As a Lead AI Engineer, I’ve seen how big companies like UHG and Optum need their AI agents to be predictable. They have strict security and compliance rules, so they can’t use solutions that give different answers each time. Old rule-based systems are reliable but not very flexible. Pure prompt-based agents depend too much on the model: change a prompt even a little, and the agent can act differently. You also have to grant the agent access to all tools at once, which can create security and maintenance headaches. Working as a team is tough, because if someone tweaks a prompt, the entire agent can break.

NOMOS solves these problems by giving you a clear, step-by-step way to build AI agents:

• Step-Based Architecture: Break the agent’s work into separate steps. Each step handles one part of the task, so you know exactly what happens at each stage. • Tool Access Control: Only give each step the tools it needs. This keeps the agent safer and follows security rules. • Multi-LLM Support: Easily switch between models like OpenAI, Mistral, or Gemini without rewriting your steps. • Production-Ready: Includes session management and scaling options out of the box, so you don’t have to build those from scratch.

Because each step works on its own, different teams can work on different steps at the same time without getting in each other’s way. From quick tests to full-scale deployment, NOMOS grows with your needs.