Netomi’s lessons for scaling agentic systems into the enterprise

Netomi, an AI customer support company, shared its approach to scaling agentic systems in enterprise environments on the OpenAI Blog. The firm emphasizes combining concurrent execution, governance frameworks, and multi-step reasoning to make AI agents reliable in production. According to Netomi, achieving enterprise-grade reliability requires agents that can handle multiple tasks simultaneously while adhering to strict governance policies, such as access controls and audit trails. They also stress the importance of multi-step reasoning to break down complex requests into manageable sub-tasks. Netomi's infrastructure leverages GPT-4.1 and GPT-5.2 models, but the broader lesson is about system design rather than specific models. For developers building AI workflows, this highlights that agentic systems need more than just a powerful language model—they must integrate with enterprise security, scalability, and compliance requirements. Netomi’s experience shows that careful orchestration of agent behaviors, error handling, and human oversight are critical for moving from prototype to production.