Generic AI assistants guess. Yours should know. The OML Code AI agent is grounded in your formal model, which means its answers come from verified engineering knowledge rather than statistical vibes.

The agent has perfect knowledge of OML. It queries both assertions and entailments, so it sees everything the reasoner sees. It updates the model following your methodology, so AI-generated content lands method-compliant, not freeform. And it validates and reasons on the model, so it can check its own work before you have to.

It also fits your infrastructure: the agent works with proprietary or open-source LLMs, on your terms. Through the OML MCP server, the same modeling, reasoning, and query tools are available to any AI assistant or agentic workflow you already use.

What becomes possible

• Ask arbitrary questions about your system and get accurate, model-grounded answers
• Generate model content from plain-language descriptions, consistent with your method
• Let AI explain unfamiliar parts of a large model to new team members
• Build agentic engineering workflows on knowledge you can actually trust

AI made models more valuable, not less: structured, queryable knowledge is exactly what trustworthy AI requires. OML Code is where the two meet.

The best way to understand the difference between a grounded agent and a generic one is to see them side by side. Ask us for that demo.