MAAM — Multi-Agent Attribution Module
Architecture:
VEGA™ — Value Execution Governance Architecture
Parent Standard:
Value Attribution Standard (VAS)
Category:
Governance & Enforcement
Subcategory:
Value Execution Governance
Type: Core Module
Governed Space:
Multi-Agent Value Attribution Governance
Module ID: VEGA-VAS-MAAM-004
Origin ID: OOF.CM.VEGA.09.04.01
Version: 1.0
Status: Canonical · Open Standard
Effective Date: 1 October 2026
Compatibility: OOF® Methodology OS™ · Modular OOF® Architecture Ecosystem
AI-Readable: Yes
Authority: OOF® — OriginOpen® Foundation
Protection:
MIP® — Methodological Intellectual Property
Canonical Language:
English (UCL™)
1. Canonical Definition
The Multi-Agent Attribution Module (MAAM) governs how materially relevant contributions to validated Value are identified, differentiated, related, evidenced, and attributed when multiple autonomous, semi-autonomous, or coordinated agents participate in Value formation.
MAAM establishes a methodological basis for determining which agents materially contributed to Value, what functions they performed, how their contributions interacted, how coordination affected the outcome, and where Value emerged from collective rather than independently separable agent activity.
It distinguishes agent participation from agent contribution, agent activity from Value creation, delegation from contribution, coordination from execution, and multi-agent activity from multi-agent Value.
MAAM does not assume that all participating agents contributed equally.
It does not assume that additional agents create additional Value.
Its principal output is:
Governed Multi-Agent Attribution State
Its foundational distinction is:
Multi-Agent Activity ≠ Multi-Agent Value Contribution.
2. Operational Role
The operational role of MAAM is:
to establish how individual agents, agent interactions, delegation structures, coordination mechanisms, and collective agent configurations materially contributed to governed Value without confusing system complexity with Value creation.
HAAM establishes Human–AI attribution.
MAAM extends attribution into systems where multiple agents may act, coordinate, delegate, validate, correct, compete, or depend on one another.
3. Position in the VAS Internal Flow
The VAS internal governance flow is:
Value Source Identification → Contribution Attribution → Human–AI Attribution → Multi-Agent Attribution → Attribution Evidence
MAAM governs:
Multi-Agent Attribution
Its principal upstream input is:
Governed Human–AI Attribution State
together with the applicable:
Governed Contribution Attribution State
Its principal output is:
Governed Multi-Agent Attribution State
That state proceeds to:
Attribution Evidence Governance
4. Central Operational Question
Which agents materially contributed to the governed Value, how did their individual and collective contributions interact, and what part of the resulting Value can be attributed to individual agents, coordination mechanisms, or the multi-agent configuration itself?
5. Multi-Agent Attribution Object
Every MAAM assessment must identify the multi-agent process associated with the governed Value.
The object should preserve:
the Value object,
relevant agent population,
agent roles,
agent interactions,
delegation relationships,
coordination mechanisms,
relevant human interactions,
execution sequence,
and available attribution evidence.
6. Multi-Agent Boundary
MAAM should establish which agents belong within the relevant Value formation boundary.
The existence of an agent within the technical system does not automatically place that agent within the Value attribution boundary.
Only agents with plausible material relevance should enter attribution analysis.
7. Agent Identification
Each materially relevant agent should be sufficiently identifiable for attribution.
Identification may include:
agent identity,
agent type,
agent role,
model or mechanism where relevant,
version,
configuration,
authority context,
task assignment,
and execution period.
Identity does not establish contribution.
8. Agent Participation
Agent Participation means an agent was operationally involved in the relevant process.
Participation may include:
receiving a task,
producing output,
routing information,
calling a tool,
reviewing another agent,
or performing execution.
Participation alone does not establish Value contribution.
Agent Participation ≠ Agent Value Contribution.
9. Agent Contribution
Agent Contribution exists where an identifiable agent action, capability, decision, transformation, correction, validation, coordination act, or other intervention materially affects governed Value.
Contribution must be connected to Value through evidence.
10. Agent Materiality
Agent contribution should be evaluated relative to its material effect on the Value object.
Possible materiality states may include:
Critical
High
Material
Supporting
Limited
Incidental
Unresolved
Agent activity volume should not determine materiality by default.
