A Unified Economic Action Representation Model for Governed AI Execution
Proposed Model: Unified Economic Action Representation Model (UEARM)
Operational Framework: CE-AI™ Pipeline
Research Domain: Economic Engineering of AI
Research Track: Governed AI Economic Agency
Document Version: Research Draft v1.0
Date: November 3, 2026
Research Status: Conceptual and Technical Proposal - Experimental Validation Pending
Research & Development by OUAMARKOM™
Abstract
The increasing ability of artificial intelligence systems to initiate, coordinate, and execute economically consequential actions introduces a fundamental engineering challenge: how can an economic action be represented, authorized, governed, executed, and independently verified throughout its lifecycle?
Existing approaches address important parts of this challenge, including identity management, authorization policies, delegated mandates, transaction processing, and auditability. However, the relationship between these components across the complete lifecycle of an AI-initiated economic action requires further systematic investigation.
This paper proposes the Unified Economic Action Representation Model (UEARM), a conceptual model designed to represent the essential elements of an economic action within a governed AI execution environment.
UEARM is complemented by the CE-AI™ Pipeline, a proposed operational sequence that connects intent formation, command generation, action normalization, identity and authority resolution, policy evaluation, governance decisions, execution enforcement, evidence capture, outcome verification, and governed learning.
The central research proposition is that economically consequential AI actions should not be treated merely as outputs generated by intelligent systems. They should be represented as structured, traceable, and independently assessable operational objects.
The paper defines the proposed model, identifies its principal components, establishes preliminary governance invariants, and outlines an experimental methodology for evaluating its technical feasibility.
UEARM is presented as a research proposal and a candidate foundation for future interoperability and standardization research. Its effectiveness, technical novelty, and suitability for broader adoption remain subject to experimental validation, comparative evaluation, and independent review.
Keywords:
AI Economic AgencyGoverned AI ExecutionEconomic Action RepresentationCE-AIUEARMDelegated AuthorityExecution GovernanceEvidence-Based VerificationEconomic Engineering of AI
1. Introduction
Artificial intelligence systems are increasingly moving beyond information processing and recommendation toward initiating and coordinating actions that may have direct economic consequences.
These actions may include:
Purchasing goods and services.
Initiating financial transfers.
Allocating organizational resources.
Managing procurement workflows.
Executing contractual or operational commitments.
Coordinating transactions across multiple digital systems.
As this transition develops, a distinction becomes increasingly important:
The ability of an AI system to reason about an economic action does not automatically establish its authority to execute that action.
A system may possess sufficient computational capability to formulate a transaction while lacking the authorization, policy compliance, execution permissions, or contextual conditions required to carry it out.
This creates a research problem extending beyond model intelligence.
The engineering challenge concerns the complete relationship between an economic intention and its verified operational outcome.
A reliable approach therefore requires more than a command or an authorization decision. It requires a structured representation of the action and a defined process for governing its lifecycle.
This paper investigates that requirement through two complementary proposals:
1. UEARM: A unified representation model for economic actions.
2. CE-AI™ Pipeline: A proposed operational framework for governing the lifecycle of those actions.
The objective is not to claim that existing authorization or transaction technologies are insufficient in every context. Rather, it is to investigate whether a unified economic-action representation can improve consistency, traceability, interoperability, and verification across heterogeneous AI execution environments.
2. Research Problem
Current technical approaches may represent different aspects of an economic action through separate structures and systems.
For example:
Identity systems establish who or what is acting.
Authorization systems evaluate whether an action is permitted.
Policy engines evaluate applicable rules and constraints.
The existence of these individual capabilities does not, by itself, guarantee that they form a consistent representation of an economic action across its complete lifecycle.
The research problem can therefore be expressed as follows:
How can an AI-initiated economic action be represented through a unified operational structure that preserves the relationship between identity, authority, economic parameters, governance decisions, execution states, evidence, and verified outcomes?
This question leads to five supporting challenges:
1. Maintaining consistency between the requested action and the authorized action.
2. Preventing the assumption that model capability implies execution authority.
3. Preserving authorization constraints during execution.
4. Representing cancellation, rejection, failure, and partial execution.
5. Establishing traceable evidence connecting execution events to their resulting outcomes.
3. Research Objectives
This research has seven primary objectives:
1. Propose a structured representation of economically consequential AI actions.
2. Define the essential information required to describe an economic action.
3. Establish a lifecycle connecting action formation, authorization, execution, and verification.
4. Identify preliminary governance invariants applicable to economically consequential actions.
5. Develop testable hypotheses for evaluating the proposed model.
6. Define an experimental pathway for implementation and comparative assessment.
7. Investigate the potential of the model to support future interoperability and standardization initiatives.
4. Research Background and Related Work
UEARM is proposed within an existing technical landscape that already includes several relevant approaches.
