RCTM — Consumption Traceability Module
Architecture:
VEGA™ — Value Execution Governance Architecture
Parent Standard:
Resource Governance Standard (RGS)
Category:
Governance & Enforcement
Subcategory:
Value Execution Governance
Type: Core Module
Governed Space:
Execution Resource Consumption Traceability
Module ID: VEGA-RGS-RCTM-005
Origin ID: OOF.CM.VEGA.04.05.01
Version: 1.0
Status: Canonical · Open Standard
Effective Date: 28 September 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 Consumption Traceability Module (RCTM) governs how resources actually consumed by intelligence execution are identified, measured, bounded, associated with the execution that caused or required them, and preserved as traceable resource-consumption evidence.
RCTM establishes the methodological transition from governed resource availability, allocation, reallocation, and constraints to evidence of what the intelligence execution actually consumed.
It distinguishes resource availability from reservation, reservation from allocation, allocation from utilization, and utilization from attributable consumption.
RCTM governs direct, indirect, shared, embedded, persistent, and supporting resource consumption where materially relevant to the execution.
It prevents the visible consumption of the primary model or application from being treated automatically as the complete resource footprint of intelligence execution.
Its principal output is:
Governed Resource Consumption Trace
Its foundational distinction is:
Allocated Resource ≠ Consumed Resource.
2. Operational Role
The operational role of RCTM is:
to establish an evidence-supported trace between intelligence execution and the resources actually consumed in producing that execution.
RGS cannot govern resource consumption merely by knowing what resources were available or allocated.
RCTM establishes what was actually used, by what, when, for what execution purpose, and with what degree of measurement confidence.
3. Position in the RGS Internal Flow
The RGS internal module progression is:
Resource Identification → Resource Allocation → Dynamic Reallocation → Resource Constraint → Consumption Traceability
RCTM governs:
Consumption Traceability
Its principal input is:
Governed Resource Constraint State
Its principal output is:
Governed Resource Consumption Trace
RCTM completes the RGS internal governance flow.
4. Central Operational Question
What resources did the intelligence execution actually consume, where did that consumption occur, what execution activity caused or required it, and what evidence supports the resulting consumption trace?
5. Consumption Object
Every consumption trace should identify the execution object whose resource use is being measured.
The Consumption Object may be:
a task,
model execution,
agent,
multi-agent workflow,
automated process,
robotic operation,
human-AI workflow,
service,
or another governed execution object.
6. Resource Consumption Object
Every material consumption record should identify the Resource Object consumed.
The resource should correspond, where applicable, to the governed resource inventory established by RIM.
7. Resource Consumption
Resource Consumption represents actual use of a Resource Object by or for the governed intelligence execution.
Consumption is not established merely because the resource was:
available,
reserved,
or allocated.
8. Consumption Basis
Every material consumption claim should possess a defensible Basis.
The Basis may derive from:
direct measurement,
telemetry,
service records,
metering,
runtime logs,
resource accounting,
estimation,
or another appropriate evidence source.
9. Consumption Boundary
The Consumption Boundary determines which resource use belongs within the governed execution trace.
A weak boundary may:
omit material consumption,
include unrelated consumption,
or distort comparisons between executions.
10. Execution Boundary Relationship
The resource-consumption boundary may extend beyond the visible application boundary.
An execution may trigger supporting activity across:
models,
tools,
retrieval,
storage,
networks,
validation,
orchestration,
and infrastructure.
Therefore:
Application Consumption ≠ Total Execution Consumption.
11. Direct Consumption
Direct Consumption occurs where the Resource Object is used directly by the primary governed execution.
Examples may include:
model compute,
active memory,
or direct tool use.
12. Indirect Consumption
Indirect Consumption occurs through a supporting mechanism required by the governed execution.
Examples may include:
routing,
retrieval,
validation,
logging,
coordination,
or supporting infrastructure.
Material indirect consumption should remain visible.
13. Embedded Consumption
Some resource use is embedded inside:
platforms,
APIs,
cloud services,
models,
tools,
or infrastructure.
