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Introduction
Conversational artificial intelligence (AI) agents have gained significant traction across various industries, providing seamless and trustworthy user experiences. However, evaluating these agents’ performance is a complex task due to their dynamic and conversational nature. Existing large language model (LLM) benchmarks, such as MT-bench, assess model capabilities but lack the ability to validate application layers. This article discusses the challenges of developing conversational AI agents and introduces Agent Evaluation, an open-source solution that streamlines agent evaluation at scale.
Solution Overview
Agent Evaluation uses LLMs on Amazon Bedrock to enable comprehensive evaluation and validation of conversational AI agents. The solution provides built-in support for popular services, orchestration of concurrent conversations, configurable hooks for validating actions, and integration with continuous integration and delivery (CI/CD) pipelines.
Use Case Overview
This article uses an insurance claim processing agent as an example to demonstrate how Agent Evaluation accelerates the development and deployment of conversational AI agents at scale. The agent is expected to handle various tasks, such as creating new claims, sending reminders for pending documents, gathering evidence, and searching for relevant information.
Testing and Evaluation
To test the agent’s capability to accurately search and retrieve relevant information, a test plan is created using Agent Evaluation. The test plan defines the target agent, evaluator, and expected results. The evaluator uses the InvokeModel API with On-Demand mode, which incurs AWS charges based on input tokens processed and output tokens generated.
Evaluator Considerations
The cost of running an evaluator for a single test is influenced by the number and length of steps, expected results, and target agent responses. You can view the total number of input tokens processed and output tokens generated using the `–verbose` flag when running the test.
Clean Up
To clean up resources, delete the IAM user created for the GitHub Action and the agent itself.
About the Authors
This article was written by Sharon Li, Bobby Lindsey, Tony Chen, Suyin Wang, and Curt Lockhart, all AI/ML Specialist Solutions Architects at Amazon Web Services.
Frequently Asked Questions
Q1: What is Agent Evaluation?
Agent Evaluation is an open-source solution that enables developers to seamlessly integrate agent evaluation into their existing CI/CD workflows using LLMs on Amazon Bedrock.
Q2: What are the benefits of using Agent Evaluation?
Agent Evaluation provides comprehensive evaluation and validation of conversational AI agents at scale, enabling developers to streamline testing and debugging, and accelerating the development and deployment of agents.
Q3: How does Agent Evaluation work?
Agent Evaluation uses the InvokeModel API with On-Demand mode to evaluate the target agent, which incurs AWS charges based on input tokens processed and output tokens generated.
Q4: What are the considerations for using Agent Evaluation?
When using Agent Evaluation, consider the number and length of steps, expected results, and target agent responses, as well as the AWS charges incurred.
Q5: Can I customize Agent Evaluation?
Yes, Agent Evaluation provides customizable test plans and evaluators, allowing developers to tailor the solution to their specific needs.
Conclusion
Agent Evaluation is a powerful solution for evaluating conversational AI agents at scale. By streamlining testing and debugging, developers can accelerate the development and deployment of agents, ensuring reliable and consistent performance. With its customizable test plans and evaluators, Agent Evaluation is an essential tool for any organization building conversational AI agents.