4.1 Policy-Based Authorization
Policy engines such as Open Policy Agent (OPA) provide mechanisms for evaluating authorization and policy decisions separately from the systems that enforce those decisions.
Cedar provides a structured authorization model based on principals, actions, resources, and contextual attributes.
These approaches provide relevant foundations for examining policy evaluation and authorization semantics.
4.2 Delegated Economic Authority
Agentic commerce initiatives, including Google’s Agent Payments Protocol (AP2), investigate mechanisms for representing delegated authority and authorizing payment-related activities.
Such work is particularly relevant to the relationship between user intent, delegated mandates, transaction authorization, and payment evidence.
4.3 Research Position
UEARM does not claim to introduce identity, authorization, policy evaluation, delegated authority, transaction mandates, or audit logging as independent technical concepts.
Its research focus is the proposed integration of these concerns into a lifecycle-oriented representation of an economic action.
The distinction between existing components and the proposed contribution must remain explicit.
The model’s novelty cannot be established solely through conceptual differentiation. It requires a documented prior-art review and comparative technical evaluation.
5. The Unified Economic Action Representation Model (UEARM)
5.1 Conceptual Definition
The Unified Economic Action Representation Model (UEARM) is a proposed structured representation of an economic action initiated, requested, coordinated, or executed within an AI-enabled environment.
It is designed to preserve the relationships between:
The identity and provenance of the initiating entity.
The economic action being requested.
The associated economic parameters.
The authority under which the action may be performed.
The applicable policies and constraints.
The execution environment.
The governance decision.
The execution state.
The evidence associated with execution.
The resulting outcome.
The model is intended to provide a consistent conceptual structure that can be evaluated across different economic-action scenarios.
5.2 Abstract Representation
The proposed model can initially be expressed as:
UEARM = {I, A, E, D, P, C, G, X, V, O}
Where:
Symbol
Component
Description
I
Identity & Provenance
Identity of the initiating entity and origin of the request
A
Economic Action
Definition of the requested economic operation
E
Economic Parameters
Amount, currency, quantity, recipient, and related parameters
D
Delegated Authority
Scope and conditions of the authority granted
P
Policy & Constraints
Applicable rules, limits, and restrictions
C
Execution Context
Operational environment and relevant contextual attributes
G
Governance Decision
Authorization, rejection, escalation, or other governance decision
X
Execution State
Current and historical execution status
V
Evidence & Verification
Evidence records and verification results
O
Outcome
Resulting economic or operational outcome
This abstract representation is a conceptual model. It is not, by itself, a finalized data standard or implementation specification.
5.3 Component Definitions
5.3.1 Identity and Provenance (I)
Establishes the identity and origin of the economic-action request.
Potential attributes include:
Agent identifier.
User or organization identifier.
Request identifier.
Initiating system.
Request timestamp.
Provenance references.
5.3.2 Economic Action (A)
Defines the operation being requested.
Examples include:
Procurement.
Financial transfer.
Resource allocation.
Payment initiation.
Contractual commitment.
The action should be represented through a defined action type and a structured set of parameters.
5.3.3 Economic Parameters (E)
Describe the economic characteristics of the requested action.
Potential attributes include:
Amount.
Currency.
Quantity.
Beneficiary or recipient.
Source account or resource.
Transaction reference.
Applicable economic limits.
The model must distinguish between requested values, authorized values, and actually executed values.
5.3.4 Delegated Authority (D)
Represents the authority under which an action may be performed.
Potential attributes include:
Granting principal.
Authorized agent.
Permitted action types.
Monetary limits.
Validity period.
Scope restrictions.
Revocation status.
Delegation provenance.
Authority must be evaluated against the specific action rather than inferred from the agent’s technical capabilities.
5.3.5 Policy and Constraints (P)
Represents applicable governance requirements.
These may include:
Organizational policies.
Transaction limits.
Recipient restrictions.
Time-based restrictions.
Risk thresholds.
Regulatory or contractual constraints.
5.3.6 Execution Context (C)
Describes the environment in which the action is evaluated and potentially executed.
Potential attributes include:
Execution environment.
System identity.
Relevant contextual attributes.
Resource availability.
