Agents need more than
better prompts
As models gain access to real systems, reliability depends on evidence, policy, and execution records that live outside the model.
Our focus
Rulith is building the substrate between an AI model and consequential work. We want an agent to remain flexible while its conclusions, permissions, external actions, and completion claims stay inspectable.
How we build
We start with small real workflows, run them end to end, keep failures visible, and move workflow knowledge into reusable capabilities instead of one-off agent code.
Work with us
We are building for developers and teams worldwide, with the United States as our first go-to-market focus. We are looking for design partners who have a workflow where an agent must not guess, skip approval, or claim an external action without a receipt.