OUAMARKOM™
Global AI Governance Position Paper

1.1 The United Nations Global AI Governance Framework

The United Nations General Assembly adopted Resolution A/RES/79/325 on 26 August 2025, establishing two distinct but complementary mechanisms:

1. The Independent International Scientific Panel on AI.
2. The Global Dialogue on AI Governance.

The Scientific Panel is intended to provide independent, evidence-based assessments of AI capabilities, opportunities, risks, and impacts. Its 40 independent experts, drawn from all five UN regions, were appointed by the General Assembly in February 2026. The Panel published its Preliminary Report on 1 July 2026 and a Thematic Brief on AI agents, misalignment, and the risk of losing human control on 21 September 2026, issued as an advance unedited version.

The Global Dialogue provides an inclusive international platform through which governments and relevant stakeholders can exchange perspectives, discuss governance approaches, and strengthen international cooperation. It is a forum for consultation and cooperation, not a forum for negotiating binding international agreements.

The inaugural Global Dialogue took place in Geneva on 6–7 July 2026, bringing together delegations from 163 countries, together with representatives of industry, academia, civil society, and the technical community. The second annual Dialogue is scheduled for 3–4 May 2027 in New York.

These developments demonstrate the growing institutional importance of international AI governance and the need for stronger connections between scientific evidence, policy discussions, and practical implementation.

Official References:

• General Assembly Resolution A/RES/79/325.
• Preliminary Report of the Independent International Scientific Panel on AI, July 2026.
• Thematic Brief on AI Agents, September 2026 (advance unedited version).
• Independent International Scientific Panel on AI, FAQ (composition and appointment of the 40 members).
• Documents of the Global Dialogue on AI Governance, 2026, including the Co-Chairs' Summary (participation of 163 countries).

1.2 From Governance Principles to Technical Implementation

A central challenge in AI governance is the transition from high-level principles to mechanisms that can operate within real technical and economic environments.

Principles such as accountability, transparency, human oversight, safety, and responsible deployment require practical implementation mechanisms.

For example:

• Accountability requires traceable records of decisions and actions.
• Human oversight requires meaningful intervention and control capabilities.
• Transparency requires sufficient evidence about how actions were authorized and executed.
• Safety requires enforceable boundaries rather than policy statements alone.
• Responsible deployment requires mechanisms for managing permissions, restrictions, and operational exposure.

These requirements raise an important engineering question:

OUAMARKOM identifies this question as a central motivation for its research direction.

2.1 The Capability–Authority Distinction

OUAMARKOM's research begins with the following distinctions:

• Capability is not Authority. The ability to perform an action does not establish permission to perform it.
• Authority is not Execution. A valid authorization does not guarantee that the execution process will respect its conditions.
• Execution is not Outcome. Completing an operation does not establish that the intended economic result was achieved.
• Evidence is not Verification. The existence of a record does not automatically establish the accuracy, completeness, or independence of the verification process.

These distinctions form an important conceptual foundation for the CE-AI research framework.

2.2 The Economic Execution Governance Gap

OUAMARKOM uses the term Economic Execution Governance Gap to describe the potential absence, insufficiency, or lack of demonstrated coordination among mechanisms that connect:

1. An AI agent's proposed economic action.
2. Explicitly delegated authority.
3. Deterministic policy evaluation.
4. Controlled transaction execution.
5. Authorization lifecycle management.
6. Revocation and intervention mechanisms.
7. Independently verifiable outcomes.

The existence and practical significance of this gap must be investigated through comparative research, technical experimentation, and measurable evidence.

OUAMARKOM therefore treats it as a research problem rather than as an already established universal conclusion.

2.3 Why This Problem Matters

Without effective mechanisms connecting authority to execution, organizations may face several categories of risk:

• Unauthorized or excessive economic actions.
• Execution beyond approved financial or operational limits.
• Continued execution after authorization has expired or been revoked.
• Inconsistent enforcement across concurrent requests.
• Excessive economic exposure resulting from delayed intervention.
• Insufficient evidence to reconstruct and evaluate execution events.
• Difficulty distinguishing legitimate agent decisions from actions that exceed delegated authority.

The challenge is not limited to improving the reasoning capabilities of AI models.

It concerns the institutional and technical conditions under which AI-generated decisions are permitted to become real-world actions.

