Independent Assurance Infrastructure for Agentic AI
Autonomous systems need independent verification.
AI agents are beginning to execute financial transactions, interact with enterprise systems, access sensitive information and make decisions across organisational boundaries.
Traditional security, logging and monitoring systems were not designed to independently establish the identity, authority, intent and provenance of every autonomous action.
INEVRI addresses this challenge.
Powered by the Agentic Provenance Protocol (APP), INEVRI provides an independent evidence and assurance layer for agentic systems, enabling organisations to observe autonomous activity, evaluate actions against authorised intent and policy, and establish verifiable records across platforms and infrastructure.
Our technology is designed to operate alongside existing AI frameworks, security systems and enterprise infrastructure without requiring organisations to replace their underlying technology.
Observe actions. Verify authority. Establish provenance.
Payments & Agentic Commerce
Verifying autonomous financial transactions
As AI agents move from recommending purchases to executing transactions, businesses face a fundamental challenge: establishing whether a transaction genuinely reflects the authority and intent of the person or organisation that initiated it.
INEVRI provides an independent assurance layer across the agentic commerce lifecycle, connecting evidence of consumer intent, agent actions, merchant interactions and payment outcomes.
Applications include:
- Purchase intent verification: Establish evidence linking an agent's actions to the instructions, permissions and constraints authorised by the customer.
- Transaction provenance: Correlate agent activity with checkout sessions, payment requests and transaction outcomes.
- Cross-platform assurance: Evaluate activity as an agent moves between AI assistants, commerce platforms, merchant infrastructure and payment providers.
- Delegated authority monitoring: Identify where an agent's actions may exceed authorised spending limits, merchant restrictions or permitted activities.
- Independent transaction evidence: Generate verifiable evidence receipts to support investigations, audits, dispute handling and risk management.
Relevant ecosystems: Meta MUSE, Stripe, Anthropic Claude, Mastercard and American Express.
Commercial outcome: Greater transaction accountability, improved fraud investigation capabilities and a stronger foundation for trusted autonomous commerce.
Agentic Cybersecurity
Securing autonomous actions beyond traditional access controls
Traditional cybersecurity focuses heavily on identities, permissions, endpoints and network activity.
Autonomous AI introduces an additional dimension: software systems capable of interpreting objectives, selecting tools, executing actions and interacting with other agents without continuous human intervention.
INEVRI extends enterprise security with an independent layer of behavioural observation and action provenance.
Applications include:
- Runtime action monitoring: Observe agent interactions with tools, APIs, files, external services and infrastructure.
- Behavioural assurance: Compare observed activity against expected behaviour, approved operating boundaries and organisational policies.
- Agent identity and origin: Evaluate available evidence about the system or actor responsible for initiating an action, distinguishing verified identity from unverified attribution.
- Cross-boundary visibility: Establish evidence continuity as actions pass between agents, gateways, execution environments and enterprise services.
- Policy evaluation: Identify unauthorised, unexpected or potentially unsafe actions and support enforcement through configured control points.
- Incident investigation: Reconstruct relevant sequences of agent activity using provenance records and evidence receipts.
Relevant environments: NVIDIA OpenShell, enterprise agent runtimes, API gateways, MCP gateways, agent-to-agent systems and cloud infrastructure.
Commercial outcome: Increased visibility into autonomous execution, stronger security investigations and improved governance of enterprise AI deployments.
INEVRI complements sandboxing, identity management and existing security controls rather than replacing them.
Enterprise Document Intelligence
Verifiable AI processing for sensitive business information
Organisations increasingly depend on AI to read, classify, extract, transform, summarise and distribute business-critical documents.
However, an AI-generated result does not automatically establish where information originated, whether it was authorised for processing, how it was transformed or which systems were involved.
INEVRI introduces provenance and independent evidence evaluation into document-processing workflows.
Applications include:
- Document origin and integrity: Record available evidence about document sources, versions, transformations and integrity.
- Processing-chain provenance: Track how information moves through extraction, classification, analysis, generation and approval stages.
- Agent accountability: Attribute processing actions to identifiable systems, workflows and authorised operators where supporting evidence exists.
- Cross-system verification: Establish continuity when documents pass between AI models, enterprise applications and external processors.
- Sensitive-information controls: Evaluate processing activities against declared permissions, workflow policies and data-handling requirements.
- Audit and review: Produce structured evidence records supporting internal investigations, governance and compliance processes.
Sector applications
Healthcare
Support traceability across AI-assisted patient documentation, clinical administration, records processing and information exchange. Establish evidence of which systems accessed or transformed information, under what permissions and with what documented outcome.
Legal
Support verifiable processing histories for contract analysis, document review, due diligence and legal research. Trace source documents, AI-assisted transformations, referenced evidence and human approval stages.
Manufacturing
Establish provenance across engineering documentation, technical specifications, quality assurance records, maintenance procedures and supplier documentation. Monitor how autonomous systems access, interpret and modify information used in operational workflows.
Commercial outcome: Increased document traceability, improved accountability, reduced investigation complexity and stronger governance over AI-assisted information processing.
One Evidence Layer. Multiple Agentic Ecosystems.
The Agentic Provenance Protocol is designed to operate across existing infrastructure rather than requiring a proprietary agent ecosystem.
INEVRI combines boundary observation, evidence correlation, behavioural evaluation and verification-state management to help enterprises distinguish between:
- What an agent claims it did.
- What the underlying systems independently observed.
- What can be verified from available evidence.
- What remains uncertain, conflicting or unverified.
The result is a more accountable architecture for autonomous systems, with evidence that can be assessed beyond the originating agent or platform.
INEVRI · Independent evidence for autonomous systems.