Netomi’s lessons for scaling agentic systems into the enterprise
Netomi uses GPT-4.1 and GPT-5.2 to scale enterprise AI agents, combining concurrency, governance, and multi-step reasoning for reliable production workflows. The approach enables Netomi to deploy AI agents that can handle complex tasks and interact with multiple systems. Builders can learn from Netomi's experience in scaling agentic systems for enterprise use. Netomi's methods focus on reliability and governance.
Key takeaways
- Netomi uses GPT-4.1 and GPT-5.2 for enterprise AI agents.
- Concurrency, governance, and multi-step reasoning are key.
- Reliability and governance are emphasized for production workflows.
Netomi uses GPT-4.1 and GPT-5.2 to scale enterprise AI agents, combining concurrency, governance, and multi-step reasoning for reliable production workflows. The approach enables Netomi to deploy AI agents that can handle complex tasks and interact with multiple systems. Builders can learn from Netomi's experience in scaling agentic systems for enterprise use. Netomi's methods focus on reliability and governance.
Key takeaways
- Netomi uses GPT-4.1 and GPT-5.2 for enterprise AI agents.
- Concurrency, governance, and multi-step reasoning are key.
- Reliability and governance are emphasized for production workflows.