Embedded consumption should be represented where material and sufficiently observable.
14. Hidden Consumption
Hidden Consumption is resource use materially caused by execution but omitted from the apparent resource footprint.
Therefore:
Visible Consumption ≠ Total Material Consumption.
15. Shared Consumption
A Resource Object may be consumed by multiple execution objects.
RCTM should avoid attributing the entire shared resource to one execution without a defensible Basis.
16. Dedicated Consumption
Dedicated resources may support only one execution object or workload.
Dedicated status may simplify attribution but does not eliminate the need to distinguish:
allocated capacity,
idle capacity,
and actual consumption.
17. Persistent Resource Consumption
Persistent services or infrastructure may consume resources beyond individual execution events.
Where materially connected to the execution system, persistent consumption may require appropriate attribution.
18. Transient Consumption
Transient Consumption occurs only during a bounded execution event or resource interaction.
The consumption trace should preserve relevant temporal boundaries.
19. Supporting Consumption
Supporting Consumption enables the primary execution without itself being the principal productive operation.
Examples may include:
monitoring,
validation,
orchestration,
state maintenance,
or coordination.
Supporting does not mean irrelevant.
20. Resource Consumption Unit
Consumption should be expressed using an appropriate unit where meaningful.
Units may include:
compute time,
processor cycles,
accelerator time,
memory usage,
storage,
data transfer,
energy,
service calls,
tool executions,
human time,
or another operational measure.
21. Native Resource Unit
Where possible, a Resource Object may be measured using its native operational unit.
RCTM does not require unlike resources to be collapsed into one artificial universal measure.
22. Composite Consumption Measure
Multiple related resource measurements may be combined where a composite measure has a legitimate methodological Basis.
The underlying resource evidence should remain recoverable where material.
23. Consumption Measurement
Measurement should determine actual resource use within the relevant boundary.
RCTM should distinguish measurement from:
allocation,
capacity,
reservation,
and theoretical requirement.
24. Direct Measurement
Direct Measurement observes actual resource consumption using instrumentation capable of measuring the relevant Resource Object.
Where available and reliable, direct measurement may provide the strongest consumption evidence.
25. Estimated Consumption
Where direct measurement is unavailable, consumption may be estimated.
The estimation method and uncertainty should remain explicit.
Therefore:
Estimated Consumption ≠ Directly Measured Consumption.
26. Proxy Measurement
A Proxy may represent resource consumption indirectly where direct measurement is impractical.
The relationship between the proxy and the underlying resource should be sufficiently justified.
27. Consumption Observability
Resource consumption may be:
Directly Observable,
Partially Observable,
Proxy Observable,
Estimated,
or Unobservable under current instrumentation.
The observability state should remain part of the trace.
28. Measurement Resolution
Consumption evidence may vary in resolution.
Measurement may occur per:
execution,
task,
agent,
workflow,
time interval,
resource pool,
or another boundary.
The resolution should be sufficient for the intended governance purpose.
29. Temporal Resolution
The frequency at which consumption is measured may affect what resource behavior can be detected.
Short resource spikes may disappear inside coarse aggregate measurements.
30. Consumption Timestamp
Where material, resource-consumption evidence should preserve when the consumption occurred.
Time alignment can support reconstruction of:
resource transitions,
execution stages,
and consumption spikes.
31. Consumption Duration
Some resource use should be represented as consumption over time.
Duration may materially affect:
compute,
hardware,
memory,
infrastructure,
or human-support consumption.
32. Consumption Rate
A Consumption Rate expresses resource use relative to time or another defined operational denominator.
Rates should remain distinguishable from total consumption.
33. Peak Consumption
Peak Consumption identifies the highest observed resource demand within the governed measurement period.
Peak demand should not automatically be treated as typical consumption.
34. Average Consumption
Average Consumption may summarize resource use across a defined period.
Averages should not conceal material peaks, variability, or idle periods where those are governance-relevant.
35. Sustained Consumption
Sustained Consumption represents resource use maintained across a meaningful operating period.
This may be more informative than short-term peaks for some resource decisions.