11. Agent Role
An agent may perform one or more roles such as:
planning,
research,
retrieval,
analysis,
reasoning,
generation,
coordination,
routing,
execution,
validation,
critique,
correction,
monitoring,
or synthesis.
Agent role describes function.
It does not establish contribution magnitude.
12. Role Assignment vs Actual Contribution
An agent may be assigned a critical role but perform little material work.
Another agent may exceed its nominal role and materially affect the outcome.
MAAM should attribute actual contribution.
Assigned Role ≠ Actual Value Contribution.
13. Planning Agent Contribution
A Planning Agent materially contributes where its decomposition, sequencing, strategy, or task structure affects the resulting Value.
A plan that is ignored or replaced should not automatically receive material attribution.
14. Research Agent Contribution
A Research Agent may materially contribute by identifying:
evidence,
information,
sources,
relationships,
or relevant operational facts.
Research volume alone does not determine contribution.
15. Retrieval Agent Contribution
A Retrieval Agent may contribute where retrieved information materially affects the Value outcome.
Retrieval that produces unused or irrelevant information should not automatically receive material attribution.
16. Reasoning Agent Contribution
A Reasoning Agent may materially contribute through:
inference,
comparison,
decomposition,
synthesis,
evaluation,
or conclusion formation.
Reasoning length or compute consumption does not establish Value contribution.
17. Generation Agent Contribution
A Generation Agent may materially contribute by producing:
text,
code,
design,
plans,
actions,
or other outputs
that materially affect Value.
Output volume should remain separate from Value contribution.
18. Execution Agent Contribution
An Execution Agent may materially contribute by converting decisions or plans into operational action.
Execution contribution should remain distinguishable from planning or decision contribution.
19. Validation Agent Contribution
A Validation Agent may materially contribute by determining whether another agent's output satisfies required conditions.
Validation contribution should remain distinguishable from generation contribution.
20. Critique Agent Contribution
A Critique Agent may materially contribute by identifying:
errors,
weaknesses,
unsupported assumptions,
constraint violations,
or alternative interpretations.
Critique becomes attributable where it materially changes or protects Value.
21. Correction Agent Contribution
A Correction Agent may materially contribute by repairing or replacing deficient output.
A small correction may have high Value significance.
Correction Size ≠ Correction Materiality.
22. Coordination Agent Contribution
A Coordination Agent may materially contribute through:
task allocation,
sequencing,
routing,
synchronization,
conflict management,
aggregation,
or workflow control.
Coordination contribution should remain distinct from the contributions being coordinated.
23. Orchestration Contribution
Value may emerge partly from the architecture that organizes agent activity.
MAAM may recognize:
Orchestration Contribution
where orchestration materially improves how agent capabilities combine to produce Value.
This does not automatically attribute all downstream Value to the orchestrator.
24. Agent Delegation
Agents may delegate tasks to other agents.
MAAM should preserve:
Delegating Agent → Delegated Task → Receiving Agent → Result → Value Relationship
Delegation itself does not transfer the receiving agent's contribution to the delegating agent.
25. Delegation Contribution
Delegation may nevertheless constitute a distinct contribution where selecting:
the correct subtask,
the correct agent,
the correct timing,
or the correct execution pathway
materially affects Value.
Delegation ≠ Execution Contribution.
26. Delegation Chain
Complex systems may contain multiple delegation levels.
MAAM may represent:
Agent A → Agent B → Agent C → Tool / Action → Outcome
A Delegation Chain should preserve the distinction between:
task origination,
delegation,
execution,
and resulting Value contribution.
27. Agent Handoff
An Agent Handoff occurs when one agent transfers:
task state,
context,
evidence,
output,
or execution responsibility
to another agent.
Handoff quality may materially affect Value.
28. Handoff Contribution
A handoff may become a material contribution where successful transfer of context or state prevents:
information loss,
execution failure,
duplication,
misinterpretation,
or unnecessary rework.
29. Agent Dependency
An agent contribution may depend on another agent's output or capability.
MAAM should preserve material dependency relationships.
A downstream agent should not automatically receive full attribution for Value created partly through upstream contribution.
30. Agent Contribution Chain
MAAM may represent:
Agent → Contribution → Agent → Contribution → Outcome → Value
This creates an:
Agent Contribution Chain
The chain should preserve which contribution occurred at each stage.
