CDM — Continuation Decision Module
Architecture:
VEGA™ — Value Execution Governance Architecture
Parent Standard:
Decision Governance Standard (DGS)
Category:
Governance & Enforcement
Subcategory:
Value Execution Governance
Type: Core Module
Governed Space:
Intelligence Continuation Decision Governance
Module ID: VEGA-DGS-CDM-002
Origin ID: OOF.CM.VEGA.10.02.01
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 Continuation Decision Module (CDM) governs whether an existing intelligence operating model remains sufficiently justified to continue operating under its current or explicitly modified conditions.
CDM evaluates the Governed Decision Evidence State to determine whether continued operation remains supported by validated Value, sufficient productivity, proportionate resource consumption, computational efficiency, economic justification, legitimate Strategic Value, sustainability, Value Attribution, and applicable governed constraints.
CDM distinguishes continued operation from justified continuation.
The fact that an intelligence system is already deployed, technically functional, funded, integrated, or operationally familiar does not by itself justify continued use.
CDM may establish continuation as:
unconditional,
conditional,
temporarily justified,
reassessment-dependent,
or unsupported.
It does not authorize continued operation.
Its principal output is:
Governed Continuation Decision State
Its foundational distinction is:
Current Operation ≠ Justified Continuation.
2. Operational Role
The operational role of CDM is:
to determine whether the governed evidence continues to justify maintaining an intelligence operating model in operation, under what conditions that justification remains valid, and when continuation must be reassessed, modified, suspended, or passed to another lifecycle decision pathway.
CDM is therefore not a historical assessment of why a system was originally deployed.
It asks whether the system remains justified now and across the relevant continuation horizon.
3. Position in the DGS Internal Flow
The DGS internal governance flow is:
Decision Evidence Consolidation → Continuation Decision → Scaling Decision → Migration Decision → Retirement Decision
CDM governs:
Continuation Decision
Its principal upstream input is:
Governed Decision Evidence State
Its principal output is:
Governed Continuation Decision State
That state may support subsequent:
Scaling Decision Governance
where expansion is being considered.
Continuation may also expose conditions requiring optimization, reconfiguration, migration, suspension, reassessment, or eventual retirement.
4. Central Operational Question
Does the governed evidence continue to justify operation of the intelligence operating model, under what conditions, for what period, and with what requirements for monitoring or reassessment?
5. Continuation Decision Object
Every CDM assessment should identify the intelligence operating model whose continuation is being evaluated.
The object may include:
a model deployment,
agent system,
automation capability,
decision-support system,
workflow,
robotic intelligence configuration,
hybrid Human–AI operating model,
or another bounded intelligence capability.
6. Continuation Boundary
CDM should define exactly what is proposed to continue.
Continuation of:
a capability,
a model,
an execution configuration,
an infrastructure configuration,
or an entire operating model
may represent different decisions.
The continuation boundary should therefore remain explicit.
7. Continuation Horizon
Every continuation assessment should define the period over which continuation is being evaluated.
Possible horizons may include:
next execution period,
next operational cycle,
temporary extension,
defined review period,
medium-term continuation,
or intended lifecycle continuation.
Continuation Today ≠ Continuation Indefinitely.
8. Continuation Context
Continuation should be evaluated under the conditions in which the system is expected to operate.
Relevant context may include:
purpose,
workload,
scale,
configuration,
environment,
cost conditions,
resource conditions,
dependency conditions,
and applicable constraints.
9. Continuation Baseline
CDM should identify the baseline against which continuation is evaluated.
The baseline may represent:
current operation,
previous configuration,
minimum acceptable state,
alternative mechanism,
or no continuation.
The baseline should be explicit where it materially affects the decision.
10. Continuation Evidence
CDM consumes the Governed Decision Evidence State established by DECM.
It should not silently substitute raw metrics for governed evidence.
Relevant evidence may concern:
Value,
execution,
productivity,
resources,
Computational Optimization,
economics,
Strategic Value,
sustainability,
attribution,
and applicable governed constraints.
11. Value Continuation Condition
Continuation should remain connected to legitimate governed Value.
