to externalize model selection parameters, enabling runtime configuration changes without code deployments.
Create adapter patterns that normalize inputs and outputs across different FMs, ensuring consistent application behavior regardless of provider.
Implement a model router component using Lambda that dynamically selects the appropriate FM based on request characteristics and configuration settings.
API Gateway → Lambda (Router) → AppConfig (Model Configuration) → Model-specific Lambda functions
Set up feature flags in AWS AppConfig to enable gradual rollout of new models, A/B testing between models, and quick rollbacks if performance issues arise.
When the system needs to coordinate multiple specialized agents while maintaining clear control hierarchies. Implement AWS Agent Squad with supervisor-agent pattern and specialized worker agents. The supervisor-agent pattern provides structured coordination through a hierarchical approach. It enables clear control flows, efficient task distribution, and centralized oversight while maintaining agent specialization.
Implement circuit breaker patterns using Step Functions to detect FM failures and automatically route requests to fallback options.
Configure Amazon Bedrock Cross-Region Inference to ensure high availability by routing requests to alternative Regions during service disruptions.
Design multi-model ensembling strategies that combine outputs from multiple FMs to improve reliability while reducing dependency on any single model.
Implement timeout and retry mechanisms with exponential backoff using Lambda to handle transient failures in FM APIs.
Create graceful degradation pathways that maintain core functionality through more basic models or rule-based systems when advanced FMs are unavailable.
Set up comprehensive monitoring using CloudWatch with custom metrics and alarms to detect model performance degradation and trigger automated remediation actions.
Configure Step Functions workflows with explicit stopping conditions that prevent infinite loops or excessive iterations, defining maximum execution counts and termination criteria.
Implement circuit breaker patterns using Step Functions and CloudWatch alarms that automatically halt processing when error rates or other metrics exceed predefined thresholds.
Design AWS Identity and Access Management (IAM) policies with graduated access levels based on operation criticality and automated risk assessment. Graduated access levels with automated risk assessment provide dynamic protection that scales with operation criticality. This approach maintains efficiency while ensuring appropriate controls are in place.