ServiceNow EnterpriseOps-Gym: A practical step toward reliable enterprise AI agents
Enterprise leaders are moving quickly from AI that answers questions to AI that takes action across business workflows. EnterpriseOps-Gym, newly released by ServiceNow Research, is designed to evaluate whether agentic AI can operate reliably in realistic enterprise conditions where success requires more than task completion
Why Does this matter for executives?
In enterprise environments, an agent can complete the “main” task and still fail if it violates policy, misses a dependency, or creates an unintended side effect that compromises data integrity or compliance. EnterpriseOps-Gym is built to make these failure modes measurable through outcome-based verification that checks the final state of the environment, rather than only the sequence of actions taken.
What EnterpriseOps-Gym is, in business terms.
EnterpriseOps-Gym is a benchmark and simulation environment for evaluating AI agents on stateful, multi-step work in enterprise-like systems.
It includes 1,150 expert-curated tasks across eight enterprise domains, spanning both collaboration and operational areas such as Email, Teams, Drive, Calendar, HR, ITSM, CSM, plus a Hybrid category requiring cross-domain execution.
The environment is designed to reflect real enterprise complexity and interconnectedness through a large tool and data surface, including 164 relational database tables and 512 functional tools.
Agents often struggle to refuse infeasible or policy-violating requests reliably, which is an important governance and risk consideration when automation can change system state.
EnterpriseOps-Gym: Environments and Evaluations for Stateful Agentic Planning and Tool Use in Enterprise Settings, Cornell University paper
What the early findings Show
The research provides a timely reality check: even leading frontier models struggle to achieve reliable success rates across these realistic workflows, which reinforces that autonomous enterprise deployment is not yet a “set and forget” capability.
A key insight is that strategic planning under constraints is a primary bottleneck. In the study, providing oracle human-authored plans improves performance by roughly 14 to 35 percentage points, highlighting that process structure and constraint-aware planning are major determinants of success.
The paper also notes that agents often struggle to refuse infeasible or policy-violating requests reliably, which is an important governance and risk consideration when automation can change system state.
What this means for enterprise adoption.
For decision makers, the takeaway is practical: the path to value is not only model selection. It is the combination of workflow readiness, clear policies, and robust controls that make outcomes repeatable and safe.
EnterpriseOps-Gym’s outcome-based approach aligns with how enterprises should measure automation: did the workflow end in the right state, without breaking policy or leaving behind downstream issues.
How Sysintegra can help customers benefit from Enterprise-Gym Ops.
SysIntegra helps organisations translate these insights into real-world outcomes on ServiceNow by:
Identifying high-value workflows that are suitable for agentic automation and defining what “success” means in business and risk terms.
Converting business policies into executable guardrails, approvals, escalation paths, and compliance controls.
Establishing outcome-based testing, verification, and auditability so automation can scale with confidence.
Find out More.
Project Website: enterpriseops-gym.github.io
Project repo: ServiceNow/EnterpriseOps-Gym [github.com]
Paper: EnterpriseOps-Gym: Environments and Evaluations for Agentic Planning
If you are planning to introduce AI agents into service operations, HR, customer service, or employee productivity workflows, SysIntegra can help you build a pragmatic roadmap that balances efficiency gains with governance and risk management. Contact us at hello@sysintegra.com