36. Idle Consumption
Resources may consume capacity or energy while performing no active productive execution.
Idle Consumption should remain visible where material.
Therefore:
No Productive Activity ≠ No Resource Consumption.
37. Standby Consumption
Standby resources may remain ready for execution while consuming a lower but non-zero resource level.
Standby consumption should remain distinguishable from active execution consumption.
38. Reserved but Unconsumed Resource
Reserved capacity may remain unused.
RCTM should not classify the entire reservation as actual consumption merely because it was unavailable to other executions.
39. Allocated but Unconsumed Resource
A resource may be allocated but never used.
Therefore:
Allocation Quantity ≠ Consumption Quantity.
40. Utilized Resource
Utilization describes the degree to which available or allocated capacity is used.
Utilization may inform consumption analysis but should not automatically be equated with attributable consumption.
41. Utilization Ratio
Where appropriate, RCTM may compare:
actual resource use
against
allocated resource capacity.
This can expose substantial differences between provisioning and actual execution behavior.
42. Consumption Attribution
Consumption Attribution connects measured or estimated resource use to the execution object that caused or required it.
This is resource-consumption attribution.
It is not the Value Attribution governed by VAS.
43. Direct Attribution
Direct Attribution exists where resource use can be connected directly to a specific execution object.
44. Shared Attribution
Where multiple execution objects share a resource, RCTM may require an attribution method to distribute consumption.
The method should remain explicit.
45. Allocation-Based Attribution
Shared consumption may sometimes be attributed according to allocated capacity.
This method should be used only where allocation provides a defensible proxy for actual consumption.
46. Usage-Based Attribution
Consumption may be attributed according to directly observed resource use by each execution object.
Where reliable measurement exists, this may provide stronger attribution than allocation-based methods.
47. Time-Based Attribution
Shared resources may sometimes be attributed according to execution time.
Time should not be used as a proxy where resource intensity differs materially without appropriate adjustment.
48. Activity-Based Attribution
Consumption may be associated with specific:
tasks,
calls,
operations,
agents,
tools,
or execution events.
Activity-based attribution should preserve the relevant execution identity.
49. Causal Consumption Relationship
Where possible, RCTM should distinguish resource use materially caused by the governed execution from resource use merely occurring at the same time.
Therefore:
Temporal Coincidence ≠ Consumption Attribution.
50. Consumption Chain
A primary execution action may trigger a sequence of resource-consuming activities.
For example:
Agent Action → Tool Call → Network Transfer → External Compute → Validation → Storage
RCTM should support traceability across such chains where material.
51. Secondary Consumption
A resource-consuming event may create further consumption elsewhere in the execution configuration.
Secondary consumption should remain visible where material to the complete resource footprint.
52. Cascading Consumption
Complex execution systems may produce multiple layers of secondary consumption.
RCTM should support bounded tracing without requiring infinite attribution across unrelated infrastructure.
53. Consumption Trace Depth
The appropriate depth of tracing should reflect:
materiality,
governance purpose,
observability,
and operational relevance.
Not every negligible infrastructure interaction requires separate representation.
54. Consumption Materiality
Materiality may determine which consumption events require explicit governance.
Materiality thresholds should not systematically exclude cumulative resource use that becomes significant in aggregate.
55. Cumulative Consumption
Repeated small resource events may produce significant total consumption.
RCTM should support aggregation across an appropriate measurement period.
56. Per-Execution Consumption
Consumption may be measured for one execution event.
This can support comparison across:
tasks,
models,
agents,
or configurations.
57. Per-Task Consumption
Resource consumption may be associated with a defined task.
The task boundary should be sufficiently explicit for meaningful interpretation.
58. Per-Completion Consumption
Consumption may be associated with Validated Completion.
This enables subsequent VEGA™ analysis such as resource consumption per successfully completed operational result.
IPS governs completion productivity.
59. Per-Output Consumption
Consumption may be associated with productive output where the output unit is sufficiently governed.
This does not independently establish productivity.
60. Batch Consumption
Where multiple tasks execute together, resource use may be measured at batch level.