Transaction state.
Concurrent execution conditions.
5.3.7 Governance Decision (G)
Records the result of evaluating the action against authority and policy requirements.
Possible decisions include:
ADMIT.
REJECT.
ESCALATE.
The decision should be associated with the evaluated action, the applicable authority, the relevant policy version, and the decision timestamp.
5.3.8 Execution State (X)
Represents the operational state of the action.
Potential states include:
CREATED.
PENDING.
AUTHORIZED.
REJECTED.
EXECUTING.
PARTIALLY_EXECUTED.
COMPLETED.
FAILED.
CANCEL_REQUESTED.
CANCELLED.
EXPIRED.
The exact state machine requires formal specification and validation.
5.3.9 Evidence and Verification (V)
Represents the records required to assess whether execution occurred and whether the resulting state is supported by reliable evidence.
Potential evidence includes:
Execution receipts.
Transaction identifiers.
Signed event records.
System acknowledgements.
State-transition records.
External settlement confirmations.
Evidence capture and independent verification must be treated as distinct functions.
5.3.10 Outcome (O)
Represents the resulting economic or operational state.
Potential outcomes include:
Successful completion.
Rejected transaction.
Failed execution.
Partial completion.
Reversed transaction.
Unresolved or disputed outcome.
A recorded execution event must not automatically be interpreted as proof of economic settlement.
6. The CE-AI™ Operational Pipeline
6.1 Proposed Lifecycle
The proposed CE-AI™ Pipeline connects the UEARM representation to an operational lifecycle:
Identifies the initiating entity and evaluates its delegated authority
5. Policy & Constraint Evaluation
Evaluates applicable restrictions and conditions
6. Governance Decision
Produces an explicit governance decision
7. Execution Enforcement
Enforces the authorized execution boundary
8. Evidence Capture
Records execution-related evidence
9. Outcome Verification
Evaluates the resulting operational or economic outcome
10. Governed Learning
Uses validated information to improve future processes without automatically expanding authority
6.3 Separation of Responsibilities
The pipeline is designed around the following distinctions:
Intent is not authorization. Command generation is not execution. Capability is not authority. Authority is not execution. Execution is not settlement. Evidence is not verification. Learning is not authorization.
These distinctions form preliminary governance principles for experimental evaluation.
7. Proposed Governance Invariants
The following invariants are proposed as testable requirements.
Invariant 1 - Capability–Authority Separation
An AI system’s reasoning or computational capability must not independently grant it economic authority.
Invariant 2 - Action-Bound Authorization
Authorization must correspond to the specific economic action and its relevant parameters.
Invariant 3 - Authority Validity
Authority must remain valid at the defined enforceable execution boundary.
Invariant 4 - Economic Limit Preservation
Economic limits must remain enforceable across concurrent and cumulative actions.
Invariant 5 - Explicit Revocation Semantics
The system must define how revocation affects pending, authorized, and executing actions.
Invariant 6 - Execution Traceability
Execution events must be associated with identifiable commands and governance decisions.
Invariant 7 - Evidence Provenance
Evidence must identify its source, origin, and relationship to the action being evaluated.
Invariant 8 - Independent Outcome Assessment
The system must distinguish an execution acknowledgement from independently established outcome evidence.
Invariant 9 - Governed Learning
Learning or adaptation must not automatically expand delegated authority.
Invariant 10 - Failure Representation
Rejection, timeout, cancellation, partial execution, and unresolved outcomes must be explicitly representable.
These invariants are proposed research requirements, not claims of experimentally established guarantees.
8. Illustrative Data Representation
The following example illustrates how a UEARM-based economic action might be represented.
It is intended to communicate the conceptual structure, not to serve as a finalized JSON Schema.
In this illustrative scenario, the requested amount exceeds the stated authorization limit.
The example demonstrates how the proposed representation could preserve the relationship between the requested economic action, delegated authority, governance decision, execution status, and recorded outcome.
It does not constitute evidence of a completed experiment or validated system performance.
9. Research Questions and Hypotheses
9.1 Research Questions
RQ1: Can UEARM provide a consistent representation of economically consequential AI actions across different action categories?
RQ2: Does a unified representation improve traceability between commands, delegated authority, governance decisions, execution events, and outcomes?
RQ3: Can explicit action-bound authorization reduce inconsistencies between requested and authorized economic parameters?
RQ4: How should revocation be represented and enforced across different execution states?
RQ5: Can the proposed representation support interoperability between heterogeneous AI agents and execution systems?
