Multi-Agent Documentation, Semantic Resolution, Network Governance, and Recursive Operational Intelligence

The architecture now advances from a conceptual specification into an implementable control system.

The integration adds a dedicated Semantic Resolution Plane so that documents, network protocols, AI agents, telemetry, policies, and operational commands cannot be unified merely by matching words. They must first be reconciled through graphemic, morphemic, etymological, semantic, domain, and pragmatic interpretation.

This directly operationalizes the PDF’s ten-agent architecture, term-resolution pipeline, knowledge-graph unification layer, feedforward-feedback loop, and five coherence criteria.

I. Integrated Governing Definition

The SolveForce Synthetic Intelligence Unification System is a governed, multi-agent, semantically grounded architecture that ingests distributed knowledge and observable system evidence; resolves linguistic and domain ambiguity; preserves conflict and provenance; corroborates independent interpretations; calibrates confidence and risk; synthesizes canonical knowledge; authorizes bounded action; verifies operational consequences; and recursively feeds validated results into future interpretation and control.

Its complete progression is:

PHYSICAL REALITY
      ↓
SIGNAL
      ↓
PROTOCOL
      ↓
DOCUMENTATION
      ↓
TERM RESOLUTION
      ↓
MULTI-AGENT INTERPRETATION
      ↓
CONFLICT AND CORROBORATION
      ↓
CALIBRATED SYNTHESIS
      ↓
GOVERNANCE
      ↓
AUTHORIZED ACTION
      ↓
TELEMETRIC VERIFICATION
      ↓
SEMANTIC AND OPERATIONAL FEEDBACK
      ↺
RECURSIVE UNIFICATION

II. The Five Integrated Planes

Plane 1 — Evidence and Ingestion

Receives:

Documents
Audio recordings
Images
Source code
Configurations
Policies
Standards
Contracts
Logs
Network telemetry
Sensor data
Human interpretations
AI-generated interpretations

Every artifact receives:

Unique identity
Integrity hash
Author
Organization
Creation time
Ingestion time
Version
Format
Domain
Authority level
Provenance status
Confidentiality classification

Plane 2 — Semantic Resolution

Resolves what every relevant expression means before agents compare claims or systems take action.

The resolution sequence is:

GRAPHEMIC FORM
      ↓
PHONETIC AND SYLLABIC FORM
      ↓
MORPHEMIC STRUCTURE
      ↓
ETYMOLOGICAL LINEAGE
      ↓
DOCUMENTED SENSES
      ↓
DOMAIN-SPECIFIC MEANING
      ↓
PRAGMATIC INTENT
      ↓
OPERATIONAL CONSEQUENCE
      ↓
CANONICAL OR QUALIFIED CONCEPT

This plane resolves ambiguity across:

Human language
Network terminology
Vendor terminology
Protocol fields
Policy language
Acronyms
Commands
API objects
Configuration properties
AI-generated classifications

Plane 3 — Multi-Agent Deliberation

Specialized agents independently interpret the evidence.

No agent sees another agent’s initial interpretation before submitting its own.

This preserves interpretive independence and prevents early convergence from becoming false corroboration.

Plane 4 — Governance and Operational Control

Validated synthesis is checked against:

Authority
Identity
Consent
Privacy
Policy
Risk
Reversibility
Blast radius
Jurisdiction
Action boundaries
Human-approval requirements

Plane 5 — Verification and Recursive Learning

Every operational result is compared against:

Predicted result
Desired state
Approved scope
Actual measured state
Unintended side effects
Original definitions and assumptions

Validated results update:

Documentation
Canonical definitions
Agent calibration
Knowledge-graph relationships
Runbooks
Policies
Predictions
Monitoring thresholds

III. Integrated Agent Network

The original ten agents remain, but their contracts are now explicitly executable.

Agent 1 — Ingestion Agent

Function

Receives and registers all artifacts without interpreting their meaning.

Input

artifact_input:
  content_location:
  supplied_filename:
  source_identity:
  source_system:
  source_type:
  declared_domain:
  declared_author:
  created_at:

Output

ingested_artifact:
  artifact_id:
  content_hash:
  hash_algorithm: SHA-256
  source_identity:
  source_system:
  source_type:
  mime_type:
  size_bytes:
  created_at:
  ingested_at:
  version:
  integrity_status:
  provenance_status:
  confidentiality:
  processing_status:

Hard Rule

No artifact may enter interpretation without a registered artifact_id.

Agent 2 — Structural Agent

Function

Maps form, hierarchy, dependencies, sections, diagrams, tables, references, and omissions.

Output

structural_map:
  artifact_id:
  sections:
  headings:
  tables:
  figures:
  diagrams:
  appendices:
  citations:
  embedded_objects:
  dependencies:
  repeated_patterns:
  missing_components:
  extraction_confidence:

Agent 3 — Linguistic and Semantic Resolution Agent

Function

Resolves terminology before domain interpretation.