31. Agent Contribution Network
Where multiple agents interact non-linearly, MAAM may represent an:
Agent Contribution Network
This may include:
parallel activity,
feedback,
delegation,
correction,
coordination,
validation,
shared dependencies,
and iterative execution.
32. Parallel Agent Contribution
Multiple agents may work on the same or related tasks simultaneously.
Parallel execution does not imply independent contribution.
MAAM should determine whether outputs were:
independent,
overlapping,
complementary,
redundant,
or conflicting.
33. Sequential Agent Contribution
Agents may contribute sequentially.
The output of one agent may become the input of another.
Sequence should be preserved where it materially affects the resulting Value.
34. Iterative Agent Contribution
Agents may repeatedly exchange outputs and refinements.
Where Value emerges through repeated cycles, MAAM should preserve:
Iterative Multi-Agent Contribution
rather than assigning Value solely to the final agent.
35. Complementary Agent Contribution
Agents may provide different capabilities that combine to create Value.
For example:
one retrieves,
one reasons,
one validates,
and one executes.
Where the contributions are materially complementary, the structure should remain visible.
36. Redundant Agent Activity
Multiple agents may perform substantially equivalent work.
Redundancy may support:
resilience,
verification,
or comparison.
But redundant activity does not automatically create additional Value.
More Agent Activity ≠ More Value.
37. Duplicate Agent Contribution
Where two agents independently produce the same material effect, MAAM should prevent that effect from being counted twice without methodological justification.
38. Agent Contribution Overlap
Agent contributions may partially overlap.
MAAM should identify where the same Value effect is associated with multiple agents.
Contribution Overlap ≠ Independent Value Creation.
39. Agent Conflict
Agents may produce conflicting:
recommendations,
analyses,
plans,
predictions,
or actions.
Conflict itself may affect Value positively or negatively.
MAAM should preserve how the conflict was resolved and which contribution affected the final outcome.
40. Conflict Resolution Contribution
An agent or mechanism may materially contribute by resolving disagreement among agents.
This may involve:
comparison,
evidence weighting,
arbitration,
validation,
or escalation.
Conflict Resolution Contribution should remain distinct from the original conflicting contributions.
41. Agent Correction Relationship
Where one agent corrects another, MAAM should preserve:
Deficient Agent Contribution → Corrective Agent Contribution → Changed Outcome
The final successful result should not erase the deficient contribution.
42. Agent Validation Relationship
Where one agent validates another, MAAM should distinguish:
generation contribution
from
validation contribution.
Validation may materially create confidence or permit Value recognition without becoming the original source of the output.
43. Agent Amplification
One agent may amplify another agent's contribution by increasing:
coverage,
quality,
speed,
scale,
precision,
or operational usefulness.
Amplification should be attributed where it materially affects Value.
44. Agent Constraint Contribution
An agent may contribute by constraining another agent's action.
Constraints may prevent:
invalid execution,
unnecessary resource consumption,
unsupported conclusions,
or boundary violations.
Preventive contribution may therefore be material even when no visible output is produced.
45. Agent Escalation Contribution
An agent may materially contribute by recognizing that a task exceeds its:
capability,
authority,
confidence,
or available evidence
and escalating it appropriately.
Successful non-execution can therefore constitute contribution where it protects the Value outcome.
46. Agent Non-Action
An agent may choose not to act.
Non-action should receive attribution only where:
the agent had a meaningful alternative,
the non-action was governed,
and the decision materially preserved or improved Value.
No Action ≠ No Contribution by default.
47. Agent Failure Contribution
An agent may negatively affect Value through:
incorrect output,
failed execution,
misrouting,
duplication,
unnecessary escalation,
resource waste,
or invalid action.
MAAM should preserve material negative contribution.
48. Agent Recovery Contribution
Another agent may restore Value after failure.
Recovery contribution should remain separately attributable from the failure that made recovery necessary.
49. Individual Agent Contribution
Where evidence supports meaningful separation, MAAM may attribute Value contribution to an individual agent.
Individual attribution should preserve:
agent identity,
role,
action,
effect,
evidence,
and Value relationship.
50. Collective Agent Contribution
Some Value emerges from the combined operation of several agents and cannot be meaningfully reduced to isolated individual contributions.