The method used to distribute batch consumption across tasks should remain explicit where required.
61. Multi-Agent Consumption
Multi-agent systems may create consumption associated with:
individual agents,
coordination,
communication,
shared context,
tools,
validation,
and orchestration.
Therefore:
Individual Agent Consumption Sum ≠ Complete Multi-Agent Consumption by default.
62. Coordination Consumption
Coordination itself may consume material resources.
It should not disappear merely because it does not produce the final operational output directly.
63. Validation Consumption
Validation may create additional resource use through:
models,
tools,
tests,
human review,
or other mechanisms.
Where material, validation consumption should remain part of the execution resource trace.
64. Correction Consumption
Failed or insufficient execution may require correction.
The resources consumed by correction should remain distinguishable where relevant.
65. Rework Consumption
Repeated work may consume resources without creating additional completed output.
RCTM should preserve that resource use for subsequent productivity and economic analysis.
66. Retry Consumption
Retries may materially increase resource consumption.
A successful final outcome should not erase the resources consumed by unsuccessful attempts.
Therefore:
Successful Final Completion ≠ Only Consumption That Matters.
67. Failure Consumption
Failed executions may consume substantial resources without producing a Validated Completion.
That consumption should remain visible.
IES may subsequently govern its economic treatment as Failure Spend.
68. Human-Support Consumption
Where human operational support materially contributes to execution, relevant human time or capacity may be traced.
This supports a more complete execution-resource representation.
69. Human Correction Consumption
Human intervention required to correct machine output may constitute material resource consumption.
It should not disappear from the resource trace merely because it occurs outside the model runtime.
70. Tool Consumption
Tools may create separate resource use.
Where material, RCTM should identify both:
the tool interaction,
and relevant underlying resource consumption where observable.
71. Context Consumption
Large context windows, retrieval, memory access, or context preparation may create material resource demand.
RCTM may trace that consumption without governing cognitive productivity.
72. Memory Consumption
Execution may consume memory resources during:
reasoning,
context management,
state retention,
or coordination.
MGIA™ governs Memory as a broader governance domain.
RCTM governs only its resource-consumption relationship.
73. Storage Consumption
Outputs, logs, state, embeddings, artifacts, and other execution products may create storage consumption.
Persistent storage effects may continue after the immediate execution ends.
74. Network Consumption
Distributed execution may create material:
bandwidth,
transfer,
or communication consumption.
Network consumption should remain traceable where relevant.
75. Hardware Consumption
Hardware execution may be represented through appropriate utilization or consumption measures.
RCTM does not determine hardware design efficiency.
COS governs relevant execution optimization.
76. Energy Consumption
Execution-related energy consumption may be traced where measurable or defensibly estimated.
RCTM governs:
energy consumed by the intelligence execution
not:
energy generation,
grid governance,
energy-market allocation,
or the complete energy lifecycle.
77. Infrastructure Consumption
Execution may use shared infrastructure beyond the primary compute layer.
Material infrastructure consumption may be included using an appropriate attribution method.
78. Consumption and Resource Constraints
Actual consumption should be evaluated against the relevant Governed Resource Constraint State.
Consumption evidence may establish that execution remained:
within,
approached,
or exceeded
an applicable resource boundary.
79. Constraint Breach Trace
Where consumption exceeds a governed constraint, RCTM should preserve evidence of:
which resource,
which execution,
when,
how much,
and under what constraint state
the breach occurred.
80. Consumption Variance
Consumption Variance represents the difference between:
expected consumption
and
actual consumption.
Variance may reveal inaccurate assumptions or changing execution behavior.
81. Allocation-to-Consumption Variance
RCTM may compare:
allocated resource
against
actual consumption.
This can reveal:
underutilization,
unexpected demand,
or allocation mismatch.
82. Expected-to-Actual Consumption Variance
Expected resource use may differ materially from actual use even where allocation remains sufficient.
This difference should be capable of measurement.
83. Consumption Anomaly
A Consumption Anomaly is a resource-use pattern materially inconsistent with the expected or governed execution condition.