RQ6: What additional computational and operational overhead is introduced by the proposed lifecycle?
9.2 Research Hypotheses
H1: A structured economic-action representation can improve the consistency of action records across the defined lifecycle.
H2: Explicit separation of capability, authority, governance, and execution can improve the identification of unauthorized-action attempts.
H3: A unified evidence model can improve the traceability of execution events and resulting outcomes.
H4: Explicit execution-state semantics can improve the assessment of cancellation, revocation, and partial-execution scenarios.
H5: The proposed representation can provide a basis for evaluating interoperability across multiple execution environments.
These hypotheses require experimental testing and must not be interpreted as established findings.
10. Experimental Methodology
The proposed evaluation will use a controlled experimental environment.
10.1 Experimental Environment
The OUAMARKOM™ Smart Hand™ environment is proposed as an experimental setting for implementing and testing selected UEARM and CE-AI Pipeline components.
The environment is intended to support:
Structured action requests.
Delegated authority evaluation.
Policy-based decisions.
Execution-state transitions.
Revocation scenarios.
Evidence recording.
Outcome assessment.
Smart Hand™ is an experimental environment, not a claim of production readiness.
10.2 Proposed Test Scenarios
Scenario A - Authorization Limit Violation
An agent requests an economic action exceeding its delegated limit.
Evaluation: whether the system rejects or appropriately escalates the action before execution.
Scenario B - Cumulative Authority Exposure
Multiple individually valid actions collectively exceed an established exposure threshold.
An authority grant is revoked while an action is pending or executing.
Evaluation: whether the system applies clearly defined revocation semantics at the relevant execution boundary.
Scenario D - Concurrent Execution
Multiple actions attempt to consume the same delegated authority.
Evaluation: whether race conditions or double-spending of authority can be prevented.
Scenario E - Partial Execution
An action is only partially completed.
Evaluation: whether the model distinguishes partial execution from successful completion.
Scenario F - Evidence and Outcome Discrepancy
An execution acknowledgement is received without sufficient evidence of the intended economic outcome.
Evaluation: whether the system preserves the distinction between execution records and outcome verification.
10.3 Proposed Evaluation Metrics
Metric
Purpose
Authorization Consistency
Measures alignment between requested and authorized actions
Unauthorized Execution Rate
Measures unauthorized actions reaching the execution boundary
Revocation Enforcement
Evaluates the effectiveness of revocation
Cumulative Exposure Control
Evaluates aggregate authority-limit enforcement
State Transition Integrity
Measures consistency of execution-state transitions
Evidence Coverage
Measures the proportion of actions with required evidence
Outcome Verification Coverage
Measures the proportion of outcomes independently verified
Execution Latency
Measures additional operational delay
Recovery and Failure Handling
Evaluates the handling of failed and unresolved actions
No performance figures are asserted in this paper. Numerical targets and acceptance criteria must be defined before experimentation.
11. Expected Research Contributions
The proposed research aims to contribute in five areas.
11.1 A Unified Conceptual Representation
A structured conceptual model for describing the lifecycle of an economically consequential AI action.
11.2 Lifecycle-Oriented Governance
A proposed connection between identity, delegated authority, policy evaluation, execution enforcement, evidence, and outcome verification.
11.3 Explicit Execution Semantics
A foundation for studying state transitions, revocation, partial execution, and failure conditions.
11.4 Experimental Evaluation
A research pathway for testing whether unified representation improves consistency, traceability, and governance effectiveness.
11.5 Future Interoperability Research
A candidate foundation for investigating whether different AI systems and economic execution environments can exchange and interpret structured action representations consistently.
These contributions are proposed research outcomes. Their actual significance will depend on implementation and experimental findings.
12. Implications for Future Industry Standardization
A unified economic-action representation may have implications for interoperability and governance across AI-enabled economic systems.
However, the development of an industry standard requires considerably more than a conceptual framework.
A credible standardization pathway would require:
1. A formally defined technical specification.
2. Precise field semantics and data types.
3. Normative validation rules.
4. Defined authorization and execution semantics.
5. A formal execution-state machine.
6. Security and threat-model analysis.
7. Interoperability testing.
8. Independent technical review.
9. Versioning and backward-compatibility requirements.
10. Participation from relevant technical and institutional stakeholders.
Accordingly, UEARM should currently be described as a proposed technical model and candidate foundation for future standardization research.
It is not presented as an approved, adopted, or recognized industry standard.
