Build custom code-based evaluators in Amazon Bedrock AgentCore

This article demonstrates how to build custom code-based evaluators in Amazon Bedrock AgentCore using Lambda-based functions, enabling the integration of custom logic into a financial market-intelligence agent. Four custom evaluators are implemented and registered with AgentCore, allowing for on-demand and online execution modes. By combining custom code-based evaluators with built-in evaluators, users can create complex decision-making pipelines. However, this approach requires careful management of evaluator dependencies and compatibility. The custom evaluators can be integrated into the agent's decision-making process using the AgentCore API.

⚡ Key Takeaways

• Four custom code evaluators are implemented for a financial market-intelligence agent using Lambda-based functions.
• Custom code-based evaluators can be registered with AgentCore for on-demand and online execution modes.
• Custom evaluators can be combined with built-in evaluators to create complex decision-making pipelines.
• The AgentCore API is used to integrate custom evaluators into the agent's decision-making process.
• Careful management of evaluator dependencies and compatibility is required when combining custom code-based evaluators.
• WhyItMatters: This article provides a practical guide for engineers building custom AI agents using Amazon Bedrock, enabling the integration of custom logic and decision-making pipelines.
• TechnicalLevel: Intermediate
• TargetAudience: ML Engineers
• PracticalSteps:
• Create a new Lambda function for each custom evaluator using the AWS Management Console or AWS CLI.
• Implement the custom logic for each evaluator using a programming language such as Python or JavaScript.
• Register each custom evaluator with AgentCore using the AgentCore API.
• Configure the agent to use the custom evaluators in on-demand and online modes.
• ToolsMentioned: Amazon Bedrock, AWS Lambda, AWS Management Console, AWS CLI
• Tags: BEDROCK, INFERENCE, PYTHON

🔧 Tools & Libraries

This article provides a practical guide for engineers building custom AI agents using Amazon Bedrock, enabling the integration of custom logic and decision-making pipelines.

✅ Practical Steps

1. Create a new Lambda function for each custom evaluator using the AWS Management Console or AWS CLI.
2. Implement the custom logic for each evaluator using a programming language such as Python or JavaScript.
3. Register each custom evaluator with AgentCore using the AgentCore API.
4. Configure the agent to use the custom evaluators in on-demand and online modes.

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