Evaluate conversational AI agents with Amazon Bedrock

1.Conversational AI agents are becoming increasingly popular across industries, but their dynamic nature makes traditional testing methods challenging. 2.The following are common pain points in developing conversational AI agents: tedious and repetitive testing, difficulty in setting up proper test cases, and complex debugging and tracing. 3.Agent Evaluation, an open-source solution using large language models (LLMs) on Amazon Bedrock, addresses these gaps by enabling comprehensive evaluation and validation of conversational AI agents at scale. 4.Agent Evaluation provides support for popular services, orchestration of concurrent conversations, configurable hooks to validate actions, integration into CI/CD pipelines, a generated test summary, and detailed traces for debugging. 5.In this post, we demonstrate how to streamline virtual agent testing at scale using Amazon Bedrock and Agent Evaluation. 6.The solution overview includes creating a test plan with three configurable components: target, evaluator, and test. 7.The test plan defines the target's functionality and how the end-user interacts with the target, including a series of steps representing interactions and expected results. 8.The evaluation workflow involves the evaluator reasoning and assessing responses based on the test plan, with the ability to initiate the conversation and evaluate the target agent's responses. 9.The use case overview involves developing an insurance claim processing agent using Agents for Amazon Bedrock and testing its capability to accurately search and retrieve relevant information from existing claims. 10.The steps to integrate Agent Evaluation with CI/CD pipelines include writing test cases, setting up GitHub Actions, configuring AWS credentials, and running the test.