AWS announces aws-bench, an open-source benchmark for AI agents on AWS
AWS has released a research preview of aws-bench, an open-source benchmark measuring how accurately and efficiently AI agents complete real-world AWS tasks. It provides a public test suite derived from real AWS usage for evaluating and improving model and agent performance on AWS operations.
AWS today announced a research preview of aws-bench, an open-source benchmark that measures how accurately and efficiently AI agents complete real-world AWS tasks. Model providers and AI researchers building agents that operate on AWS infrastructure need an objective, reproducible way to measure performance and diagnose failures. aws-bench provides a public suite of test cases derived from analysis of real AWS usage, including investigation, troubleshooting, and infrastructure creation tasks. Each test case pairs a natural-language query with a defined cloud resource state and a ground-truth answer, allowing any agent or model to be scored on a consistent, verifiable basis. Researchers and model providers can use aws-bench to improve foundation model performance on AWS tasks, enhance agent harnesses, and track progress. The release includes an easy-to-use CLI tool to instantiate testing environments, execute and score evaluation runs, and reset resource state. aws-bench is now available on GitHub. To get started, follow the setup instructions on the README.