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🧠 AI🔴 BearishImportance 7/10
EnterpriseOps-Gym: Environments and Evaluations for Stateful Agentic Planning and Tool Use in Enterprise Settings
arXiv – CS AI|Shiva Krishna Reddy Malay, Shravan Nayak, Jishnu Sethumadhavan Nair, Sagar Davasam, Aman Tiwari, Sathwik Tejaswi Madhusudhan, Sridhar Krishna Nemala, Srinivas Sunkara, Sai Rajeswar|
🤖AI Summary
Researchers introduced EnterpriseOps-Gym, a new benchmark for evaluating AI agents in enterprise environments, revealing that even top models like Claude Opus 4.5 achieve only 37.4% success rates. The study highlights critical limitations in current AI agents for autonomous enterprise deployment, particularly in strategic reasoning and task feasibility assessment.
Key Takeaways
- →EnterpriseOps-Gym benchmark tests AI agents on 1,150 tasks across eight enterprise verticals using 164 database tables and 512 tools.
- →Top-performing Claude Opus 4.5 achieved only 37.4% success rate in enterprise planning tasks.
- →Providing oracle human plans improved performance by 14-35 percentage points, indicating strategic reasoning as the main bottleneck.
- →AI agents frequently fail to refuse infeasible tasks, with the best model achieving only 53.9% refusal accuracy.
- →Current AI agents are not ready for autonomous enterprise deployment due to reliability and safety concerns.
Mentioned in AI
Models
ClaudeAnthropic
OpusAnthropic
#ai-agents#enterprise-ai#llm-benchmarks#autonomous-ai#ai-evaluation#claude-opus#ai-limitations#enterprise-deployment
Read Original →via arXiv – CS AI
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