CloudOpsBench Tests AI Agents on AWS Cloud Operations Tasks

1 reports 1 reporting sources Updated 2 hours ago

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The University of Michigan team led by Ang Chen released CloudOpsBench 1.0, an open-source benchmark of 45 AWS DevOps tasks. Claude Fable 5.1 completed 60% of the tasks, while GPT-6 Astra completed 53%. The benchmark runs tasks identically in real AWS and an emulator. The team attributes about 80% of failures to reasoning problems, such as misjudging a task’s scope, overlooking relevant services, or stopping too early. The other roughly 20% involved incorrect actions, some with irreversible consequences. The project is licensed under Apache 2.0; extensions for GCP, Azure, and OCI are planned.

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Latest development11/5 00:42 (UTC+8)

CloudOpsBench Finds Reasoning Is the Main Bottleneck for AI in Cloud Operations The Decoder

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Claude Fable 5.1#5 overall$10 input / $50 output per million tokens
GPT-6 Astra#3 overall$10 input / $50 output per million tokens

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11/5

00:42
The Decoder
CloudOpsBench Finds Reasoning Is the Main Bottleneck for AI in Cloud Operations
CloudOpsBench, a new open-source benchmark for AI agents handling cloud DevOps tasks, finds that reasoning is the main bottleneck. Version 1.0 includes 45 AWS tasks; Claude Fable 5.1 solved 60 percent and GPT-6 Astra 53 percent. The team led by Ang Chen at the University of Michigan attributes about 80 percent of failures to agents misjudging task scope, missing relevant services, or stopping too early, while roughly 20 percent involve wrong actions, some with irreversible consequences.

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