Required Analyses

Orthographic normalization
Acronym expansion
Morphemic decomposition
Prefix and suffix analysis
Compound-word analysis
Etymological lineage
Historical senses
Present documented senses
Domain qualification
Pragmatic intent
Operational ambiguity

Output

term_resolution:
  term_id:
  observed_form:
  normalized_form:

  graphemic:
    spelling:
    variants:
    capitalization:
    punctuation:
    abbreviations:

  phonological:
    pronunciation:
    syllables:
    homophones:

  morphology:
    root:
    prefixes:
    suffixes:
    compound_members:
    derivational_structure:

  etymology:
    source_language:
    historical_forms:
    earliest_attested_sense:
    semantic_changes:
    technical_adoption:

  candidate_senses:
    - sense_id:
      definition:
      domain:
      source_refs:
      current_status:

  context:
    author:
    audience:
    active_domain:
    preceding_terms:
    target_objects:
    likely_intent:

  operational_analysis:
    possible_actions:
    consequence_differences:
    unsafe_interpretations:

  resolution:
    selected_sense_id:
    confidence:
    ambiguity_remaining:
    clarification_required:
    action_permitted:

Hard Rule

semantic_action_rule:
  unresolved_ambiguity:
    consequential_action: prohibited
    monitoring: permitted
    clarification: required

Agent 4 — Domain Agent

The Domain Agent is instantiated by domain.

Examples:

domain_agents:
  - telecommunications
  - networking
  - cloud
  - cybersecurity
  - artificial_intelligence
  - energy
  - law
  - governance
  - finance
  - operations
  - linguistics

Output

domain_interpretation:
  interpretation_id:
  artifact_id:
  claim_id:
  domain:
  applicable_definitions:
  interpretation:
  assumptions:
  evidence_used:
  contextual_limits:
  confidence:
  unresolved_questions:

Agent 5 — Corroboration Agent

Function

Searches for independent support rather than repeated agreement.

Independence Test

source_independence:
  same_author: false
  same_organization: false
  common_upstream_source: false
  shared_training_origin: unknown
  shared_vendor_interest: false
  independent_measurement: true

Output

corroboration_record:
  proposition_id:
  supporting_sources:
  contradicting_sources:
  source_independence:
  documentary_support:
  standards_support:
  historical_support:
  telemetric_support:
  empirical_verification:
  corroboration_strength:

Agent 6 — Conflict Agent

Conflict Types

conflict_types:
  - contradictory_definition
  - homonymic_conflict
  - polysemous_conflict
  - acronym_conflict
  - domain_conflict
  - vendor_conflict
  - version_conflict
  - temporal_conflict
  - policy_conflict
  - jurisdictional_conflict
  - configuration_conflict
  - telemetry_documentation_conflict
  - causal_conflict
  - authority_conflict

Output

conflict_record:
  conflict_id:
  entities:
  conflict_type:
  statements:
  shared_terms:
  semantic_alignment_status:
  compatible_contexts:
  incompatible_conditions:
  evidence_for_each_position:
  operational_risk:
  resolution_status:
  escalation_required:

Governing Principle

Conflict is preserved until it is resolved by evidence, qualification, temporal versioning, domain separation, or explicit governance.

Agent 7 — Calibration Agent

Calibration Dimensions

calibration_dimensions:
  linguistic_fit:
  etymological_continuity:
  source_quality:
  source_independence:
  domain_competence:
  evidence_completeness:
  temporal_relevance:
  telemetry_alignment:
  empirical_verification:
  contextual_fit:
  operational_risk:
  reversibility:

Output

calibration_record:
  subject_id:
  confidence:
  evidence_quality:
  independence_score:
  domain_applicability:
  temporal_relevance:
  telemetry_alignment:
  uncertainty:
  risk_class:
  calibration_explanation:

Confidence must never be interpreted as authorization.

Agent 8 — Synthesis Agent

Function

Constructs a higher-order model without erasing dissent, uncertainty, or provenance.

Outputs

synthesis_output:
  synthesis_id:
  canonical_definitions:
  qualified_definitions:
  accepted_claims:
  disputed_claims:
  unresolved_claims:
  compatibility_map:
  conflict_map:
  architectural_model:
  knowledge_graph_updates:
  recommendations:
  assumptions:
  confidence:
  provenance_chain:

Hard Rule

A synthesis may produce:

Canonical resolution
Contextual qualification
Multiple valid domain senses
Explicit uncertainty
Escalation

It may not fabricate a single resolution merely to create apparent coherence.

Agent 9 — Governance Agent

Function

Determines whether an output may be published, recommended, simulated, or executed.