3.1 Institutional Positioning

OUAMARKOM™
Research & Engineering for Governed AI Economic Execution

• Institutional Category: Deep Tech Research & Engineering Company.
• Research Direction: Economic Engineering of AI.
• Foundational Research Framework: CE-AI™ - Command Economy of AI.
• Proposed Engineering Direction: Controlled Economic AI Engineering.
• Applied Research Area: Governed AI Economic Execution.
• Long-Term Strategic Ambition: Global Economic Execution Infrastructure Provider.

3.2 Strategic Principles

OUAMARKOM adopts the following principles as the basis for its research and engineering activities:

Evidence Before Architecture

Engineering architecture must be supported by research questions, evidence, and measurable requirements.

No Layer Without Evidence

A proposed technical layer must have a clearly identified problem and a defensible reason for its existence.

No Independent Layer Without Independent Value

Architectural separation must demonstrate meaningful functional or security value rather than merely introducing additional complexity.

Every Layer Must Earn Its Existence

Each component must demonstrate its contribution through measurable performance, enforceability, reliability, or verification value.

These principles are intended to prevent premature architectural claims and to maintain a disciplined relationship between research, engineering, and validation.

4.1 Commands

The command represents an instruction, request, or intended action presented to an AI system or an execution environment.

A command may originate from a human user, an authorized institution, an enterprise workflow, or another permitted system.

The research question is how commands can be translated into explicitly defined and assessable execution requests.

4.2 Delegated Authority

Delegated authority defines the permissions under which an AI agent may act.

It may include:

• Authorized action categories.
• Financial or operational limits.
• Approved counterparties or vendors.
• Time restrictions and expiration conditions.
• Contextual and organizational restrictions.
• Conditions for human approval.
• Revocation and suspension requirements.

The central research concern is ensuring that authority is explicitly represented and is not inferred merely from the agent's technical capabilities or internal decisions.

4.3 Governed Execution

Governed execution concerns the enforcement of authorization conditions before and during the execution of an economic action.

This may require deterministic policy evaluation, controlled execution interfaces, state management, concurrency controls, and intervention mechanisms.

The objective is to investigate whether execution can be constrained by an authority model that operates independently of the agent's discretionary reasoning.

4.4 Verifiable Outcomes

Verifiable outcomes concern the evidence required to determine what actually happened.

This includes the relationship between:

• The original command.
• The delegated authority.
• The policy decision.
• The execution event.
• The resulting state.
• The evidence used to verify the outcome.

The existence of logs or records alone is not sufficient to establish independent verification. Verification requires appropriate integrity, completeness, traceability, and validation procedures.

4.5 CE-AI as a Complementary Governance Research Framework

CE-AI is not presented as a replacement for international AI governance principles, legal frameworks, or technical standards.

Its proposed contribution is to investigate an engineering dimension that may support the implementation of selected governance requirements in AI-driven economic environments.

The framework remains subject to further research, comparison with existing work, experimental development, and independent assessment.

5.1 CEP™ - Command Execution Protocol

CEP is a proposed engineering specification intended to structure the evaluation and control of AI-generated economic execution requests.

Its conceptual decision model includes three principal outcomes:

• ADMIT: The request satisfies the applicable authorization and policy conditions.
• REJECT: The request violates an applicable condition or lacks sufficient authorization.
• ESCALATE: The request requires additional review, clarification, or human approval.

Potential policy conditions include:

• Financial limits.
• Approved vendor or counterparty lists.
• Authorization scope.
• Time-to-live (TTL) restrictions.
• Contextual restrictions.
• Revocation status.
• Execution-state requirements.

CEP is currently a proposed engineering direction. It must not be represented as an internationally adopted standard or a fully validated protocol.

5.2 Out-of-Band Control Plane

The Out-of-Band Control Plane is a proposed architectural approach in which authority evaluation and enforcement mechanisms operate outside the agent's ordinary reasoning path.

The research motivation is to reduce the possibility that the same agent whose actions are being governed can independently alter, bypass, or redefine the controls intended to constrain those actions.

The proposed architecture may include:

• Independent authorization evaluation.
• Deterministic policy enforcement.
• Permission and state management.
• Revocation and emergency intervention.
• Controlled execution interfaces.
• Evidence collection and event recording.

Architectural separation alone does not prove security, independence, or resistance to circumvention.

These properties must be established through threat modeling, implementation analysis, adversarial testing, and measurable experimental results.

5.3 Proposed Control Requirements

The engineering research agenda includes investigating:

1. Whether an authorization decision can be enforced independently of the agent.
2. Whether revoked authority can be prevented from authorizing subsequent execution.
3. Whether concurrent requests can be evaluated without violating shared limits.
4. Whether execution can be interrupted within a measurable time boundary.
5. Whether economic exposure can be measured and constrained.
6. Whether execution evidence can be reconstructed and independently assessed.