MAAM may represent:
Collective Agent Contribution
where the collective configuration itself materially creates Value.
51. Emergent Multi-Agent Contribution
A multi-agent system may produce Value that cannot be traced to any single agent action alone.
Where Value emerges from interaction among agents, MAAM may identify:
Emergent Multi-Agent Contribution
Emergence should be supported by evidence of interaction effects rather than assumed from system complexity.
52. Joint Agent Contribution
Where two or more agents materially produce one inseparable Value effect, MAAM may preserve:
Joint Agent Contribution
rather than force artificial decomposition.
Joint Agent Contribution ≠ Equal Agent Contribution.
53. Agent Contribution Decomposition
Where evidence supports separation, collective or joint contribution may be decomposed into:
individual agent contributions,
coordination contribution,
interaction contribution,
validation contribution,
and other material components.
Decomposition should stop where evidence stops.
54. Agent Contribution Necessity
MAAM may assess whether an agent contribution was necessary to the observed Value outcome.
Possible states include:
Necessary
Material but Non-Necessary
Supporting
Substitutable
Redundant
Incidental
Unresolved
Necessity does not establish attribution weight.
55. Agent Contribution Sufficiency
An agent may be necessary without being sufficient.
An agent may also provide a material contribution that requires other agents before Value can emerge.
Agent Necessity ≠ Agent Sufficiency.
56. Agent Substitutability
MAAM may assess whether another agent or mechanism could plausibly replace a material contribution.
A substitutable agent may still have materially contributed to the observed Value.
Agent Substitutability ≠ No Contribution.
57. Agent Specialization
An agent may provide a specialized capability unavailable elsewhere in the configuration.
Specialization may affect:
necessity,
substitutability,
or contribution significance.
It does not automatically establish greater attribution weight.
58. Agent Contribution Increment
Where evidence permits, MAAM may evaluate the incremental Value effect associated with an agent or agent configuration.
The relevant question is:
What materially changed because this agent or configuration participated?
59. Agent Removal Analysis
Where methodologically appropriate, an agent may be removed or excluded from a comparative execution to observe changes in:
quality,
completion,
cost,
time,
validation,
or Value.
Agent Removal Analysis can strengthen attribution evidence.
It should not be used where removal would violate applicable governed constraints.
60. Agent Replacement Analysis
An agent may be replaced with:
another agent,
another model,
a deterministic mechanism,
or a human
to test whether the relevant contribution persists.
Replacement analysis can support assessment of:
substitutability,
uniqueness,
and incremental contribution.
61. Configuration Contribution
The configuration of agents may itself materially affect Value.
Relevant configuration factors may include:
agent selection,
agent count,
role assignment,
routing,
communication,
delegation,
validation,
and termination logic.
MAAM may therefore recognize:
Multi-Agent Configuration Contribution
where supported by evidence.
62. Agent Count
The number of agents is not a Value measure.
Additional agents may:
increase capability,
improve validation,
increase resilience,
create duplication,
increase cost,
increase latency,
or reduce Value.
More Agents ≠ More Value Contribution.
63. Coordination Overhead
Multi-agent systems may consume resources through:
communication,
routing,
state synchronization,
duplication,
conflict resolution,
and management.
Coordination overhead should remain distinguishable from coordination contribution.
Coordination Activity ≠ Coordination Value.
64. Coordination Value
Coordination creates Value where it materially improves the ability of agents to combine their capabilities into a better governed outcome.
Examples may include:
correct routing,
effective sequencing,
conflict resolution,
specialization,
or information integration.
65. Coordination Net Contribution
Where useful, MAAM may compare:
Coordination Value
against
Coordination Overhead
to understand whether coordination materially improves the Value-producing process.
This does not replace IES or COS economic and computational governance.
66. Agent Communication Contribution
Communication among agents may materially contribute where information exchange improves the resulting Value.
Communication volume alone does not establish contribution.
More Agent Communication ≠ More Value.
67. Shared Context Contribution
A shared memory, context, workspace, or state mechanism may materially enable multi-agent coordination.
Where applicable, its enabling contribution may be identified separately from the contributions of individual agents.
Memory governance itself remains outside MAAM.
68. Tool-Mediated Agent Contribution
Agents may produce Value through tools.
MAAM should distinguish:
agent reasoning or decision contribution
from
tool execution contribution
where the distinction is material.
