AWS releases open-source benchmark to measure AI agent performance on cloud tasks

AWS announced aws-bench, an open-source benchmark for evaluating AI agents that operate on AWS infrastructure. The tool is designed to measure both accuracy and efficiency as agents complete real-world cloud tasks. Each test case in aws-bench pairs a natural-language query with a predefined cloud resource state and a correct answer, enabling consistent and reproducible scoring across different agents and models. The test cases themselves are derived from analysis of actual AWS usage, covering three main categories: investigation (diagnosing issues), troubleshooting (fixing problems), and infrastructure creation (provisioning resources). Researchers and model providers can use aws-bench to improve how foundation models (the underlying AI systems) perform on AWS-specific tasks, refine the harnesses that control agent behavior, and track progress over time. To make aws-bench accessible, AWS included a command-line tool that automates the setup process, runs evaluation tests, scores the results, and resets the environment state between runs. The benchmark is available immediately on GitHub, with setup and usage instructions provided in the project README. This release addresses a gap in the AI agent evaluation landscape: until now, teams building agents for AWS lacked an objective, standardized way to diagnose failures and measure improvement.

AI agents that operate on cloud infrastructure have grown in capability, but the AI industry lacked a standardized way to evaluate how well these agents perform real-world AWS operations. AWS's decision to release aws-bench as open-source addresses a concrete need: model providers and researchers building agents for AWS had no objective, reproducible benchmark against which to measure performance or diagnose failures. By grounding the benchmark in actual AWS usage patterns—investigation, troubleshooting, and infrastructure creation—AWS ensures the test cases reflect the problems engineers actually encounter. The inclusion of a CLI tool that handles environment instantiation, test execution, scoring, and state reset lowers the friction for adoption, making it easier for external teams to integrate aws-bench into their development workflows. This move may help establish aws-bench as a standard evaluation metric in the agent-building community, similar to how other benchmarks have shaped model development priorities.

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