Verifiable AI Control Plane: Making AI Accountable by Design

AI systems no longer just produce answers. They’re taking action.

Agents now book travel, generate research summaries, write code, and even orchestrate other agents or robots to complete multi-step jobs. As AI grows more autonomous, our own thinking must shift from:

“What can AI do?” to “How can we trust what it does?”

In the first part of this series, Better AI Starts with Verifiable Data via The Sui Stack, we explored how verifiable data underpins trustworthy AI. This follow-up looks at how those same principles extend to control, ensuring that every model, agent, and action can prove what it did and why.

For every model, agent, or robot that acts on behalf of a user or organization, there should be proof that it:

• Used the correct, authorized data.
• Followed approved policies and consent.
• Ran as claimed, without tampering or hidden steps.

That’s where the Verifiable AI Control Plane comes in.

From compliant models to verifiable agents

The Sui AI Stack, composed of Walrus, Seal, Nautilus, and Sui, provides the foundation for this control plane. It lets developers add provenance, policy, and attested execution to any AI or agentic workflow, without rebuilding their existing infrastructure.

Here’s how it works:

• Walrus anchors the data layer. It stores datasets, models, and agent memory with verifiable IDs, giving every action a traceable data lineage.
• Seal defines and enforces access. Agents and models can decrypt data only under defined policies: who requested it, for what purpose, and for how long.
• Nautilus executes confidentially. Agentic workflows, from model inference to robotic task planning, can run in trusted enclaves that produce verifiable proofs of correct execution.
• Sui coordinates it all onchain. Policies, access events, and receipts are recorded transparently, creating a privacy-preserving audit trail for every AI action.

Together, they create a control plane for all AI, where every data fetch, model inference, or agent decision can be proven, authorized, and audited.

Why builders need verifiable AI

For developers building agent frameworks, orchestration systems, or multi-agent environments, trust has become a design requirement, not an afterthought. The Verifiable AI Control Plane provides a foundation where agents can safely query or modify data on Walrus, with every access governed by Seal policies and verified on Sui. 

In multi-agent systems, coordination or shared state occurs only when each agent’s permissions are cryptographically validated, ensuring every interaction is authorized by design. Through Nautilus attestations, every step of an agentic workflow, from data retrieval to computation, can produce a verifiable “digital receipt,” proving what happened and when.

For large model builders and AI service providers, the same primitives translate directly into business value. Each model training or inference run carries cryptographic provenance, reducing compliance risk and audit friction.

Models, adapters, or agent endpoints can be licensed safely per tenant, time, or seat, with access rules enforced onchain. For enterprises that demand visibility, verifiable logs turn compliance from a defensive necessity into a differentiating feature — proof becomes a product advantage.

What this looks like in practice

The Verifiable AI Control Plane extends across a wide spectrum of AI and agentic systems. Model builders can host encrypted weights or adapters on Walrus, control access through Seal, and use Nautilus to generate verifiable proofs for each inference request, creating a trustworthy foundation for licensed model hosting.

In multi-agent pipelines, agents can fetch data from Walrus, decrypt it within defined policies, and collaborate inside Nautilus enclaves so that every input, output, and intermediate step is provable. 

The same framework scales to the physical world.

Fleets of robots, each effectively a physical manifestation of an AI agent, can execute subtasks governed by Sui-recorded policies, where every data fetch, plan update, or completed action emits an auditable event. Even at the network level, specialized agents for search, analytics, or negotiation can operate under a unified Seal policy layer and coordinate securely through Sui.

In all these examples, the Verifiable AI Control Plane functions as the connective tissue that keeps complex AI ecosystems provable, policy-aware, and trustworthy without slowing them down.

Why enterprises need verifiable AI

For enterprises, verifiable AI brings clarity instead of guesswork. It allows teams to confirm that agents and models accessed only approved data, adhered to consent and contractual terms, and provided verifiable records for each critical action, from inference runs to robotic operations. These assurances transform agentic AI from a promising experiment into an enterprise-ready system that can scale safely across teams, regions, and compliance regimes.

For global organizations, whether AI providers, enterprise builders, or compliance and audit platforms, the Verifiable AI Control Plane establishes a shared trust layer that integrates seamlessly with existing infrastructure, ensuring transparency and control by design.

How to get started

Adding verifiability doesn’t require rebuilding your entire system. The first steps are to:

• Pick a workflow, whether it’s an inference API, agent pipeline, or multi-agent coordination job.
• Wrap it with policy and proof using Seal for data access, Walrus for provenance, and Nautilus for attestations.
• Connect proofs to your billing or governance logic so trust translates directly into visibility and revenue.

From there, you can scale horizontally, linking multiple agents, models, or datasets across teams or devices, all governed by a unified layer of verifiable control.

What’s next

In the next part of this series, we’ll look at how these same primitives can power a shared licensing protocol, one that enables any premium content creator or owner to define how their work is accessed and monetized, and lets any consumer or AI agent license it responsibly and transparently.

Because once every model or agent can prove what data it used and how it ran, the next step is making that trust the basis for fair value exchange.