The agent should not automatically absorb the tool's entire contribution.
69. Human–Agent Interaction
Human contribution may remain present within multi-agent systems.
Examples include:
task definition,
agent selection,
intervention,
correction,
validation,
override,
or escalation handling.
MAAM should preserve the Governed Human–AI Attribution State supplied by HAAM rather than reassign human contribution merely because agents are present.
70. Human Intervention in Agent Networks
A human may intervene at one point in a long agent chain.
Where that intervention materially changes the outcome, it should remain visible in the multi-agent attribution structure.
The number of agent steps surrounding the intervention does not reduce its materiality.
71. Agent Attribution Weight
Where evidence and methodology support it, MAAM may represent relative agent contribution weight.
Representation may be:
quantitative,
categorical,
ordinal,
bounded,
or comparative.
No universal percentage allocation is required.
72. Multi-Agent Attribution Precision
The precision of Multi-Agent Attribution must correspond to the available evidence.
Multi-Agent Attribution Precision must not exceed Evidence Precision.
Where individual contributions cannot be reliably separated, collective or joint attribution is preferable to fabricated percentages.
73. Agent Attribution Confidence
Possible states may include:
High Confidence
Moderate Confidence
Conditional
Evidence-Limited
Low Confidence
Unresolved
Confidence should reflect the quality of evidence connecting agent activity to Value.
74. Multi-Agent Attribution Uncertainty
Uncertainty may arise from:
large agent populations,
complex interactions,
unobservable internal processes,
shared context,
overlapping outputs,
dynamic delegation,
or incomplete execution traces.
Uncertainty should remain explicit.
75. Agent Attribution Conflict
Different evidence may support conflicting interpretations of agent contribution.
Possible states include:
Consistent
Partially Conflicting
Materially Conflicting
Evidence-Incomplete
Unresolved
Conflict should not be hidden by arbitrary allocation.
76. Multi-Agent Evidence
Relevant evidence may include:
agent identities,
task assignments,
agent outputs,
delegation records,
messages,
tool calls,
routing events,
execution traces,
validation events,
corrections,
failures,
human interventions,
and resulting Value states.
77. Agent Traceability
Where practicable, MAAM should preserve:
Agent → Task → Action → Interaction → Effect → Outcome → Value
This allows contribution to be traced through complex agent systems.
78. Delegation Traceability
Delegation should preserve:
Delegating Agent → Delegation Decision → Receiving Agent → Execution → Result → Value Effect
This prevents delegation authority from being confused with execution contribution.
79. Coordination Traceability
Where coordination materially contributes to Value, MAAM should preserve:
Coordination Mechanism → Coordination Action → Agent-State Change → Operational Effect → Value Effect
80. Multi-Agent Provenance
Material attribution should preserve sufficient provenance to identify:
agents,
versions,
models where relevant,
roles,
configurations,
task assignments,
interactions,
delegations,
tools,
execution time,
and supporting evidence.
81. Agent Attribution Integrity
Agent attribution must not be manipulated because:
one agent owns the final output,
one agent appears last,
one agent consumed the most compute,
one agent produced the most tokens,
one agent delegated the task,
or one agent has the highest system authority.
Final Agent ≠ Primary Value Contributor by default.
82. Final-Agent Bias
The final agent in a workflow may receive disproportionate attribution because its output is most visible.
MAAM should trace upstream material contributions before determining attribution.
83. Coordinator Bias
A coordinating agent may appear responsible for the entire system outcome.
Coordination should receive attribution only for the Value effect created through coordination.
Coordination Authority ≠ Total Value Contribution.
84. Compute Bias
An agent consuming the most computational resources may not create the most Value.
Highest Compute Consumption ≠ Highest Value Contribution.
85. Output Bias
An agent producing the largest output may not create the most Value.
Largest Output ≠ Largest Value Contribution.
86. Agent Attribution Drift
Contribution structures may change as:
agents are added,
agents are removed,
roles change,
models change,
delegation changes,
routing changes,
or autonomy increases.
MAAM should detect material attribution drift.
87. Multi-Agent Reconfiguration
A reconfigured agent system may produce the same Value through a materially different contribution structure.
Previous attribution should not automatically carry forward.