A system that continues producing outputs but no longer produces sufficient Value may no longer justify continuation.
Continued Output ≠ Continued Value.
12. Value Persistence
CDM should evaluate whether previously validated Value remains present.
Value may:
persist,
increase,
decline,
change form,
become conditional,
or disappear.
Historical Value does not automatically establish current Value.
13. Value Deterioration
Material deterioration in Value should trigger continuation reassessment.
Deterioration may occur because:
the operational need changed,
outputs became less useful,
another mechanism became more effective,
or the Value threshold changed.
14. Value Transformation
An intelligence operating model may cease producing its original Value while creating another legitimate form of Value.
CDM should distinguish Value transformation from Value disappearance.
Any new Value claim should be appropriately governed rather than assumed.
15. Productivity Continuation Condition
Continuation should consider whether the system continues producing sufficient validated productive output.
High operational activity does not establish sufficient productivity.
Continued Activity ≠ Continued Productivity.
16. Productivity Deterioration
CDM should identify material decline in productive performance.
Possible causes may include:
changed workload,
model degradation,
workflow complexity,
increasing correction,
or changing operational conditions.
17. Resource Continuation Condition
Continued operation should remain proportionate to the resources it consumes.
Resource evidence may concern:
compute,
energy,
hardware,
infrastructure,
human effort,
time,
or another constrained resource.
18. Resource Escalation
A system may continue producing the same Value while consuming increasing resources.
CDM should identify whether resource escalation remains justified.
Stable Value + Rising Consumption requires renewed justification.
19. Computational Continuation Condition
The current execution configuration should remain sufficiently computationally proportionate to the required outcome.
CDM may consume COS evidence concerning:
Problem-Space Efficiency,
Cognitive Efficiency,
Orchestration Efficiency,
Model Allocation Efficiency,
Compute Allocation Efficiency,
and Hardware Execution Efficiency.
20. Over-Intelligence Condition
An operating model may use more intelligence than the task now requires.
Changes in:
task structure,
automation,
algorithms,
models,
or operational knowledge
may make a simpler mechanism sufficient.
CDM should preserve this condition for reconfiguration or optimization.
Previously Justified Intelligence ≠ Permanently Justified Intelligence.
21. Economic Continuation Condition
Continuation should remain economically justified where economic Value is relevant.
CDM may consider:
Effective Task Cost,
Failure Spend,
Correction Cost,
Intelligence ROI,
and Value-to-Cost evidence.
22. Cost Deterioration
Cost deterioration may occur even where the system remains technically successful.
Examples include:
increased inference cost,
higher infrastructure cost,
higher human-review cost,
increased correction,
or increasing maintenance burden.
Material deterioration should affect continuation evaluation.
23. Strategic Value Continuation Condition
An intelligence operating model may remain justified despite weak immediate return where legitimate Strategic Value persists.
Strategic Value should remain evidenced.
Strategic Value ≠ Permanent Exemption from Performance Evidence.
24. Strategic Value Maturation
As continuation extends and investment accumulates, Strategic Value claims should mature through stronger evidence.
Strategic justification should not remain indefinitely dependent on the same early assumptions.
25. Strategic Milestone Continuation
Where continuation depends on Strategic Milestones, CDM should identify whether those milestones are:
achieved,
progressing,
delayed,
failed,
or unresolved.
Persistent milestone failure may weaken continuation justification.
26. Sustainability Continuation Condition
Continuation should remain compatible with the governed Intelligence Sustainability state.
A system may be viable now while becoming unsustainable across its intended lifecycle.
Current Viability ≠ Long-Term Sustainability.
27. Sustainability Deterioration
Material deterioration in:
energy viability,
infrastructure sustainability,
economic continuity,
or dependency sustainability
should trigger continuation reassessment.
28. Dependency Continuation Condition
Continuation may depend on:
providers,
models,
hardware,
infrastructure,
data,
tools,
specialized expertise,
or other critical dependencies.
CDM should consume the relevant governed dependency state rather than recreate it.
