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Basic Information

Agent Evaluation is a generative AI-powered framework designed to test and validate virtual agents. It provides an LLM-based evaluator that orchestrates multi-turn conversations with a target agent and assesses the agent's responses during those interactions. The project is intended as a developer-focused testing tool that can connect to popular AWS model and hosting services and also accept custom agent targets. It supports concurrent conversations so multiple scenarios can be exercised in parallel, and it exposes hooks to run additional checks or integrations as part of a test flow. The README and documentation describe how to get started, how to add custom targets, and how to incorporate the framework into existing development and deployment workflows for systematic agent validation.

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App Details

Features
The repository highlights built-in support for AWS services including Amazon Bedrock, Amazon Q Business, and Amazon SageMaker, plus the ability to bring your own agent target for evaluation. It implements an LLM evaluator that orchestrates and evaluates multi-turn conversations, and it can run concurrent conversations to scale testing. The framework provides hook points for custom actions such as integration tests or other post-interaction tasks. It is designed to be incorporated into CI/CD pipelines to enable automated checks. The project includes documentation and contribution guidance to help developers configure targets, define evaluations, and extend the system.
Use Cases
Agent Evaluation helps teams validate conversational agents by automating scenario-based testing and judgment of agent responses. By using an LLM evaluator that conducts and scores multi-turn conversations, the framework enables repeatable assessments of agent behavior under realistic interactions. Support for concurrent conversations allows broader coverage and scalable testing. Hooks let teams chain integration tests or custom validations to exercise external systems and end-to-end flows. Built-in integrations with AWS model and hosting services and support for custom targets make it flexible for different deployment architectures. The framework can be integrated into CI/CD pipelines to accelerate delivery cycles while preserving stability and confidence in agent behavior.

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