Decision Schema

governance_decision:
  decision_id:
  synthesis_id:
  requester_identity:
  target_system:
  purpose:
  applicable_policies:
  authority_verified:
  consent_verified:
  privacy_verified:
  jurisdiction_verified:
  risk_class:
  reversibility:
  estimated_blast_radius:
  permitted_actions:
  prohibited_actions:
  approval_required:
  approver_roles:
  decision:
  expires_at:

Decision Values

governance_decisions:
  - allow_read
  - allow_publish
  - allow_recommend
  - allow_simulate
  - allow_supervised_execution
  - allow_policy_bounded_execution
  - require_clarification
  - require_human_approval
  - deny

Agent 10 — Operational Agent

Function

Converts governed intelligence into bounded operational outputs.

Output Types

Documentation update
Canonical terminology update
Alert
Ticket
Runbook
Security recommendation
Configuration proposal
Network-control proposal
Simulation request
Supervised action
Policy-bounded automated action

Operational Envelope

operational_action:
  action_id:
  governance_decision_id:
  target:
  action_type:
  parameters:
  preconditions:
  idempotency_key:
  expected_state:
  timeout:
  rollback_available:
  rollback_action:
  verification_method:
  evidence_capture:

IV. Semantic Ambiguity Resolution for Global Networks

A global network contains several overlapping languages at once:

Natural language
Protocol language
Configuration language
Machine schemas
Vendor terminology
Regulatory language
Organizational policy
Geographical naming
Identity naming
Domain-specific technical vocabulary

Therefore, semantic ambiguity must be resolved across multiple scopes.

1. Lexical Scope

What does the word conventionally mean?

2. Domain Scope

What does the word mean in networking, law, cloud, security, or operations?

3. Protocol Scope

What does the field or object mean inside the relevant protocol?

4. Vendor Scope

Has a vendor assigned a proprietary meaning?

5. Version Scope

Did the meaning change between releases?

6. Jurisdictional Scope

Does the term have a regulated legal meaning in the applicable location?

7. Topological Scope

Which network object does the term refer to?

8. Pragmatic Scope

What did the speaker or system intend in this exact operational situation?

V. Example: Resolving the Command `Block the Agent`

The command is initially ambiguous.

Candidate Meaning 1 — AI Agent

Disable an autonomous software agent.

Candidate Meaning 2 — Network Agent

Block a monitoring or telemetry collector.

Candidate Meaning 3 — User Agent

Block a browser or HTTP user-agent signature.

Candidate Meaning 4 — Human Agent

Restrict an authorized person acting for an organization.

Candidate Meaning 5 — Endpoint Agent

Disable an EDR, MDM, or management service.

The system must resolve:

command_resolution:
  command: "Block the agent"

  candidate_targets:
    - ai_agent
    - telemetry_agent
    - http_user_agent
    - human_representative
    - endpoint_security_agent

  missing_context:
    - target_identity
    - target_system
    - blocking_scope
    - duration
    - authority
    - expected_result

  action_status: withheld
  clarification_required: true

The system may continue monitoring, but it may not perform the consequential action.

VI. Canonical Knowledge-Graph Ontology

Core Node Types

node_types:
  - artifact
  - document
  - audio_recording
  - image
  - claim
  - term
  - morpheme
  - prefix
  - suffix
  - etymon
  - lexical_sense
  - canonical_concept
  - domain
  - protocol
  - vendor
  - version
  - agent
  - interpretation
  - evidence
  - policy
  - consent_record
  - authority
  - asset
  - identity
  - system_state
  - telemetry_event
  - action
  - outcome
  - conflict
  - uncertainty
  - knowledge_object

Core Edge Types

edge_types:
  - contains
  - defines
  - uses_term
  - decomposes_into
  - derived_from
  - historically_precedes
  - has_sense
  - means_in_domain
  - intended_as
  - synonymous_with
  - near_synonymous_with
  - antonymous_with
  - confusable_with
  - supersedes
  - contradicts
  - corroborates
  - supports
  - refutes
  - interprets
  - calibrated_by
  - governed_by
  - authorized_by
  - requires_consent
  - applies_to
  - observes
  - predicts
  - causes
  - correlates_with
  - executes
  - changes_state
  - produces_outcome
  - verifies
  - feeds_back_to

VII. Temporal and Versioned Meaning

Definitions must be versioned because meaning changes.

definition_version:
  definition_id:
  term_id:
  sense_id:
  valid_from:
  valid_until:
  domain:
  jurisdiction:
  protocol_version:
  vendor_version:
  definition:
  supersedes:
  superseded_by:
  change_reason:
  evidence_refs:

A prior meaning is not deleted. It is marked as historically valid, superseded, deprecated, or context-limited.