88. Agent Attribution Revision
Attribution should be revised when:
new evidence appears,
agent roles change,
the configuration changes,
a material contribution was omitted,
an attribution error is discovered,
or the Value object changes.
Revision should preserve previous states.
89. Agent Attribution Versioning
Material attribution changes should preserve:
previous state,
current state,
agent configuration,
agent versions,
methodology version,
reason for change,
supporting evidence,
and effective time.
90. Governed Multi-Agent Attribution State
The principal output of MAAM is:
Governed Multi-Agent Attribution State
Possible states may include:
Individually Attributed
Jointly Attributed
Collectively Attributed
Coordination-Attributed
Delegation-Attributed
Validation-Attributed
Corrective Contribution
Complementary Contribution
Emergent Multi-Agent Contribution
Redundant Contribution
Negative Contribution
Conditionally Attributed
Evidence-Limited
Conflicted
Unattributed
Unresolved
These states describe methodological Value contribution.
They do not determine legal or economic entitlement.
91. Minimum Implementation Framework
A conforming implementation of MAAM must provide exactly the following six capabilities:
1. Multi-Agent Object, Identity & Boundary Governance
Capability to identify the governed multi-agent process, relevant agents, roles, configurations, attribution boundary, and applicable Value object.
2. Agent Contribution & Role Governance
Capability to assess material contribution from planning, research, retrieval, reasoning, generation, execution, validation, critique, correction, coordination, and other agent roles based on actual Value effect.
3. Delegation, Interaction & Network Governance
Capability to represent delegation, handoffs, dependencies, sequences, parallel activity, iterative activity, Agent Contribution Chains, Networks, conflicts, correction, validation, amplification, constraints, and escalation.
4. Individual, Joint & Collective Attribution Governance
Capability to distinguish individual, joint, collective, emergent, orchestration, coordination, redundant, overlapping, necessary, sufficient, substitutable, and configuration-level contributions without artificial allocation.
5. Multi-Agent Evidence & Integrity Governance
Capability to preserve evidence, traceability, delegation traceability, coordination traceability, provenance, confidence, uncertainty, conflict, bias controls, drift, reconfiguration, revision, versioning, and attribution integrity.
6. VAS Attribution Evidence Handoff Governance
Capability to produce a Governed Multi-Agent Attribution State sufficiently explicit for subsequent Attribution Evidence Governance.
92. Governance Logic
The canonical MAAM governance logic is:
Governed Human–AI Attribution State
↓
Identify Multi-Agent Attribution Object
↓
Establish Agent Boundary
↓
Identify Relevant Agents / Roles / Configuration
↓
Identify Agent Participation
↓
Qualify Material Agent Contribution
↓
Classify Agent Contribution Functions
↓
Map Delegation / Handoffs
↓
Map Agent Dependencies
↓
Map Sequential / Parallel / Iterative Contributions
↓
Construct Agent Contribution Chains / Networks
↓
Identify Coordination / Orchestration Contribution
↓
Identify Validation / Critique / Correction Contribution
↓
Identify Agent Conflict / Conflict Resolution
↓
Identify Overlap / Redundancy
↓
Control Double-Counting
↓
Assess Necessity / Sufficiency / Substitutability
↓
Assess Individual / Joint / Collective Contribution
↓
Assess Emergent Multi-Agent Contribution
↓
Assess Configuration Contribution
↓
Assess Agent Removal / Replacement Evidence where justified
↓
Determine Attribution Weight where defensible
↓
Control Attribution Precision
↓
Assess Evidence / Traceability / Provenance
↓
Assess Confidence / Uncertainty / Conflict
↓
Test Attribution Bias / Integrity
↓
Assess Attribution Drift / Reconfiguration
↓
Establish Governed Multi-Agent Attribution State
↓
Handoff to Attribution Evidence Governance
93. Governance Outputs
MAAM may produce:
Multi-Agent Attribution Object
Agent Attribution Boundary
Agent Identity Record
Agent Role Record
Agent Participation Record
Agent Contribution Record
Agent Materiality State
Planning Agent Contribution State
Research Agent Contribution State
Retrieval Agent Contribution State