13. Relationship to the OUAMARKOM™ Research Architecture
The proposed model occupies a specific position within the broader OUAMARKOM™ research direction.
Research Component
Primary Function
CE-AI™
Research framework for governed AI economic agency
UEARM
Proposed representation of an economic action
CE-AI™ Pipeline
Proposed lifecycle for governing economic actions
CEP™
Research into controlled command-to-execution mechanisms
Smart Hand™
Experimental environment for testing implementation hypotheses
GEAB™
Proposed benchmark scenarios and evaluation methodology
The relationship can be summarized as follows:
UEARM defines what information an economic action should represent.
The CE-AI™ Pipeline defines how the action moves through its proposed lifecycle.
CEP™ investigates mechanisms for controlling the transition toward execution.
Smart Hand™ provides an environment for experimental implementation.
GEAB™ investigates how the resulting behavior can be evaluated.
These components remain part of an evolving research and development program. Their integration and technical necessity must be established through evidence.
14. Limitations
This paper has several important limitations.
First, UEARM is a proposed conceptual representation rather than a fully validated technical specification.
Second, the model’s novelty has not been conclusively established through a comprehensive prior-art review.
Third, the proposed lifecycle has not yet been demonstrated to provide measurable improvements over existing approaches.
Fourth, the proposed governance invariants require formal definitions and experimental evaluation.
Fifth, economic execution environments differ in their legal, operational, technical, and settlement requirements. A universal representation may therefore require domain-specific extensions.
Sixth, evidence availability does not necessarily establish the correctness, authenticity, or completeness of an economic outcome.
Finally, standardization requires independent review and broader stakeholder participation beyond the development of a single research framework.
15. Future Research Roadmap
The proposed research roadmap consists of six stages.
Stage 1 - Conceptual Consolidation
Refine the UEARM component definitions, terminology, and lifecycle relationships.
Stage 2 - Technical Specification
Develop a formal specification covering:
JSON Schema.
Mandatory and optional fields.
Data types and semantic constraints.
Identifier and provenance requirements.
Authority binding.
Economic parameter validation.
State-transition rules.
Evidence references.
Versioning and compatibility.
Stage 3 - Experimental Implementation
Implement selected components within Smart Hand™ and construct reproducible test scenarios.
Compare the proposed model against relevant authorization, policy, agentic commerce, and transaction-processing approaches.
Stage 6 - Interoperability and Standardization Exploration
Investigate external technical review, interoperability profiles, and possible participation in appropriate standardization initiatives.
Progression between stages should depend on documented evidence and explicit research decisions.
16. Conclusion
The transition toward AI systems capable of initiating and coordinating economically consequential actions creates a need to examine the engineering relationship between intelligence, authority, execution, evidence, and outcomes.
This paper proposes the Unified Economic Action Representation Model (UEARM) as a structured approach to representing those relationships.
Through the proposed CE-AI™ Pipeline, the research investigates how an economic action may be represented and governed from its originating intent through authorization, execution, evidence capture, outcome verification, and governed learning.
The central proposition is that an economic action should be treated as a traceable operational object rather than merely an instruction or an output generated by an AI system.
UEARM is intended to provide a foundation for further technical specification, experimental evaluation, and interoperability research.
Its ultimate value will not be established by terminology or conceptual ambition, but by the consistency of its implementation, the quality of its evidence, its comparative performance, and its ability to withstand independent technical scrutiny.
A unified representation must earn its status through implementation, evidence, and validation.
17. Research Claim and Institutional Position
OUAMARKOM™ presents UEARM as a proposed research model within its broader investigation of governed AI economic agency.
The research does not claim that UEARM is an approved industry standard, a completed scientific theory, or a universally validated architecture.
The intended contribution is to investigate a unified way of representing economically consequential AI actions and to evaluate whether this representation can support more consistent governance, execution traceability, and outcome verification.
The research position is governed by four principles:
Evidence Before Architecture.
No Layer Without Evidence.
No Independent Layer Without Independent Value.
Every Layer Must Earn Its Existence.
The development of any future technical standard must be supported by formal specification, reproducible experiments, comparative research, independent assessment, and appropriate institutional participation.
The references above identify relevant technical approaches for further study. A formal publication should expand this bibliography through a systematic literature and prior-art review.
Companion Technical Specification
This paper is accompanied by UEARM Technical Specification v0.1 (OUAMARKOM-UEARM-SPEC-001), which translates the conceptual model proposed here into an initial technical contract.
The specification is a proposed, experimental document. It is not an approved industry standard, and its validation remains pending.