29. Dependency Concentration
A system may produce strong Value while becoming increasingly dependent on a single critical dependency.
This does not automatically require discontinuation.
But it may justify:
conditions,
mitigation,
migration analysis,
or increased monitoring.
30. Attribution Continuation Condition
Value Attribution may reveal whether the operating model's current contribution structure remains justified.
For example, a complex AI configuration may appear valuable while attribution shows that most material Value now comes from a simpler human or technical component.
Such evidence may affect continuation.
31. Human Contribution Change
If human intervention becomes increasingly necessary to preserve Value, the operating model may have changed materially.
CDM should preserve evidence of:
increasing correction,
manual recovery,
human validation,
or human substitution.
32. AI Contribution Change
AI contribution may increase, decrease, or change in function over time.
Continuation should reflect the current Value-producing structure rather than historical assumptions about AI contribution.
33. Multi-Agent Contribution Change
In multi-agent systems, agents may accumulate while only some remain materially relevant.
CDM should consider whether system complexity remains justified by actual contribution.
More Agents ≠ More Continuation Value.
34. Applicable Constraint Continuation
Continuation is feasible only within applicable governed constraints.
Strong Value, productivity, or economics cannot by themselves justify continuation where a competent non-compensable constraint is violated.
35. Constraint Persistence
CDM should determine whether applicable constraints remain satisfied throughout the continuation horizon.
A system should not receive unconditional continuation where compliance with a material constraint is temporary or uncertain.
36. New Constraint
A new legal, institutional, technical, operational, or governance constraint may materially change continuation feasibility.
CDM should support reassessment when a competent new constraint becomes applicable.
37. Continuation Evidence Sufficiency
Evidence supporting continuation should be sufficient for the scope, duration, and materiality of the continuation being considered.
A short reversible extension may require a different evidence threshold from long-term mission-critical continuation.
38. Continuation Evidence Freshness
Evidence should remain sufficiently current.
Historical success alone should not justify continuation where:
configuration,
cost,
Value,
dependencies,
or operating conditions
have materially changed.
39. Continuation Confidence
Possible confidence states may include:
High Confidence
Moderate Confidence
Conditional
Evidence-Limited
Low Confidence
Conflicted
Unresolved
Confidence should correspond to the underlying evidence.
40. Continuation Uncertainty
Material uncertainty should remain explicit.
Uncertainty may concern:
future Value,
cost,
resource demand,
Strategic Value,
dependency,
sustainability,
or another continuation condition.
Uncertainty does not automatically prevent continuation.
It may require conditional continuation.
41. Continuation Conflict
Evidence may support continuation in one dimension and challenge it in another.
For example:
Value may remain high,
cost may deteriorate,
Strategic Value may remain strong,
and sustainability may be uncertain.
CDM should preserve the conflict.
42. Continuation Trade-Off
Where continuation requires accepting a material trade-off, that trade-off should be explicit.
A trade-off may involve:
Value versus cost,
capability versus dependency,
productivity versus resource consumption,
or short-term Value versus long-term sustainability.
43. Non-Compensable Continuation Condition
Some continuation conditions cannot be offset by strength elsewhere.
A non-compensable constraint remains binding regardless of:
ROI,
productivity,
Strategic Value,
or technical performance.
44. Continuation Threshold
CDM may define thresholds indicating whether continuation remains sufficiently justified.
Thresholds may be:
quantitative,
categorical,
conditional,
or composite.
No universal threshold is required.
45. Threshold Breach
A material threshold breach should trigger an appropriate response.
Possible responses include:
reassessment,
optimization,
reconfiguration,
conditional continuation,
migration analysis,
suspension,
or retirement analysis.
46. Continuation State
Possible continuation states may include:
Continue
Continue Conditionally
Continue Temporarily
Continue with Optimization
Continue with Reconfiguration
Continue Pending Migration Assessment
Reassess
Suspend Pending Evidence
Continuation Not Supported
Evidence-Limited
Conflicted
Unresolved
These states are methodological decision states.
They are not authorizations.