An anomaly does not independently establish:
failure,
abuse,
or inefficiency.
It establishes a condition requiring explanation.
84. Consumption Spike
A short-term increase in resource use may be legitimate or anomalous.
RCTM should preserve the spike and relevant execution context where material.
85. Consumption Drift
Consumption Drift occurs where resource use progressively changes relative to the established baseline without explicit resource-governance reassessment.
Possible causes may include:
model changes,
configuration changes,
workload changes,
software changes,
or hidden dependencies.
86. Resource Leakage
Resource Leakage represents consumption that cannot be adequately connected to legitimate execution need or expected resource behavior.
Leakage should be investigated rather than automatically classified as waste.
87. Orphan Consumption
Orphan Consumption is measured resource use for which no sufficiently governed Consumption Object can be identified.
This condition should remain visible.
Therefore:
Measured Resource Use Without Attribution ≠ Governed Consumption Trace.
88. Missing Consumption Evidence
An expected Resource Object may lack consumption evidence.
Absence of measurement should not automatically be interpreted as zero consumption.
Therefore:
No Measurement ≠ No Consumption.
89. Consumption Uncertainty
Consumption measurement may contain uncertainty arising from:
instrumentation,
sampling,
estimation,
shared-resource attribution,
or incomplete observability.
Uncertainty should remain explicit.
90. Consumption Confidence
Where useful, RCTM may represent confidence in a consumption estimate or attribution.
Confidence should derive from evidence quality.
91. Consumption Evidence Quality
Evidence quality may vary between:
direct metering,
high-resolution telemetry,
platform reporting,
reliable proxy,
modelled estimate,
or weak inference.
The evidence class should remain visible where material.
92. Consumption Reconciliation
Multiple measurement systems may produce different consumption records.
RCTM should support reconciliation where material differences affect the governed trace.
93. Duplicate Consumption
The same underlying resource use may appear in multiple monitoring, billing, or telemetry systems.
RCTM should prevent duplicate counting.
Therefore:
Multiple Consumption Records ≠ Multiple Consumption Events by default.
94. Missing Consumption
Material resource use may be omitted because of:
instrumentation gaps,
external services,
shared infrastructure,
or incomplete execution boundaries.
RCTM should preserve the gap rather than silently converting it to zero.
95. Consumption Completeness
The consumption trace should be sufficiently complete for the intended governance purpose.
Completeness does not require exhaustive measurement of every negligible resource interaction.
It requires material resource use to be represented.
96. Consumption Traceability Chain
A defensible trace should support, where applicable:
Operational Requirement → Execution → Resource Object → Allocation State → Constraint State → Consumption Event → Measurement Evidence → Attribution → Consumption Record
This establishes the resource-consumption chain required for subsequent VEGA™ governance.
97. Trace Identity
Consumption records should possess sufficient identity to prevent confusion between:
executions,
Resource Objects,
measurement periods,
or consumption events.
98. Trace Continuity
Where consumption occurs across multiple execution stages, the trace should preserve sufficient continuity to reconstruct the relevant resource path.
99. Trace Granularity
Trace granularity should correspond to the intended governance purpose.
Too little granularity may hide material consumption.
Excessive granularity may create measurement overhead without additional governance value.
100. Trace Integrity
Consumption traces should resist:
omission,
duplication,
misattribution,
boundary manipulation,
and unsupported estimation.
101. Consumption Provenance
Every material consumption record should preserve sufficient provenance to identify:
measurement source,
measurement method,
Resource Object,
Consumption Object,
time boundary,
attribution method,
uncertainty,
and relevant version.
102. Consumption Versioning
Material changes to:
measurement methods,
resource identity,
attribution methods,
execution configuration,
or consumption boundaries
should be versioned where necessary for comparability and auditability.
103. Consumption Reassessment
A consumption trace should be reassessed where:
new evidence becomes available,
measurement improves,
the execution configuration changes,
previously hidden resources are discovered,
or attribution methodology changes materially.
104. Consumption and Productivity
RCTM establishes the resource-consumption evidence.
