Agentic AI Endpoints
Conformance & Readiness Requirements
Digital Trade Readiness for Trade Gateways and Canonical AI Endpoints
Assessment Criteria
Each Agentic AI Endpoint is evaluated against key readiness criteria to ensure legal alignment, data standardization, and open architectural interoperability.
Core Evaluation Questions
- MLETR Alignment: Does the Agentic AI Endpoint support alignment with the UNCITRAL Model Law on Electronic Transferable Records (MLETR)?
- ETR Generation & Management: Is it capable of generating and managing Electronic Transferable Records (ETRs), such as electronic Bills of Lading and electronic promissory notes, in a manner designed to satisfy applicable MLETR functional-equivalence and reliability requirements in supported jurisdictions?
- Data Model Mapping: Is the underlying data model mapped to relevant ICC DSI / Standards Toolkit and UN/CEFACT standards, schemas, semantic elements and code lists?
- Anti-Lock-In Architecture: Does the architecture avoid proprietary lock-in, including allowing authorised external systems to query and update permitted Agentic workflows using documented open protocols?
- Data Standardisation: Does it provide data standardisation and interoperability across independently operated systems?
- Open Interfaces: Does it avoid becoming a proprietary “digital island” by exposing open machine interfaces, including REST APIs and, where justified, GraphQL, using published standards-aligned schemas without requiring every counterparty to join the same platform?
Digital Trade Readiness Requirements
Each Trade AI Endpoint will be designed, implemented and tested against the following conformance and interoperability requirements. Machine-verifiable conformance and readiness evidence should be distinguished from any final determination of legal compliance in a particular jurisdiction.
1. MLETR Alignment & Control Profile
Each Agentic AI Endpoint will maintain or reference an MLETR Control Profile where applicble, covering:
Integrity Protection
Exclusive Control Capability
Controller Identification
Transfer of Control
Authorised Amendment
Lifecycle Termination / Surrender
Audit & Transaction Evidence
The profile will becomes a reusable MLETR readiness and conformance profile.
2. ETR Generation & Jurisdiction Profiles
Target Capability: “Capable of generating and managing ETRs in a manner designed to satisfy MLETR functional-equivalence and reliability requirements in supported jurisdictions.”
Each Geographic AI Endpoint will maintain or reference an applicable Jurisdiction Profile. The profile identifies the relevant legal framework, the extent of MLETR adoption or alignment, supported ETR types, and any applicable substantive-law or jurisdiction-specific requirements.
3. ICC DSI / UN/CEFACT Data Conformance
Every relevant canonical field should be traceable through a published standards mapping:
Machine-readable JSON artefacts will publish the applicable schemas and mappings.
4. Anti-Lock-In as an Architectural Rule
Every conforming AI Endpoint should support, where applicable:
OpenAPI Documentation
GraphQL (where justified)
Webhooks / Event Interfaces
Machine-Readable Schemas
Data Export & Import
External Identity & Trust Providers
External Execution Environments
External Settlement Mechanisms
No critical transaction state should exist only in an undocumented proprietary representation. Participation in the network should create value without requiring every counterparty to become a member of the same proprietary platform.
5. Interoperability & Testing Framework
An AI Endpoint will be considered interoperable where an independently operated external system or authorised AI Agent can discover its interface, interpret its published schemas, authenticate appropriately, exchange standards-aligned messages, perform permitted workflow operations and receive machine-readable evidence of the resulting state without dependence on proprietary internal tooling.
External Read
An independently operated external system can retrieve a standards-aligned resource from the AI Endpoint.
External Write
An authorised external party or system can create or update a permitted workflow through a documented open interface.
Cross-Node Transaction
Two independently operated AI Endpoints can exchange standards-aligned data and evidence.
ETR Transfer
A supported transferable record can be transferred between independently governed parties while preserving the required control and integrity.
Exit / Portability
Transaction data can be exported and reconstructed independently using documented formats and schemas.
6 & 7. Machine Manifests & Assessment Agents
6. Machine-Readable Manifests
Each AI Endpoint should publish predictable machine-readable manifests, including core canonical, AI-endpoint and source metadata, standards, interoperability, ETR-capability, trust, schema, and conformance manifests.
The Endpoints team Agent can assess technical AI Endpoint conformance, including manifest completeness, schema validity, endpoint accessibility, interoperability capabilities, and evidence against the specification.
7. Role of the MLETR.com AI Agent
The MLETR.com AI Agent queries AI Endpoint manifests and automatically assesses declared capabilities and machine-verifiable evidence against defined MLETR-aligned readiness criteria, identifying areas of conformance, non-conformance, and evidence requiring further legal verification.
The MLETR.com AI Agent is characterised as an automated MLETR Readiness Assessment Agent, not as an autonomous legal authority that conclusively establishes legal compliance.
8. Separation of Assessment Responsibilities
Technical conformance, functional readiness, system reliability, and legal determinations are separated across distinct assessment layers:
| Assessment Layer / Entity | Primary Role & Responsibilities |
|---|---|
| Endpoints team Agent | Technical and interoperability conformance of the Agentic AI Endpoint. |
| MLETR.com AI Agent | MLETR-aligned functional readiness and machine-verifiable evidence assessment. |
Core Principle
An Agentic AI Gateway should be independently operable, standards-addressable and externally interoperable. Its value should come from participation in the network, not from forcing counterparties into a proprietary platform.
Machines can establish conformance with machine-testable requirements, assess and evidence MLETR readiness, and support reliability assessment. They should not independently declare final legal compliance with MLETR.
Open Schemas → Machine Verification → Interoperable Evidence → Trusted Digital Trade