47. Unconditional Continuation
Unconditional Continuation may be supported where the evidence sufficiently establishes that:
Value remains justified,
the operating model remains proportionate,
material constraints remain satisfied,
and no material continuation condition requires intervention.
“Unconditional” applies within the defined decision horizon.
It does not mean permanent approval.
48. Conditional Continuation
Conditional Continuation applies where continued operation remains justified only if one or more explicit conditions are satisfied.
Conditions should be:
material,
measurable where practicable,
time-bounded where appropriate,
and linked to reassessment.
49. Continuation Condition Object
Each material continuation condition should identify:
the required state,
the current state,
the evidence source,
the threshold,
the monitoring mechanism,
the review point,
and the consequence of failure.
50. Optimization Condition
Continuation may remain justified subject to optimization.
Examples may include:
reducing unnecessary reasoning,
improving orchestration,
changing model allocation,
reducing compute,
or improving hardware utilization.
Optimization should not become an indefinite substitute for resolving persistent failure.
51. Reconfiguration Condition
Continuation may require a material configuration change.
Potential changes include:
model,
agent structure,
tools,
routing,
validation,
human involvement,
compute,
or infrastructure.
The current capability may remain valuable even where its current configuration does not.
52. Evidence Acquisition Condition
Continuation may be temporarily justified while missing evidence is acquired.
The missing evidence and acquisition deadline should remain explicit.
Temporary Continuation ≠ Evidence Sufficiency.
53. Dependency Mitigation Condition
Continuation may require reduction or mitigation of a material dependency.
Possible responses include:
alternative providers,
redundancy,
substitution capability,
migration preparation,
or reduced concentration.
54. Cost Reduction Condition
Where economic deterioration threatens continuation justification, continued operation may depend on measurable cost improvement.
The condition should remain linked to Value preservation.
Reducing cost while destroying Value does not satisfy the underlying objective.
55. Sustainability Improvement Condition
Continuation may require improvement in:
energy viability,
infrastructure sustainability,
economic continuity,
or dependency sustainability.
The required state should be measurable where practicable.
56. Validation Condition
Where Value or performance evidence remains insufficiently validated, continuation may depend on additional validation.
CDM does not replace VALIDOS™.
It preserves validation requirements as continuation conditions.
57. Human Oversight Condition
Where another competent governance architecture requires human oversight, CDM may preserve that requirement as a continuation condition.
CDM does not independently create Human Override or oversight authority.
58. Continuation Monitoring
Material continuation conditions should be monitored.
Monitoring should focus on the states capable of changing continuation justification.
Not every available metric requires continuous monitoring.
59. Monitoring Proportionality
Monitoring intensity should correspond to:
decision materiality,
system volatility,
condition criticality,
reversibility,
and rate of change.
60. Continuation Trigger
A Continuation Trigger is an event or state requiring review.
Triggers may include:
Value deterioration,
cost increase,
resource escalation,
dependency failure,
threshold breach,
configuration change,
constraint change,
or material new evidence.
61. Event-Driven Continuation Review
CDM should support reassessment when material events occur.
A system should not wait for a scheduled review where evidence already indicates that continuation conditions materially changed.
62. Periodic Continuation Review
Where appropriate, continuation should also be reviewed periodically.
Review frequency should reflect the operating environment and decision materiality.
63. Continuation State Expiration
A Governed Continuation Decision State may have an explicit expiration point.
Expiration requires:
renewal,
reassessment,
or transition to another lifecycle state.
Continuation is a governed state, not a permanent inheritance.
64. Continuation Drift
Continuation Drift occurs where a system remains operational while the evidence supporting its original continuation gradually changes.
This may include:
Value drift,
cost drift,
resource drift,
dependency drift,
or contribution drift.
65. Silent Continuation
Silent Continuation occurs where operation continues because no explicit decision to stop has been made.
CDM rejects this as a sufficient governance basis.
No Decision to Stop ≠ Decision to Continue.
66. Inertia Risk
Operational familiarity may create resistance to reassessment.
A system may continue because:
teams know it,
processes depend on it,
contracts exist,
or replacement appears inconvenient.
These factors may be relevant.