IPS establishes productivity.
Together they can support later relationships such as:
resource consumed per productive output,
or
resource consumed per Validated Completion.
Therefore:
Resource Consumption ≠ Productivity.
105. Consumption and Computational Optimization
COS may use RCTM evidence to determine whether computational resources are being used proportionately and efficiently.
RCTM itself does not optimize execution.
Therefore:
Consumption Measurement ≠ Optimization.
106. Consumption and Economics
Resource consumption may produce economic cost.
IES governs the economic translation.
RCTM preserves the underlying resource evidence.
Therefore:
Resource Consumption ≠ Economic Cost by itself.
107. Consumption and Value
The amount of resource consumed does not independently establish the Value produced.
VGS governs Value.
Therefore:
Low Consumption ≠ High Value.
108. Consumption and Sustainability
ISS may use consumption evidence when assessing long-term intelligence sustainability.
RCTM does not govern the complete sustainability domain.
109. Consumption and Authority
The ability to measure resource consumption does not create authority to change allocation, execution, or organizational policy.
Therefore:
Measurement ≠ Decision Authority.
110. Consumption Integrity Principle
RCTM should resist:
treating allocation as consumption,
treating reservation as consumption,
counting only primary-model resources,
ignoring indirect resource use,
ignoring failed attempts,
ignoring correction and rework,
hiding human support,
double-counting shared resources,
treating missing measurement as zero,
and converting weak estimates into precise consumption claims.
The governing principle is:
Trace what the execution actually consumes — not merely what the system exposes most visibly.
111. Governed Resource Consumption Trace
The principal output of RCTM is:
Governed Resource Consumption Trace
It should identify, where applicable:
Consumption Object,
Resource Object,
Consumption Basis,
Consumption Boundary,
direct consumption,
indirect consumption,
embedded consumption,
hidden consumption,
shared consumption,
dedicated consumption,
persistent consumption,
transient consumption,
supporting consumption,
measurement unit,
measurement method,
observability,
resolution,
timestamp,
duration,
rate,
peak,
average,
sustained consumption,
idle consumption,
standby consumption,
utilization,
attribution method,
consumption chain,
secondary consumption,
cumulative consumption,
per-execution consumption,
per-task consumption,
per-completion consumption,
multi-agent consumption,
validation consumption,
correction consumption,
rework consumption,
retry consumption,
failure consumption,
human-support consumption,
constraint relationship,
variance,
anomalies,
drift,
leakage,
orphan consumption,
missing evidence,
uncertainty,
evidence quality,
reconciliation,
completeness,
trace identity,
version,
and provenance.
112. Meaning of the Output
A Governed Resource Consumption Trace establishes what material resources the intelligence execution actually consumed and the evidentiary relationship connecting that consumption to the execution.
It enables subsequent VEGA™ governance to distinguish:
what was available,
what was allocated,
what was reserved,
what was actually consumed,
and what consumption remains uncertain or unattributed.
It completes the RGS resource-governance chain.
113. Minimum Implementation Framework
A conforming implementation of RCTM must provide exactly the following six capabilities:
1. Consumption Object & Boundary Governance
Capability to identify the governed execution, Resource Objects, consumption scope, and material direct and indirect resource relationships.
2. Consumption Measurement & Observability Governance
Capability to measure, estimate, or represent material resource consumption using appropriate units, resolution, observability states, and uncertainty.
3. Consumption Attribution & Chain Governance
Capability to connect resource use to execution objects and trace material shared, embedded, supporting, secondary, and cascading consumption.
4. Consumption Variance & Anomaly Governance
Capability to identify allocation-to-consumption variance, expected-to-actual variance, spikes, drift, leakage, orphan consumption, and missing evidence.
5. Consumption Evidence & Trace Integrity Governance
Capability to preserve evidence quality, reconciliation, completeness, identity, continuity, versioning, provenance, and prevent omission, duplication, or unsupported attribution.
6. RGS Completion & VEGA™ Handoff Governance
Capability to produce a Governed Resource Consumption Trace suitable for subsequent Computational Optimization and broader VEGA™ governance.
