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The paper introduces a continuous benchmark generation process for evaluating enterprise-scale LLM agents in dynamic environments where ground truth data is scarce. They leverage semi-structured documents expressing high-level intent and use LLMs to generate benchmarks, addressing the limitations of fixed benchmark sets for evolving agent requirements. The approach is instantiated in a service migration case study, demonstrating a maintainable evaluation framework that provides rapid feedback and facilitates targeted agent improvements.
Forget hand-crafted benchmarks: this paper shows how LLMs can continuously generate relevant evaluation datasets for enterprise AI agents from just a few semi-structured documents.
The rapid adoption of AI agents across domains has made systematic evaluation crucial for ensuring their usefulness and successful production deployment. Evaluation of AI agents typically involves using a fixed set of benchmarks and computing multiple evaluation metrics for the agent. While sufficient for simple coding tasks, these benchmarks fall short for enterprise-scale agents, where services and requirements evolve continuously and ground-truth examples are sparse. We propose a process of benchmark generation that helps evolve the benchmarks as the requirements change and perform robust evaluation of evolving AI agents. We instantiate this approach for a case study of service migration from one deployment platform to another at a large public enterprise. Our approach relies on semi-structured documents where developers express the high-level intent, and uses state-of-the-art LLMs to generate benchmarks from just a small number of such documents. Overall, this process results in a maintainable evaluation framework, enabling rapid feedback on agent performance and facilitating targeted improvements.
OpenAI