They do not establish Value justification.
67. Sunk-Cost Bias
Past investment may influence organizational preferences.
CDM should preserve:
Historical Investment ≠ Future Value Justification.
The relevant question is whether future continuation remains justified.
68. Legacy Bias
An established system should not receive preferential continuation merely because it has operated for a long period.
System Age ≠ Continuation Justification.
69. Success Bias
Past success may be relevant evidence.
It is not sufficient where present conditions materially differ.
Historical Success ≠ Current Continuation Justification by default.
70. Capability Bias
A highly capable system may still be disproportionate to the operational requirement.
Capability ≠ Continuation Justification.
71. Availability Bias
A system may continue because it is immediately available.
Availability can affect transition feasibility.
But:
Availability ≠ Value Justification.
72. Contractual Lock-In
Existing contracts may affect the economic or operational feasibility of change.
CDM may preserve this evidence.
It does not convert contractual lock-in into Value.
73. Technical Lock-In
Technical integration may make replacement difficult.
This may support temporary continuation while migration becomes feasible.
It should not automatically establish long-term continuation justification.
74. Operational Dependency
Operations may become materially dependent on an intelligence capability.
Dependency may increase the cost of discontinuation.
It may also increase the importance of sustainability and migration planning.
Dependency ≠ Justification by itself.
75. Continuation Alternatives
CDM should consider whether continuation is being evaluated against realistic alternatives.
Alternatives may include:
current configuration,
optimized configuration,
reconfigured system,
simpler intelligence mechanism,
deterministic mechanism,
human process,
migration,
or discontinuation.
76. No-Intelligence Alternative
Where appropriate, CDM should preserve the possibility that the intelligence mechanism is no longer required.
The VEGA™ allocation principle remains relevant:
Use the least complex mechanism capable of reliably producing the required validated outcome.
77. Simpler-Mechanism Alternative
A simpler mechanism may become capable of producing the required validated outcome.
Continuation of a more complex intelligence configuration should then require renewed justification.
78. Human Alternative
Where appropriate, a human or Human–AI process may be a relevant alternative.
CDM does not assume automation is inherently preferable.
The comparison should follow governed Value and resource evidence.
79. Alternative Configuration
Continuation may be justified at the capability level while the current configuration is not.
CDM should therefore distinguish:
Continue Capability
from
Continue Current Configuration.
80. Continuation Reversibility
CDM should assess how easily continued operation can later be changed.
Possible states may include:
Highly Reversible
Reversible
Conditionally Reversible
Difficult to Reverse
Effectively Irreversible
Lower reversibility may justify stronger continuation evidence.
81. Continuation Commitment
Continuation may create additional:
capital commitment,
technical integration,
data dependency,
operational dependency,
or organizational lock-in.
These effects should be visible where material.
82. Continuation Optionality
A continuation decision may preserve or reduce future options.
Temporary or conditional continuation may preserve optionality while additional evidence develops.
83. Continuation-to-Scaling Boundary
Continuation does not automatically justify scaling.
A system may remain justified at its current scope without being justified at larger scale.
Justified Continuation ≠ Justified Scaling.
Scaling is governed separately.
84. Continuation-to-Migration Boundary
Continuation and migration are not opposites.
A system may continue temporarily while migration is prepared.
CDM may therefore establish:
Continue Pending Migration Assessment
where appropriate.
85. Continuation-to-Retirement Boundary
Continuation Not Supported does not automatically establish immediate retirement.
Other lifecycle states may include:
suspension,
reconfiguration,
migration,
or further evidence acquisition.
Retirement is governed separately.
86. Continuation-to-Optimization Relationship
Optimization may preserve continuation where the underlying Value remains legitimate but execution has become disproportionate.
CDM identifies the continuation condition.
COS governs the applicable Computational Optimization mechanisms.
87. Continuation-to-Sustainability Relationship
ISS establishes whether the intelligence operating model can remain viable across its intended lifecycle.
CDM consumes that evidence to determine whether continuation remains justified.
CDM does not recreate sustainability governance.