Any performance targets, including revocation latency or emergency cut-off time, must be treated as engineering targets until demonstrated under documented test conditions.

6.1 Smart Hand™

Smart Hand is conceived as a Governed Economic Action Experimental Environment.

Its purpose is to provide a controlled environment for testing the relationship between AI-generated action requests, delegated authority, policy enforcement, execution control, and evidence generation.

Smart Hand should be treated as an experimental environment, not as a completed commercial product or a production-grade infrastructure platform.

6.2 GEAB - Governed Economic Action Benchmarks

GEAB is a proposed experimental benchmark program covering representative economic execution scenarios.

The initial scenario structure includes:

GEAB-01 - Procurement

Examines procurement requests, approved vendors, authorization limits, and policy enforcement.

GEAB-02 - Financial Transfer

Examines transaction authorization, financial limits, execution controls, and economic exposure.

GEAB-03 - Revocation

Examines whether previously delegated authority can be revoked and whether subsequent execution attempts are effectively prevented.

The scenarios are intended to provide structured conditions for comparing execution mechanisms and evaluating their limitations.

6.3 UEARM - Reference Implementation Direction

UEARM is a proposed reference implementation direction intended to integrate relevant governance and execution mechanisms.

Potential components include:

• Authorization validation.
• Governance and policy evaluation.
• Execution state management.
• Evidence recording.
• Structured event logs.
• Experimental reports.

Its implementation and validation status must be documented separately from its conceptual specification.

6.4 Proposed Research Metrics

The experimental program may evaluate the following metrics:

These metrics are proposed evaluation dimensions. No performance result should be claimed without reproducible test data, documented methodology, and clearly defined experimental conditions.

6.5 Evidence-Based Research Process

OUAMARKOM follows a research process structured as:

This process is intended to ensure that architectural decisions follow evidence rather than precede it.

7.1 Human Responsibility

Technical controls should support meaningful human oversight rather than create an assumption that responsibility can be transferred to an automated system.

The delegation of execution authority must remain attributable to an appropriate human or institutional authority.

7.2 Explicit Delegation

Economic permissions should be defined through explicit authorization conditions rather than inferred from the availability of tools or access credentials.

7.3 Least-Privilege Authorization

An AI agent should receive only the authority necessary for its approved task, subject to applicable restrictions and review requirements.

7.4 Revocability

Delegated authority should be capable of suspension, expiration, or revocation through mechanisms that can be evaluated and tested.

7.5 Traceability and Evidence

Economic actions should generate sufficient records to support investigation, accountability, and outcome verification.

7.6 Proportionality

Control mechanisms should be proportionate to the potential financial, operational, legal, and societal impact of the action.

8.1 Complementarity Rather Than Replacement

The company does not seek to replace existing governance frameworks or establish an alternative global policy regime.

Its research focuses on a specific engineering question: how selected authorization, accountability, oversight, and safety requirements may be translated into enforceable controls over AI-driven economic execution.

8.2 Interoperability and Compatibility

The long-term engineering direction should consider compatibility with existing enterprise systems, authorization models, security mechanisms, audit requirements, and relevant technical standards.

Interoperability must be demonstrated through documented interfaces, integration tests, and practical evaluation.

8.3 No Unsupported Standardization Claims

CE-AI and CEP are proposed research and engineering contributions.

They are not represented as UN-approved frameworks, internationally adopted standards, or formally recognized regulatory instruments.

Any future standardization activity would require a separate process involving relevant technical communities, institutions, and recognized standardization bodies.

9.1 From Governance Requirements to Enforceable Mechanisms

OUAMARKOM seeks to investigate whether technical infrastructure can support the practical enforcement of governance requirements at the point of economic execution.

Potential contributions include:

• Explicit economic authority representation.
• Independent authorization evaluation.
• Deterministic policy enforcement.
• Controlled execution mechanisms.
• Revocation and intervention capabilities.
• Traceable evidence generation.
• Measurable outcome verification.

9.2 A Complementary Engineering Layer

The proposed infrastructure is not intended to determine whether an AI system is universally safe, ethical, or compliant.

Those determinations require broader legal, institutional, technical, and contextual assessments.

Instead, the proposed engineering layer focuses on a more specific question:

This is the practical research question OUAMARKOM seeks to investigate.

