arXiv:2604.18934v1 Announce Type: new Abstract: Existing AI benchmarks for software automation rarely combine cross-application coordination, autonomous API discovery, and policy adherence. Real business workflows demand all three: a single task may span a CRM, inbox, calendar, and messaging platform - requiring the agent to find the right endpoints, follow a policy document, and write correct data to each system. To address this gap, we introduce AutomationBench, a benchmark for evaluating AI a
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✦ AI Summary· Claude Sonnet
Computer Science > Artificial Intelligence
[Submitted on 21 Apr 2026]
AutomationBench
Daniel Shepard, Robin Salimans
Existing AI benchmarks for software automation rarely combine cross-application coordination, autonomous API discovery, and policy adherence. Real business workflows demand all three: a single task may span a CRM, inbox, calendar, and messaging platform - requiring the agent to find the right endpoints, follow a policy document, and write correct data to each system. To address this gap, we introduce AutomationBench, a benchmark for evaluating AI agents on cross-application workflow orchestration via REST APIs. Drawing on real workflow patterns from Zapier's platform, tasks span Sales, Marketing, Operations, Support, Finance, and HR domains. Agents must discover relevant endpoints themselves, follow layered business rules, and navigate environments with irrelevant and sometimes misleading records. Grading is programmatic and end-state only: whether the correct data ended up in the right systems. Even the best frontier models currently score below 10%. AutomationBench provides a challenging, realistic measure of where current models stand relative to the agentic capabilities businesses actually need.
Subjects: Artificial Intelligence (cs.AI)
Cite as: arXiv:2604.18934 [cs.AI]
(or arXiv:2604.18934v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2604.18934
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Submission history
From: Daniel Shepard [view email]
[v1] Tue, 21 Apr 2026 00:14:59 UTC (420 KB)
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