88. Continuation-to-Attribution Relationship
VAS may reveal which contributors actually produce the Value supporting continuation.
This can expose configurations where:
complexity persists,
but material Value comes from only a limited subset of the system.
CDM may use this evidence to support reconfiguration or optimization conditions.
89. Continuation-to-Decision Authority Boundary
CDM produces a governed continuation state.
It does not authorize continued operation.
The competent Decision Authority may be:
human,
organizational,
institutional,
regulatory,
or otherwise legitimately established.
Governed Continuation State ≠ Continuation Authorization.
90. Automated Continuation Assessment
CDM may be implemented through automated or semi-automated assessment.
Automation may:
monitor evidence,
detect thresholds,
identify drift,
or propose continuation states.
Automated Continuation Assessment ≠ Automated Authority.
91. Continuation Recommendation
Where appropriate, CDM may produce a methodological continuation recommendation associated with the Governed Continuation Decision State.
The recommendation should preserve:
evidence,
conditions,
confidence,
uncertainty,
and authority boundary.
92. Governed Continuation Decision State
The principal output of CDM is:
Governed Continuation Decision State
It should identify, where applicable:
Continuation Decision Object,
continuation boundary,
continuation horizon,
continuation context,
baseline,
Value condition,
productivity condition,
resource condition,
Computational Optimization condition,
economic condition,
Strategic Value condition,
sustainability condition,
dependency condition,
attribution condition,
applicable constraints,
evidence sufficiency,
confidence,
uncertainty,
conflicts,
trade-offs,
thresholds,
conditions,
monitoring requirements,
review triggers,
expiration,
reversibility,
optionality,
and Decision Authority boundary.
93. Minimum Implementation Framework
A conforming implementation of CDM must provide exactly the following six capabilities:
1. Continuation Object, Boundary & Horizon Governance
Capability to identify the intelligence operating model, continuation scope, operating context, decision horizon, and relevant baseline.
2. Continuation Justification Governance
Capability to evaluate whether Value, productivity, resources, Computational Optimization, economics, Strategic Value, sustainability, attribution, and applicable governed constraints continue to support operation.
3. Continuation Condition & Threshold Governance
Capability to establish Continuation Thresholds, Non-Compensable Conditions, Conditional Continuation requirements, optimization conditions, reconfiguration conditions, evidence conditions, and other measurable continuation requirements.
4. Continuation Monitoring & Reassessment Governance
Capability to monitor material continuation conditions, detect threshold breaches and drift, establish triggers, support periodic and event-driven review, and define Continuation State Expiration.
5. Continuation Integrity & Alternative Governance
Capability to control sunk-cost, legacy, success, capability, availability, dependency, and lock-in biases while preserving realistic continuation alternatives, reversibility, and optionality.
6. DGS Scaling Decision Handoff Governance
Capability to produce a Governed Continuation Decision State sufficiently explicit for subsequent Scaling Decision Governance while preserving conditions, uncertainty, and Decision Authority boundaries.
