Netomi’s lessons for scaling agentic systems into the enterprise

Netomi’s lessons for scaling agentic systems into the enterprise

Built with OpenAI GPT‑4.1 and GPT‑5.2, Netomi provides a blueprint for scaling safe, predictable agentic systems across the enterprise.

Enterprises expect AI agents to handle messy workflows reliably, honor policies by default, operate under heavy load, and show their work.

Netomi builds systems that meet that high bar, serving Fortune 500 customers like United Airlines and DraftKings. Their platform pairs GPT‑4.1 for low-latency, reliable tool use with GPT‑5.2 for deeper, multi-step planning, running both inside a governed execution layer designed to keep model-driven actions predictable under real production conditions.

Running agentic systems at this scale has given Netomi a blueprint for what makes these deployments work inside the enterprise.

A single enterprise request rarely maps to a single API. Real workflows span booking engines, loyalty databases, CRM systems, policy logic, payments, and knowledge sources. The data is often incomplete, conflicting, or time-sensitive. Systems that depend on brittle flows collapse under this variability.

Netomi designed its Agentic OS so OpenAI models sit at the center of a governed orchestration pipeline built for this level of ambiguity. The platform uses GPT‑4.1 for fast, reliable reasoning and tool-calling—critical for real-time workflows—and GPT‑5.2 when multi-step planning or deeper reasoning is required.

To ensure consistent agent behavior across long, complex tasks, Netomi follows the agentic prompting patterns recommended by OpenAI:

• Persistence reminders to help GPT‑5.2 carry reasoning across long, multi-step workflows
• Explicit tool-use expectations, suppressing hallucinated answers by steering GPT‑4.1 to call tools for authoritative information during transactional operations
• Structured planning, which leverages GPT‑5.2’s deeper reasoning to outline and execute multi-step tasks
• Agent-driven rich media decisions, relying on GPT‑5.2 to detect and signal when a tool call should return images, videos, forms, or other rich, multimodal elements

Few industries expose the need for multi-step reasoning as clearly as airlines, where one interaction routinely spans multiple systems and policy layers. A single question may require checking fare rules, recalculating loyalty benefits, initiating ticket changes, and coordinating with flight operations.

In high-pressure moments—rebooking during a storm, resolving a billing issue, or handling sudden spikes in demand—users will abandon any system that hesitates. Latency defines trust.

Most AI systems fail because they execute tasks sequentially: classify → retrieve → validate → call tools → generate output. Netomi instead designed for concurrency, taking advantage of low-latency streaming and tool-calling stability of GPT‑4.1.

DraftKings, for instance, regularly stress-tests this model, with traffic during major sporting events spiking above 40,000 concurrent customer requests per second. During such events, Netomi has sustained sub-three-second responses with 98% intent classification accuracy.

Enterprise AI must be trustworthy by design, with governance woven directly into the runtime—not added as an external layer. At a technical level, the governance layer handles schema validation, policy enforcement, PII protection, and deterministic fallback.

Netomi’s path shows what it takes to earn enterprise trust: build for complexity, parallelize to meet latency demands, and bake governance into every workflow. OpenAI models form the reasoning backbone, while Netomi’s systems engineering ensures that intelligence is operationally safe, auditable, and ready for Fortune 500 environments.