Reasoning Agent Contribution State
Generation Agent Contribution State
Execution Agent Contribution State
Validation Agent Contribution State
Critique Agent Contribution State
Correction Agent Contribution State
Coordination Agent Contribution State
Orchestration Contribution State
Delegation Record
Delegation Contribution State
Delegation Chain
Agent Handoff Record
Agent Dependency Record
Agent Contribution Chain
Agent Contribution Network
Parallel Contribution State
Sequential Contribution State
Iterative Contribution State
Complementary Contribution State
Redundancy State
Agent Overlap State
Agent Conflict State
Conflict Resolution Contribution State
Agent Correction Relationship
Agent Validation Relationship
Agent Amplification State
Agent Constraint Contribution State
Agent Escalation Contribution State
Agent Failure Contribution State
Agent Recovery Contribution State
Individual Agent Contribution State
Collective Agent Contribution State
Emergent Multi-Agent Contribution State
Joint Agent Contribution State
Agent Necessity State
Agent Sufficiency State
Agent Substitutability State
Agent Contribution Increment
Agent Removal Analysis
Agent Replacement Analysis
Multi-Agent Configuration Contribution State
Coordination Overhead Record
Coordination Value State
Agent Communication Contribution State
Agent Attribution Weight
Multi-Agent Attribution Confidence State
Multi-Agent Attribution Uncertainty State
Multi-Agent Attribution Conflict State
Agent Traceability Record
Delegation Traceability Record
Coordination Traceability Record
Multi-Agent Provenance Record
Multi-Agent Attribution Drift State
Multi-Agent Attribution Version
The principal output is:
Governed Multi-Agent Attribution State
94. Critical Distinctions
MAAM preserves the following distinctions:
Multi-Agent Activity ≠ Multi-Agent Value Contribution
Agent Participation ≠ Agent Value Contribution
Assigned Role ≠ Actual Value Contribution
Agent Activity Volume ≠ Agent Contribution Materiality
Output Volume ≠ Agent Value Contribution
Delegation ≠ Execution Contribution
Delegation Authority ≠ Value Contribution
Coordination Activity ≠ Coordination Value
Coordination Authority ≠ Total Value Contribution
More Agents ≠ More Value Contribution
More Agent Activity ≠ More Value
More Agent Communication ≠ More Value
Redundancy ≠ Additional Value by default
Contribution Overlap ≠ Independent Value Creation
Joint Agent Contribution ≠ Equal Agent Contribution
Collective Contribution ≠ Equal Individual Contribution
Agent Necessity ≠ Agent Sufficiency
Agent Necessity ≠ Attribution Weight
Agent Substitutability ≠ No Contribution
Emergent Contribution ≠ Unattributable Contribution
Correction Size ≠ Correction Materiality
Final Agent ≠ Primary Value Contributor by default
Highest Compute Consumption ≠ Highest Value Contribution
Largest Output ≠ Largest Value Contribution
Multi-Agent Attribution Precision must not exceed Evidence Precision
Multi-Agent Attribution ≠ Legal Ownership
Multi-Agent Attribution ≠ Liability Attribution
Agent Contribution ≠ Agent Authority
95. Cross-Architecture Boundary
MAAM governs methodological Value Attribution within multi-agent systems.
VSIM identifies Value sources.
CATM establishes Contribution Attribution.
HAAM establishes Human–AI Attribution.
MAAM establishes Multi-Agent Attribution.
AEM subsequently governs Attribution Evidence.
MAAM does not govern whether agents possess legitimate operational authority.
Agent authority, permission, delegation authority, autonomous-system governability, and related control questions belong to the competent agent or autonomous-system governance architecture.
MAAM may use those governed states as evidence where relevant.
VGS governs Value.
XGS governs execution.
IPS governs Productivity.
RGS governs resources.
COS governs Computational Optimization.
IES governs Intelligence Execution Economics.
SVGS governs Strategic Value.
ISS governs Intelligence Sustainability.
DGS governs lifecycle decisions.
HAGA™ governs Harm.
LIGA™ governs Liability.
ADIT® may govern broader evidence and auditability.
VALIDOS™ may govern validation methodology.
MAAM governs only the methodological attribution of multi-agent contribution to Value.
96. Canonical Boundary
MAAM begins when sufficiently explicit contribution states exist for multi-agent Value formation.