94. Governance Logic
The canonical CDM governance logic is:
Governed Decision Evidence State
↓
Identify Continuation Decision Object
↓
Define Continuation Boundary / Horizon / Context
↓
Establish Continuation Baseline
↓
Assess Value Persistence
↓
Assess Productivity Continuation
↓
Assess Resource Proportionality
↓
Assess Computational Proportionality
↓
Assess Economic Continuation
↓
Assess Strategic Value Persistence
↓
Assess Sustainability
↓
Assess Dependency Conditions
↓
Assess Attribution Relevance
↓
Identify Applicable Governed Constraints
↓
Assess Evidence Sufficiency / Freshness
↓
Assess Confidence / Uncertainty / Conflict
↓
Identify Continuation Trade-Offs
↓
Apply Continuation Thresholds
↓
Test Non-Compensable Conditions
↓
Evaluate Continuation Alternatives
↓
Assess Reversibility / Optionality / Lock-In
↓
Determine Continuation State
↓
Define Continuation Conditions
↓
Define Monitoring / Review Triggers / Expiration
↓
Preserve Decision Authority Boundary
↓
Establish Governed Continuation Decision State
↓
Handoff to Scaling Decision Governance where applicable
95. Governance Outputs
CDM may produce:
Continuation Decision Object
Continuation Boundary
Continuation Horizon
Continuation Context
Continuation Baseline
Value Continuation State
Value Persistence State
Value Deterioration State
Productivity Continuation State
Resource Continuation State
Resource Escalation State
Computational Continuation State
Over-Intelligence State
Economic Continuation State
Cost Deterioration State
Strategic Value Continuation State
Strategic Milestone Continuation State
Sustainability Continuation State
Dependency Continuation State
Dependency Concentration State
Attribution Continuation State
Applicable Constraint Continuation State
Continuation Evidence Sufficiency State
Continuation Confidence State
Continuation Uncertainty State
Continuation Conflict State
Continuation Trade-Off Record
Continuation Threshold
Non-Compensable Continuation Condition
Continuation State
Continuation Condition Object
Optimization Condition
Reconfiguration Condition
Evidence Acquisition Condition
Dependency Mitigation Condition
Cost Reduction Condition
Sustainability Improvement Condition
Validation Condition
Continuation Monitoring State
Continuation Trigger
Continuation Review State
Continuation State Expiration
Continuation Drift State
Continuation Alternative Set
Continuation Reversibility State
Continuation Commitment State
Continuation Optionality State
Continuation Recommendation
The principal output is:
Governed Continuation Decision State
96. Critical Distinctions
CDM preserves the following distinctions:
Current Operation ≠ Justified Continuation
Continuation Today ≠ Continuation Indefinitely
Continued Output ≠ Continued Value
Continued Activity ≠ Continued Productivity
Historical Value ≠ Current Value
Historical Success ≠ Current Continuation Justification by default
Historical Investment ≠ Future Value Justification
System Age ≠ Continuation Justification
Capability ≠ Continuation Justification
Availability ≠ Value Justification
Dependency ≠ Justification by itself
Strategic Value ≠ Permanent Exemption from Performance Evidence
Current Viability ≠ Long-Term Sustainability
Previously Justified Intelligence ≠ Permanently Justified Intelligence
More Agents ≠ More Continuation Value
Stable Value + Rising Consumption requires renewed justification
Temporary Continuation ≠ Evidence Sufficiency
No Decision to Stop ≠ Decision to Continue
Continue Capability ≠ Continue Current Configuration
Justified Continuation ≠ Justified Scaling
Continuation Not Supported ≠ Immediate Retirement
Governed Continuation State ≠ Continuation Authorization
Automated Continuation Assessment ≠ Automated Authority
97. Cross-Architecture Boundary
CDM governs whether continued operation of an intelligence operating model remains methodologically justified within DGS.
DECM governs Decision Evidence Consolidation.
CDM governs Continuation Decision.
SDM governs Scaling Decision.
MDM governs Migration Decision.
RDM governs Retirement Decision.
VGS governs Value.
XGS governs execution.
IPS governs Intelligence Productivity.
RGS governs resources.
COS governs Computational Optimization.
IES governs Intelligence Execution Economics.
SVGS governs Strategic Value.
ISS governs Intelligence Sustainability.
VAS governs Value Attribution.
HAGA™ governs Harm.
LIGA™ governs Liability.
Relevant Accountability architectures govern Accountability.
VALIDOS™ may govern validation methodology.
ADIT® may govern broader evidence and auditability.
Relevant agent and autonomous-system governance architectures may govern authority, permission, delegation, escalation, and Human Override.
CDM consumes applicable governed states without replacing those domains.
98. Canonical Boundary
CDM begins when DECM has produced a sufficiently usable:
Governed Decision Evidence State
for continuation evaluation.
