Governed AI agents with cryptographic proof of every authorization

Agent Command issues cryptographically-scoped mandates to AI agents. Every action is bounded, logged, and provable, satisfying the requirements of MAS AI Risk Guidelines, MindForge, and enterprise governance frameworks.

The problem

AI agents executing workflows with real consequences need provable authority

Enterprise AI agents are executing workflows with real financial and regulatory consequences. The authorization for those actions typically lives in a prompt or a config file, neither of which is auditable, verifiable, or tamper-proof.

Application-layer policy can be overridden or misread

An AI agent's behavior is controlled by configuration, prompts, and application logic. A sufficiently capable model can misinterpret, circumvent, or hallucinate past those controls. None of it is cryptographically enforced.

Regulators are requiring explainable, auditable AI

MAS AI Risk Management Guidelines, the EU AI Act, and enterprise governance frameworks all require explainability, human oversight, and tamper-proof audit trails for AI systems in regulated contexts. Reconstructed logs don't satisfy this.

No current framework can prove what an agent was authorized to do

When an AI agent takes an action, the question any regulator or auditor will ask is: who authorized this, and under what mandate? Without cryptographic agent identity, there is no verifiable answer.

The solution

Agents governed by what they were authorized to do

Know Your Agent: verified machine identity for every action

Every AI agent receives a signed, cryptographically-scoped credential (Smart VC) that identifies it, names its principal, and bounds what it can do. KYA brings the same rigour to machine identity that KYC brings to human identity.

Scoped mandates: agents can only act within what was signed

Agent credentials encode specific capabilities, spend limits, data types, and time windows. Actions outside the credential scope are rejected at the infrastructure level rather than by application-layer policy that can be overridden or misread.

Tamper-proof audit trail for every agent decision

Every action taken by an agent is logged in a Merkle-tree-backed ledger anchored to the T3 Network. The audit trail is cryptographically tamper-evident. Regulators receive provable records rather than reconstructed logs.

Instant revocation when an agent's mandate expires or is withdrawn

Agent credentials can be revoked immediately. When a project ends, a principal changes, or an incident occurs, the next action attempted by that agent fails at the credential layer without a manual process or application update.

Features

The tools that power enterprise AI governance

Agent Command and the Agent Developer Kit work together. Agent Command issues mandates and logs actions; the ADK enforces them at runtime.

Every agent gets a verifiable identity scoped to its mandate

Agent Command issues Smart Verifiable Credentials to AI agents. Each credential is cryptographically bound to a principal, scoped to specific capabilities, and limited in time. Agents carry cryptographic proof of their authorized scope, independently of what they were prompted to do.

Built for regulated AI deployment

MAS AI Risk Guidelines alignment

Agent Command's four pillars — Visibility, Control, Privacy, and Auditability — map directly to MAS AI Risk Management Guidelines (2025) and the principles from MAS Project MindForge.

Merkle-tree audit integrity

Agent action logs are structured as a Merkle tree anchored to the T3 Network. Each entry is cryptographically linked to the prior. Tampering with any record invalidates all subsequent entries.

Quantum-resistant agent credentials

Smart VCs are signed using FIPS 204 post-quantum cryptography. Agent governance records and audit trails remain cryptographically valid against future threats.

For developers

Build governed AI agents with the Agent Developer Kit

The Agent Developer Kit exposes APIs for agent credentialing, mandate enforcement, and audit log access, compatible with major LLM providers and enterprise AI frameworks.

Security & compliance

Built for institutions that operate in regulated AI environments

We take security seriously and have implemented robust measures to protect your data.

FAQs

Questions about AI governance in your regulatory context?

01. What MAS guidelines does Agent Command address?
Agent Command is designed to align with the MAS AI Risk Management Guidelines (2025) and the principles from MAS Project MindForge. The four pillars — Visibility, Control, Privacy, and Auditability — map directly to the MAS framework's requirements for explainability, human oversight, data governance, and audit readiness.

02. How do AI agents receive authorization to act?
Agents receive Smart Verifiable Credentials issued by a human principal through Agent Command. These credentials are cryptographically scoped — bounded to specific capabilities, spend limits, or data types — and time-limited. Any action outside the credential scope is rejected at the infrastructure level, not at the application layer.

03. How does the tamper-proof audit trail work?
Agent Command logs every decision, data access, and instruction in a Merkle-tree-backed ledger anchored to the T3 Network. Each entry is cryptographically linked to the previous: a tampered record invalidates all subsequent entries. When a regulator asks what an AI agent did and why, the answer is a cryptographic proof rather than a reconstructed log.

04. Can this work with existing enterprise AI frameworks and LLM providers?
Yes. The Agent Developer Kit integrates with major LLM providers and enterprise AI frameworks. Agent Command sits as a governance and credential layer on top of existing AI infrastructure and does not require replacing the underlying models or orchestration systems.