Overview of Amazon Bedrock AgentCore Evaluation Framework

Amazon Bedrock AgentCore provides a productivity-focused evaluation framework to ensure the reliability of AI agents. This framework is designed to maintain accuracy in production environments and measures and improves quality from development to deployment. (Source: aws.amazon.com)

9-Stage Evaluation Process

AgentCore Evaluations offers an integrated evaluation flow that covers nine stages of the agent lifecycle. This is not just a tool integration, but a consistent evaluation surface across nine points, including from Define to Improve, with features such as:

  • On-demand evaluation (for experimentation)
  • Batch evaluation (for continuous monitoring)
  • Online evaluation (for live traffic)
  • Integration with existing evaluators and custom evaluators
  • Dataset evaluation (public preview)
  • Integration with CloudWatch
  • Recommendation of A/B testing and optimization features (Source: aws.amazon.com)

Quality Assurance Mechanism in Production Environment

AgentCore has the following mechanisms to ensure the reliability of agents when they affect actual business decisions:

  • Continuous Quality Loop: A mechanism to constantly measure and improve the agent’s behavior
  • Sub-agent Integrated Evaluation: Evaluation of the collaboration of multiple specialized sub-agents (e.g., performance insight agents, customer insight agents)
  • Security Controls: Response scope control based on user domain and role
  • Error Soft Fail: An automatic defense mechanism to prevent the agent from failing silently (Source: aws.amazon.com)

Summary

  • Utilize AgentCore Evaluations to continuously measure and improve agent quality
  • Combine on-demand evaluation and batch evaluation to achieve quality control from development to production
  • Enable real-time performance monitoring with CloudWatch integration
  • Use A/B testing features to select the optimal agent settings with data-driven approaches
  • Evaluate the collaboration between sub-agents to ensure reliability in complex business processes