CDM governs:
Continuation Decision Objects,
continuation boundaries,
continuation horizons,
continuation contexts,
continuation baselines,
Continuation Evidence,
Value persistence,
Value deterioration,
Value transformation,
Productivity Continuation,
Resource Continuation,
Resource Escalation,
Computational Continuation,
Over-Intelligence conditions,
Economic Continuation,
Cost Deterioration,
Strategic Value Continuation,
Strategic Value maturation,
Strategic Milestone Continuation,
Sustainability Continuation,
Sustainability Deterioration,
Dependency Continuation,
Dependency Concentration,
Attribution Continuation,
Human Contribution change,
AI Contribution change,
Multi-Agent Contribution change,
Applicable Constraint Continuation,
Constraint Persistence,
new constraints,
Continuation Evidence Sufficiency,
freshness,
confidence,
uncertainty,
conflict,
trade-offs,
Non-Compensable Continuation Conditions,
Continuation Thresholds,
threshold breaches,
Continuation States,
Conditional Continuation,
Continuation Conditions,
optimization conditions,
reconfiguration conditions,
Evidence Acquisition Conditions,
Dependency Mitigation Conditions,
Cost Reduction Conditions,
Sustainability Improvement Conditions,
Validation Conditions,
monitoring,
triggers,
event-driven review,
periodic review,
Continuation State Expiration,
Continuation Drift,
Silent Continuation,
inertia,
sunk-cost bias,
legacy bias,
success bias,
capability bias,
availability bias,
contractual lock-in,
technical lock-in,
operational dependency,
Continuation Alternatives,
No-Intelligence alternatives,
simpler-mechanism alternatives,
Human alternatives,
alternative configurations,
reversibility,
commitment,
optionality,
and continuation recommendations.
CDM does not independently authorize:
continued operation,
scaling,
migration,
retirement,
legal permission,
organizational approval,
or regulatory approval.
CDM ends when continuation evidence has been represented as a:
Governed Continuation Decision State
for competent authority consideration and, where applicable, subsequent Scaling Decision Governance.
99. Use Case 1 — The AI System Still Works, but Its Cost Has Doubled
An organization operates an AI system that continues producing the same validated operational Value it produced one year earlier.
Technical performance remains stable.
However:
inference cost has doubled,
human correction requirements have increased,
and a smaller model can now produce a comparable validated outcome.
CDM does not treat continued technical operation as sufficient continuation justification.
It evaluates:
Value persistence,
cost deterioration,
resource proportionality,
the simpler-mechanism alternative,
and the feasibility of reconfiguration.
The resulting state may be:
Continue with Reconfiguration
rather than unconditional continuation or immediate retirement.
Current Operation ≠ Justified Continuation of the Current Configuration.
100. Use Case 2 — The Project Has No Immediate ROI but Is Building Strategic Capability
An organization is developing an intelligence capability with weak immediate financial return.
The system has not yet reached commercial-scale economics.
However, governed Strategic Value evidence shows:
material capability development,
validated technical progress,
Strategic Milestones being achieved,
and a credible pathway toward future operational Value.
CDM does not reject continuation solely because immediate ROI remains weak.
It evaluates whether the Strategic Value remains sufficiently evidenced and whether continued investment remains proportionate.
The resulting state may be:
Continue Conditionally
with defined Strategic Milestones, cost limits, and a future reassessment point.
Strategic Value can justify continuation.
But:
Strategic Value must become more evidenced as commitment becomes more committed.
101. Canonical Closing Statement
Continuation should never become the default state of intelligence simply because stopping requires an explicit decision.
A system can remain technically functional after its Value has weakened.
It can remain valuable after its configuration has become inefficient.
It can remain strategically important while immediate economics remain weak.
And it can remain successful while a simpler, cheaper, or more sustainable operating model becomes available.
Continuation therefore requires renewed justification.
Current Operation ≠ Justified Continuation.
Historical Success ≠ Current Continuation Justification.
Historical Investment ≠ Future Value Justification.
Capability ≠ Continuation Justification.
Dependency ≠ Justification by itself.
Justified Continuation ≠ Justified Scaling.
Governed Continuation State ≠ Continuation Authorization.
The purpose of CDM is not to keep intelligence running and not to stop it.
It is to determine whether the evidence still supports keeping it alive under the conditions in which it must now operate.
CDM turns continuation from organizational inertia into a governed requirement for intelligence to keep earning its place in operation.