114. Governance Logic
The canonical RCTM governance logic is:
Governed Resource Constraint State
↓
Identify Consumption Object
↓
Identify Relevant Resource Objects
↓
Establish Consumption Boundary
↓
Identify Direct / Indirect / Shared / Embedded Consumption
↓
Determine Measurement Method / Unit / Resolution
↓
Observe or Estimate Actual Resource Consumption
↓
Attribute Consumption to Relevant Execution Objects
↓
Trace Supporting / Secondary / Cascading Consumption
↓
Account for Failure / Retry / Correction / Rework Consumption
↓
Compare Allocation / Expected / Actual Consumption
↓
Detect Variance / Anomaly / Drift / Leakage
↓
Identify Missing / Orphan / Uncertain Consumption
↓
Assess Evidence Quality / Completeness
↓
Preserve Identity / Version / Provenance
↓
Establish Governed Resource Consumption Trace
↓
RGS Resource Governance Complete
↓
Handoff to Computational Optimization and Subsequent VEGA™ Governance
115. Governance Outputs
RCTM may produce:
Consumption Object Record
Resource Consumption Record
Consumption Boundary
Direct Consumption Record
Indirect Consumption Record
Embedded Consumption Record
Hidden Consumption Record
Shared Consumption Record
Persistent Consumption Record
Supporting Consumption Record
Resource Consumption Unit
Consumption Measurement Record
Consumption Observability State
Consumption Timestamp
Consumption Duration
Consumption Rate
Peak Consumption State
Average Consumption State
Sustained Consumption State
Idle Consumption Record
Standby Consumption Record
Resource Utilization State
Consumption Attribution Record
Consumption Chain
Secondary Consumption Record
Cumulative Consumption Record
Per-Execution Consumption
Per-Task Consumption
Per-Completion Consumption
Multi-Agent Consumption Record
Validation Consumption Record
Correction Consumption Record
Rework Consumption Record
Retry Consumption Record
Failure Consumption Record
Human-Support Consumption Record
Constraint Breach Consumption Trace
Consumption Variance State
Consumption Anomaly State
Consumption Drift State
Resource Leakage State
Orphan Consumption State
Missing Consumption Evidence State
Consumption Uncertainty
Consumption Evidence Quality State
Consumption Reconciliation Record
Consumption Completeness State
Consumption Trace Identity
Consumption Version
Consumption Provenance
The principal output is:
Governed Resource Consumption Trace
116. Critical Distinctions
RCTM preserves the following distinctions:
Allocated Resource ≠ Consumed Resource
Application Consumption ≠ Total Execution Consumption
Visible Consumption ≠ Total Material Consumption
Estimated Consumption ≠ Directly Measured Consumption
No Productive Activity ≠ No Resource Consumption
Allocation Quantity ≠ Consumption Quantity
Temporal Coincidence ≠ Consumption Attribution
Successful Final Completion ≠ Only Consumption That Matters
Individual Agent Consumption Sum ≠ Complete Multi-Agent Consumption by default
Measured Resource Use Without Attribution ≠ Governed Consumption Trace
No Measurement ≠ No Consumption
Multiple Consumption Records ≠ Multiple Consumption Events by default
Resource Consumption ≠ Productivity
Consumption Measurement ≠ Optimization
Resource Consumption ≠ Economic Cost by itself
Low Consumption ≠ High Value
Measurement ≠ Decision Authority
117. Cross-Architecture Boundary
RCTM governs Consumption Traceability within RGS.
RIM governs Resource Identification.
RSAM governs Resource Allocation.
DRRM governs Dynamic Resource Reallocation.
RCM governs Resource Constraint.
IPS governs productivity.
COS governs computational optimization.
IES governs execution economics.
VGS governs Value.
ISS governs long-term intelligence sustainability.
VAS governs Value Attribution.
DGS governs lifecycle decisions.
MGIA™ governs Memory as a broader governance domain.
VALIDOS™ may govern validation methodology.
ADIT® may govern evidence and auditability.
