AWS announces AWS-bench, an open-source benchmark for AI agents on AWS
{"data":{"items":[{"fields":{"nofollow":"0","noindex":"0","postBody":"<p>Today, AWS announces a research preview of aws-bench, an open-source benchmark that measures how accurately and efficiently AI agents complete real-world AWS tasks. Researchers and model providers can use aws-bench to improve foundation model performance on AWS tasks, improve agent harnesses, and track improvement progress. The release includes an easy-to-use CLI tool to instantiate testing environments, execute and score evaluation runs, and reset resource state.<br>\n<br>\naws-bench is available now on <a href=\"https://github.com/aws-bench/aws-bench\">GitHub</a>.
- ▪{"data":{"items":[{"fields":{"nofollow":"0","noindex":"0","postBody":"<p>Today, AWS announces a research preview of aws-bench, an open-source benchmark that measures how accurately and efficiently AI agents complete real-world AWS tasks.
- ▪Researchers and model providers can use aws-bench to improve foundation model performance on AWS tasks, improve agent harnesses, and track improvement progress.
- ▪The release includes an easy-to-use CLI tool to instantiate testing environments, execute and score evaluation runs, and reset resource state.<br>\n<br>\naws-bench is available now on <a href=\"https://github.com/aws-bench/aws-bench\">GitHu
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| Original publisher | Amazon Web Services, Inc. |
| Canonical URL | https://aws.amazon.com/about-aws/whats-new/2026/07/aws-bench/ |
| Publication time | Sat, 25 Jul 2026 04:42:35 +0000 |
| Retrieval time | 2026-07-25T05:00:22.584Z |
| Last seen | 2026-07-25T05:00:22.584Z |
| Headline source | Publisher (no WeSearch rewrite) |
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| Summary source text | contentText |
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| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
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| Commercial reuse | May the content be reused commercially? | Not permitted |
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Opening excerpt (first ~120 words) tap to expand
{"data":{"items":[{"fields":{"nofollow":"0","noindex":"0","postBody":"<p>Today, AWS announces 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.<br>\n<br>\nEach test case pairs a natural-language query with a defined cloud resource state and a ground-truth answer, so you can score any agent or model on a consistent, verifiable basis.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Amazon Web Services, Inc..
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