MAAM governs:
Multi-Agent Attribution Objects,
agent boundaries,
agent identities for attribution purposes,
agent roles,
agent participation,
agent contribution,
agent materiality,
planning contribution,
research contribution,
retrieval contribution,
reasoning contribution,
generation contribution,
execution contribution,
validation contribution,
critique contribution,
correction contribution,
coordination contribution,
orchestration contribution,
delegation,
Delegation Contribution,
Delegation Chains,
handoffs,
dependencies,
Agent Contribution Chains,
Agent Contribution Networks,
parallel contribution,
sequential contribution,
iterative contribution,
complementary contribution,
redundancy,
duplicate contribution,
overlap,
agent conflict,
Conflict Resolution Contribution,
correction relationships,
validation relationships,
amplification,
constraint contribution,
escalation contribution,
non-action contribution,
failure contribution,
recovery contribution,
individual contribution,
collective contribution,
Emergent Multi-Agent Contribution,
Joint Agent Contribution,
decomposition,
necessity,
sufficiency,
substitutability,
specialization,
Contribution Increment,
Agent Removal Analysis,
Agent Replacement Analysis,
Multi-Agent Configuration Contribution,
agent count relevance,
coordination overhead,
coordination Value,
communication contribution,
shared-context contribution relevance,
tool-mediated agent contribution,
Human–Agent interaction,
Human Intervention,
Attribution Weight,
precision,
confidence,
uncertainty,
conflict,
evidence,
traceability,
provenance,
integrity,
bias,
drift,
reconfiguration,
revision,
and versioning.
MAAM does not independently determine:
agent operational authority,
agent permissions,
legal agency,
legal ownership,
compensation,
tax treatment,
Accountability,
Responsibility,
Harm,
Liability,
or final Decision Authority.
MAAM ends when material multi-agent contribution has been represented as a:
Governed Multi-Agent Attribution State
for subsequent Attribution Evidence Governance.
97. Use Case 1 — Twelve Agents Participate but Four Create Most of the Material Contribution
An organization operates a twelve-agent research and decision-support system.
All twelve agents generate activity.
Execution analysis shows that:
one agent retrieves the decisive evidence,
one reasoning agent identifies the relevant relationship,
one critique agent discovers a material flaw,
and one coordination agent routes the corrected result through the required validation path.
Several other agents produce outputs that are duplicated, unused, or immaterial to the final Value.
MAAM does not divide Value across twelve agents merely because twelve agents participated.
It identifies:
material individual contribution,
supporting contribution,
redundancy,
coordination contribution,
and incidental activity.
The resulting attribution reflects the actual Value formation structure.
Agent Participation ≠ Agent Value Contribution.
98. Use Case 2 — No Individual Agent Created the Result
A complex engineering system uses specialized agents for:
design,
simulation,
constraint analysis,
optimization,
critique,
and validation.
No single agent produces the final solution.
Instead, the design agent proposes a configuration.
The simulation agent identifies weaknesses.
The constraint agent rejects an invalid parameter.
The optimization agent modifies the configuration.
The critique agent identifies another failure.
The cycle repeats until the validation agent confirms the required outcome.
The resulting Value cannot be accurately attributed to the final validation agent or any single preceding agent.
MAAM represents the system as:
Emergent Multi-Agent Contribution
with identifiable individual contributions embedded within a broader collective Value formation process.
Collective Value does not require a fictional single creator.
99. Canonical Closing Statement
A multi-agent system can generate enormous activity without generating proportionate Value.
Agents can communicate without contributing.
They can duplicate one another.
They can create errors that other agents must repair.
A coordinating agent can improve the entire system without producing the final output.
And sometimes Value exists only because several specialized contributions interact in a way that no individual agent could reproduce alone.
Multi-Agent Attribution must therefore follow Value formation rather than agent count.
Multi-Agent Activity ≠ Multi-Agent Value Contribution.
Agent Participation ≠ Agent Value Contribution.
Assigned Role ≠ Actual Value Contribution.
More Agents ≠ More Value Contribution.
Coordination Activity ≠ Coordination Value.
Joint Agent Contribution ≠ Equal Agent Contribution.
Final Agent ≠ Primary Value Contributor by default.
Multi-Agent Attribution Precision must not exceed Evidence Precision.
The purpose of MAAM is not to distribute Value across every active agent.
It is to reveal which agents, interactions, coordination mechanisms, and collective configurations materially made that Value possible.
MAAM governs the difference between a system in which many agents act and a system in which their actions actually become Value.