Mubit

Agentic runtime calibration

Build reliable agents with a model for every task.

Point your model calls at Mubit. It calibrates every call at runtime, then builds and refreshes a dedicated model for each task your agents repeat, switching over only when it does better. Your frontier models stay. You own what’s built.

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Mubit / Launch week

Five days.
Five major features.

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00Hours
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One major feature each day

How it works

How Mubit calibrates your runtime

Connect your agents, capture what happens, and use verified outcomes to improve how the next run behaves.

  1. Connect your agent runtime

    Add Mubit to your existing agent workflow. Keep your models, tools and orchestration while connecting runs to shared experience.

    Agent workflows connect through Mubit.

  2. Capture traces and outcomes

    Record the context, decisions and tool calls behind each task. Connect them to tests, application checks or human feedback that show what worked.

    Agent traces are connected to task outcomes.

  3. Calibrate the next run

    Turn useful experience into reusable memory and candidate improvements to prompts, skills and model behavior. Bring the right lessons into the next task.

    Verified experience informs memory, prompts, skills and model behavior.

  4. Evaluate changes before rollout

    Test proposed changes on your tasks, review the results and activate the versions that help. Keep earlier versions available so you can roll back when needed.

    Evaluated changes can be activated while earlier versions remain available.

Banyan models

Open foundations.
Shaped by experience.

Banyan is our family of specialised models, built on open-source and open-weight foundations. We train them through Mubit, using task experience and evaluated outcomes to develop focused capabilities. Our aim is dependable intelligence with a lighter footprint, closer to the work your agents do.

Research previewOpen weights

Banyan Sapling

The first model in the Banyan family focuses on verification. Sapling checks claims against the policy and evidence you provide, identifying conflicts, missing information and inconsistent context—with uncertainty made explicit.

  • Identify policy violations and conflicting context
  • Distinguish missing evidence from supported claims
  • Use structured results with calibrated uncertainty

Explore with Sapling

Policy checks, evidence review and claim validation

View on Hugging Face

Semantic verification

PolicyEvidenceClaim
SupportedViolationMissing informationInconsistent context

Calibrated uncertainty with every result.

Security & trust

Enterprise
Foundations.

We build Mubit around security, privacy and responsible AI. As agents learn and systems evolve, our focus is on clear controls, accountable decisions and evidence you can review.

Explore our security practices, certifications and compliance documentation.

Visit our Trust Center

Make every run
a better starting point.

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Product questions.

What is agentic runtime calibration?

It is the process of improving how an agent behaves using evidence from its runs. Mubit connects traces and verified outcomes to reusable memory, prompt and skill improvements, and model behavior.

Do I need to replace my models or framework?

No. Start with your existing agents, models, tools and orchestration. Add Mubit through a supported SDK or framework integration to connect runtime experience to future tasks.

What can Mubit improve?

Mubit helps agents reuse relevant lessons and turns recurring patterns into candidate prompt and skill improvements. Task-specific models can support suitable workloads as part of the wider runtime.

What counts as useful feedback?

Tests, application checks and human reviews can establish whether a task worked. A trace records what the agent did; a successful API response alone does not prove the answer was correct.

How do I stay in control of changes?

Evaluate candidates on your tasks and review changes before activation. Keep earlier prompt and skill versions available for rollback, and preserve hard constraints in your application.

Can agents share what they learn?

Relevant lessons can be reused across runs and agents within your configured scope. Control who can access that experience and review lessons before widening their use.

How do I measure the improvement?

Compare your current setup with runtime calibration on held-out tasks. Measure task success, repeated errors, latency and cost per successful task. Talk through your use case.

Backed by
leading investors.

and angels from Cloudflare, AWS and Mistral