EnterpriseOps-Gym: Environments and Evaluations for Stateful Agentic Planning and Tool Use in Enterprise Settings
Large language models are shifting from passive information providers to active agents intended for complex workflows. However, their deployment as reliable AI workers in enterprise is stalled by benchmarks that fail to capture the intricacies of professional environments, specifically, the need for long-horizon planning amidst persistent state changes and strict access protocols. In this work, we introduce EnterpriseOps-Gym, a benchmark designed to evaluate agentic planning in realistic enterprise settings. Specifically, EnterpriseOps-Gym features a containerized sandbox with 164 database tables and 512 functional tools to mimic real-world search friction. Within this environment, agents are evaluated on 1,150 expert-curated tasks across eight mission-critical verticals (including Customer Service, HR, and IT). Our evaluation of 14 frontier models reveals critical limitations in state-of-the-art models: the top-performing Claude Opus 4.5 achieves only 37.4% success. Further analysis shows that providing oracle human plans improves performance by 14-35 percentage points, pinpointing strategic reasoning as the primary bottleneck. Additionally, agents frequently fail to refuse infeasible tasks (best model achieves 53.9%), leading to unintended and potentially harmful side effects. Our findings underscore that current agents are not yet ready for autonomous enterprise deployment. More broadly, EnterpriseOps-Gym provides a concrete testbed to advance the robustness of agentic planning in professional workflows.
69b91a506b3f36c249fbc64e
Shiva Krishna Reddy Malay
claimed_verified
2026-03-18T08:46:07.865Z
661708ddd5db35ab21925ff4
/avatars/55db3307ae86c636f4f475aa97c185e3.svg
Shiva Krishna Reddy Malay
shivakrishnareddymalay
user
shivakrishnareddymalay
69b91a506b3f36c249fbc64f
Shravan Nayak
claimed_verified
2026-03-17T09:36:38.402Z
643ee7606d5be535d28034f6
https://cdn-avatars.huggingface.co/v1/production/uploads/643ee7606d5be535d28034f6/1F241mP_buW74byC2GAWH.jpeg
Shravan Nayak
BAJUKA
user
BAJUKA
69b91a506b3f36c249fbc650
Jishnu Sethumadhavan Nair
claimed_verified
2026-03-17T17:34:32.793Z
64a58f67aee3750f265d3f84
https://cdn-avatars.huggingface.co/v1/production/uploads/64a58f67aee3750f265d3f84/LXLEQ-eamQExzQ-1o4bze.jpeg
Jishnu Nair
jishnunair
user
jishnunair
69b91a506b3f36c249fbc651
Sagar Davasam
69b91a506b3f36c249fbc652
Aman Tiwari
claimed_verified
2026-03-17T09:53:24.566Z
62d913739a5353eef9d7edf3
https://cdn-avatars.huggingface.co/v1/production/uploads/62d913739a5353eef9d7edf3/pRgen2izGJle3ahupOdC7.jpeg
Aman Tiwari
amant555
user
amant555
69b91a506b3f36c249fbc653
Sathwik Tejaswi Madhusudhan
69b91a506b3f36c249fbc654
Sridhar Krishna Nemala
69b91a506b3f36c249fbc655
Srinivas Sunkara
69b91a506b3f36c249fbc656
Sai Rajeswar
claimed_verified
2026-03-17T17:34:30.770Z
642f99079b2484d7d857341b
/avatars/01965cc5a5dbe9c08025a51973462a6a.svg
Sai Rajeswar
rajeswarsai
user
rajeswarsai
EnterpriseOps-Gym: Environments and Evaluations for Stateful Agentic Planning and Tool Use in Enterprise Settings
Large language models are shifting from passive information providers to active agents intended for complex workflows. However, their deployment as reliable AI workers in enterprise is stalled by benchmarks that fail to capture the intricacies of professional environments, specifically, the need for long-horizon planning amidst persistent state changes and strict access protocols. In this work, we introduce EnterpriseOps-Gym, a benchmark designed to evaluate agentic planning in realistic enterprise settings. Specifically, EnterpriseOps-Gym features a containerized sandbox with 164 database tables and 512 functional tools to mimic real-world search friction. Within this environment, agents are evaluated on 1,150 expert-curated tasks across eight mission-critical verticals (including Customer Service, HR, and IT). Our evaluation of 14 frontier models reveals critical limitations in state-of-the-art models: the top-performing Claude Opus 4.5 achieves only 37.4% success. Further analysis shows that providing oracle human plans improves performance by 14-35 percentage points, pinpointing strategic reasoning as the primary bottleneck. Additionally, agents frequently fail to refuse infeasible tasks (best model achieves 53.9%), leading to unintended and potentially harmful side effects. Our findings underscore that current agents are not yet ready for autonomous enterprise deployment. More broadly, EnterpriseOps-Gym provides a concrete testbed to advance the robustness of agentic planning in professional workflows.