Mubit

Mubit, from Reliable AI, Inc., is an agentic runtime calibration platform that sits around an existing agent workflow. It captures execution traces and verified outcomes, then uses them to improve how later runs behave. Access is currently through a waitlist, and the company also publishes an open-weight research-preview model called Banyan Sapling.

What It Is

Mubit connects to your existing agents, models, tools and orchestration and records the context, decisions and tool calls behind each task. These traces are linked to tests, application checks or human feedback that show whether the task worked. Mubit says it calibrates each model call at runtime and builds and refreshes a dedicated model for each task your agents repeat, switching over only when it does better. Frontier models stay in place.

How the calibration loop works

The workflow has four steps: connect the agent runtime, capture traces and outcomes, calibrate the next run, and evaluate changes before rollout. Useful experience becomes reusable memory and candidate improvements to prompts, skills and model behavior. Candidates are tested on your tasks and reviewed before activation, and earlier versions stay available for rollback. Mubit's documentation says results depend on the workload and no fixed improvement is promised. Hard safety constraints should stay in application code.

Banyan models

Banyan is Mubit's family of specialised models built on open-source and open-weight foundations and trained through Mubit. The first, Banyan Sapling, is a research preview focused on verification. It checks claims against the policy and evidence you supply, flags conflicts, missing information and inconsistent context, and returns structured results with calibrated uncertainty. It is published on Hugging Face.

Security and trust

Mubit's homepage lists SOC 2, ISO 42001, ISO 27001, GDPR and HIPAA compliance badges and points to a Trust Center for documentation.