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AI Engineering
OpenAI Blog·Jan 8·5 min read
Netomi’s lessons for scaling agentic systems into the enterprise
TL;DR
Enterprises struggle to deploy AI agents reliably at scale, lacking governance and coordination mechanisms for multi-step reasoning tasks. Netomi built a production system combining GPT-4.1 and GPT-5.2 with concurrency controls, governance frameworks, and multi-step reasoning to handle complex workflows.
✦ Why It Matters
Engineers can apply Netomi's concurrency, governance, and multi-step reasoning patterns to deploy production AI agents reliably.
Key Takeaways
- Implement concurrency management to safely handle parallel agent requests in production environments.
- Layer governance controls into agent systems to enforce compliance, safety, and audit requirements.
- Decompose complex tasks into sequential multi-step reasoning workflows for reliability and debuggability.
- Test agentic systems with real enterprise workloads before full deployment to validate failure modes.
Full Summary
Enterprise adoption of AI agents faces critical challenges: coordinating multiple concurrent requests, maintaining governance and safety controls, and executing complex multi-step reasoning reliably in production. Netomi developed a scaled agentic system architecture leveraging GPT-4.1 and GPT-5.2 (large language models trained by OpenAI) as reasoning engines.
The system implements concurrency management to handle parallel agent operations, governance layers to enforce compliance and safety policies, and multi-step reasoning workflows that break complex tasks into sequential, verifiable steps. By combining these three components, Netomi achieved reliable production deployments across enterprise customer bases.
The architecture demonstrates that agentic systems—AI systems that autonomously plan and execute tasks—can scale beyond prototypes when paired with proper operational infrastructure. Results show improved task completion rates and reduced failure modes in real-world enterprise workflows.