ORA™ may establish relevant Operational Reality.
HAGA™ governs Harm.
LIGA™ governs Liability.
RCTM may provide resource evidence to these governed spaces without replacing their methodologies.
118. Canonical Boundary
RCTM begins where intelligence execution operates within an identified, allocated, dynamically governed, and constrained resource state.
RCTM governs:
Consumption Objects,
Resource Consumption Objects,
Consumption Bases,
Consumption Boundaries,
direct consumption,
indirect consumption,
embedded consumption,
hidden consumption,
shared consumption,
dedicated consumption,
persistent consumption,
transient consumption,
supporting consumption,
resource units,
native units,
composite measures,
direct measurement,
estimation,
proxy measurement,
observability,
measurement resolution,
temporal resolution,
timestamps,
duration,
rates,
peak consumption,
average consumption,
sustained consumption,
idle consumption,
standby consumption,
reserved-but-unconsumed resources,
allocated-but-unconsumed resources,
utilization,
consumption attribution,
shared attribution,
usage-based attribution,
time-based attribution,
activity-based attribution,
causal consumption relationships,
consumption chains,
secondary consumption,
cascading consumption,
trace depth,
materiality,
cumulative consumption,
per-execution consumption,
per-task consumption,
per-completion consumption,
per-output consumption,
batch consumption,
multi-agent consumption,
coordination consumption,
validation consumption,
correction consumption,
rework consumption,
retry consumption,
failure consumption,
human-support consumption,
tool consumption,
context consumption,
memory consumption,
storage consumption,
network consumption,
hardware consumption,
execution-related energy consumption,
infrastructure consumption,
constraint-breach traces,
consumption variance,
anomalies,
spikes,
drift,
leakage,
orphan consumption,
missing evidence,
uncertainty,
confidence,
evidence quality,
reconciliation,
duplicate prevention,
completeness,
trace identity,
trace continuity,
granularity,
integrity,
provenance,
versioning,
and reassessment.
RCTM does not independently govern:
Resource Identification,
Resource Allocation,
Dynamic Resource Reallocation,
Resource Constraint methodology,
productivity,
computational optimization,
economic treatment,
Value,
sustainability,
Value Attribution,
Harm,
Liability,
or lifecycle authority.
RCTM ends when actual execution-resource consumption has been represented as a:
Governed Resource Consumption Trace
for subsequent VEGA™ governance.
119. Use Case 1 — AI Service Appears Efficient When Only Model Usage Is Counted
An organization measures the resource footprint of an AI service using only primary model compute.
RCTM traces the wider execution and identifies additional material consumption from retrieval, routing, validation, tool calls, storage, network activity, retries, and human exception handling.
The resulting Governed Resource Consumption Trace establishes a materially different resource picture from the model- only measurement.
Subsequent optimization and economic analysis can therefore operate on the actual execution system rather than its most visible component.
Application Consumption ≠ Total Execution Consumption.
120. Use Case 2 — Successful Agent Task Required Multiple Failed Attempts
An autonomous workflow ultimately completes a required task successfully.
The final successful execution consumed relatively little resource.
However, the workflow previously generated several failed attempts, repeated tool calls, validation cycles, and a human correction.
RCTM preserves the resource consumption associated with the entire governed execution path rather than attributing consumption only to the final successful attempt.
This provides subsequent VEGA™ governance with the evidence necessary to evaluate the real resource requirement of producing the Validated Completion.
Successful Final Completion ≠ Only Consumption That Matters.
121. Canonical Closing Statement
A system can reserve resources it never uses.
It can allocate capacity it never consumes.
And it can report the resources of its visible model while an entire execution system consumes resources around it.
Consumption traceability exists to separate those realities.
The relevant question is not what the system could consume.
It is not what the infrastructure made available.
And it is not what was assigned on paper.
It is what the execution actually consumed in producing the governed operational result.
Allocated Resource ≠ Consumed Resource.
Trace the resource from execution to evidence.
What intelligence truly consumes becomes governable only when the consumption can be connected to the work that caused it.