9.3 Potential Relevance to International Stakeholders

If supported by credible research and experimental evidence, this work may be relevant to:

• AI developers and agent-platform providers.
• Financial institutions and payment infrastructure providers.
• Enterprises deploying autonomous workflows.
• Public-sector institutions using AI-enabled operational systems.
• Regulators and governance researchers.
• Universities and independent research laboratories.
• Technical standards and interoperability communities.

These are potential stakeholder categories, not confirmed customers, partners, or adopters.

10.1 Commercial Hypothesis

The company intends to investigate whether its research and engineering work could support future infrastructure offerings in areas such as:

• Governed AI transaction execution.
• Delegated authority management.
• Enterprise AI execution control.
• Economic policy enforcement.
• Revocation and intervention infrastructure.
• Execution evidence and verification services.

This represents a strategic commercial hypothesis, not evidence of established market demand, revenue, customer adoption, or product-market fit.

10.2 Potential Value Proposition

The proposed value proposition is not simply to make AI agents more capable.

It is to help organizations establish clearer and more enforceable boundaries between an AI agent's decision-making capabilities and the economic authority it is permitted to exercise.

The value of such infrastructure would ultimately depend on demonstrated improvements in control effectiveness, risk reduction, operational reliability, integration feasibility, and measurable economic value.

Phase I - Research Consolidation and Specification (Months 0–3)

Primary objectives:

• Consolidate the CE-AI foundational research framework.
• Define the economic execution governance problem.
• Establish the Claim–Evidence Register.
• Develop the proposed CEP engineering specification.
• Define the threat model and system boundaries.
• Establish the initial experimental methodology.

Expected outputs:

• CE-AI Foundational Research Whitepaper.
• CEP Control Plane Specification.
• Research and Evidence Traceability Register.
• Experimental Test Specification.

Phase II - Experimental Environment Development (Months 3–6)

Primary objectives:

• Develop the Smart Hand experimental environment.
• Implement initial authorization and policy evaluation mechanisms.
• Define execution states and event schemas.
• Develop the first GEAB scenarios.
• Establish reproducible test conditions.

Expected outputs:

• Smart Hand Experimental Harness.
• GEAB-01 and GEAB-02 test implementations.
• Initial UEARM reference implementation components.
• Structured experimental logs.

Phase III - Revocation, Concurrency, and Stress Testing (Months 6–12)

Primary objectives:

• Develop GEAB-03 revocation experiments.
• Evaluate concurrent authorization requests.
• Measure revocation enforcement latency.
• Evaluate economic exposure under selected failure conditions.
• Test policy enforcement under adversarial and exceptional scenarios.
• Compare alternative architectural approaches.

Expected outputs:

• GEAB experimental reports.
• Revocation and concurrency test results.
• Engineering comparison reports.
• Updated threat model and architecture.

Phase IV - Independent Evaluation and Strategic Validation (Months 12–18)

Primary objectives:

• Consolidate experimental evidence.
• Seek independent technical review where feasible.
• Assess integration requirements.
• Evaluate potential institutional and commercial applications.
• Identify areas requiring further research.

Expected outputs:

• Consolidated experimental evidence package.
• Technical validation report, subject to actual review and testing.
• Updated engineering specifications.
• Institutional and commercial assessment.

14.1 Governance of Delegated AI Authority

International AI governance discussions should consider not only model capabilities and outputs, but also the authority delegated to AI agents operating in economic environments.

14.2 Separation of Reasoning and Execution Authority

Research should examine mechanisms that distinguish an AI agent's reasoning process from the authority required to execute external actions.

14.3 Enforceable Human Oversight

Human oversight should be evaluated through practical intervention, suspension, revocation, and recovery mechanisms rather than through the mere presence of human approval procedures.

14.4 Measurable Revocation and Intervention

The ability to revoke permissions and interrupt execution should be assessed through measurable performance and reliability criteria.

14.5 Evidence-Based Economic Execution

AI-driven economic actions should generate evidence sufficient to support traceability, accountability, and outcome assessment.

14.6 Interoperability of Governance Mechanisms

Research should explore interoperable approaches to representing permissions, evaluating policies, controlling execution, and recording evidence across different AI systems and institutional environments.

14.7 Inclusion of Emerging Research and Engineering Contributions

International governance processes should remain open to contributions from universities, independent research groups, emerging technology companies, and institutions outside the largest established AI markets.

This is particularly important for strengthening scientific diversity and broadening participation in the development of practical governance mechanisms.