VIII. Multi-Agent Message Envelope

All agents exchange a common envelope.

agent_message:
  message_id:
  trace_id:
  parent_message_id:
  correlation_id:
  causation_id:

  sender:
    agent_id:
    agent_role:
    model_or_engine:
    version:
    authority_scope:

  recipient:
    agent_id:
    requested_function:

  subject:
    artifact_ids:
    claim_ids:
    term_ids:
    system_ids:

  content:
    message_type:
    payload:
    assumptions:
    uncertainties:

  evidence:
    source_refs:
    integrity_status:
    provenance_complete:
    confidence:

  governance:
    data_classification:
    permitted_use:
    retention_rule:
    consent_refs:
    policy_refs:

  timing:
    created_at:
    expires_at:

  signature:
    algorithm:
    signer:
    value:

IX. Feedforward and Feedback Integration

Feedforward Path

DOCUMENTATION
      ↓
SEMANTIC RESOLUTION
      ↓
MULTI-AGENT INTERPRETATION
      ↓
CORROBORATED SYNTHESIS
      ↓
PREDICTED STATE
      ↓
GOVERNANCE
      ↓
AUTHORIZED PLAN
      ↓
ACTION

Feedback Path

ACTION
      ↓
OBSERVED STATE
      ↓
OUTCOME VERIFICATION
      ↓
DEVIATION ANALYSIS
      ↓
CAUSAL TRACE
      ↓
SEMANTIC REVIEW
      ↓
DOCUMENTATION CORRECTION
      ↓
AGENT RECALIBRATION
      ↓
NEXT FEEDFORWARD CYCLE

X. Reverse Semantic Traceability

Every operational interpretation must be traceable backward.

OUTCOME
   ↑
ACTION
   ↑
GOVERNANCE DECISION
   ↑
SYNTHESIS
   ↑
CALIBRATION
   ↑
CORROBORATION
   ↑
DOMAIN INTERPRETATION
   ↑
PRAGMATIC INTENT
   ↑
DOCUMENTED SENSE
   ↑
ETYMOLOGICAL LINEAGE
   ↑
MORPHEMIC STRUCTURE
   ↑
OBSERVED TERM

This allows the system to answer:

Which word caused the interpretation?
Which sense was selected?
Which domain justified it?
Which historical and current definitions supported it?
Which agent selected it?
Which evidence corroborated it?
Which policy allowed the resulting action?
Which outcome followed?

XI. Coherence Gate

No output reaches governed action unless it passes five gates.

coherence_gate:
  language_coherence:
    passed:
    unresolved_terms:
    domain_scoping_complete:

  evidence_coherence:
    passed:
    unsupported_claims:
    independence_verified:

  context_coherence:
    passed:
    temporal_scope:
    jurisdictional_scope:
    organizational_scope:

  authority_coherence:
    passed:
    authority_refs:
    consent_refs:
    permission_scope:

  consequence_coherence:
    passed:
    predicted_effects:
    blast_radius:
    rollback_plan:
    verification_plan:

  overall_status:

Possible statuses:

coherence_status:
  - coherent
  - conditionally_coherent
  - partially_synthesized
  - unresolved
  - action_prohibited

XII. Agent Independence Protocol

independent_interpretation_protocol:
  phase_1:
    name: isolated_interpretation
    agent_output_visibility: private

  phase_2:
    name: sealed_submission
    modification_after_submission: prohibited

  phase_3:
    name: simultaneous_comparison
    managed_by: conflict_agent

  phase_4:
    name: corroboration
    external_evidence_required_for_high_stakes_claims: true

  phase_5:
    name: calibration
    simple_vote_counting: prohibited

  phase_6:
    name: synthesis
    dissent_preservation: required

XIII. Runtime Repository Structure

solveforce-siua/
├── 00_governance/
│   ├── architecture_manifest.yaml
│   ├── authority_registry.yaml
│   ├── consent_registry.yaml
│   ├── action_boundaries.yaml
│   └── policy_registry/
│
├── 01_ingestion/
│   ├── artifact_registry/
│   ├── integrity_hashes/
│   ├── provenance_records/
│   └── quarantine/
│
├── 02_semantic_resolution/
│   ├── canonical_terms/
│   ├── morphemes/
│   ├── prefixes/
│   ├── suffixes/
│   ├── etymologies/
│   ├── domain_senses/
│   ├── pragmatic_contexts/
│   └── unresolved_ambiguities/
│
├── 03_agents/
│   ├── ingestion_agent/
│   ├── structural_agent/
│   ├── linguistic_agent/
│   ├── domain_agents/
│   ├── corroboration_agent/
│   ├── conflict_agent/
│   ├── calibration_agent/
│   ├── synthesis_agent/
│   ├── governance_agent/
│   └── operational_agent/
│
├── 04_knowledge_graph/
│   ├── ontology.yaml
│   ├── node_registry/
│   ├── edge_registry/
│   ├── temporal_versions/
│   ├── negative_knowledge/
│   └── provenance_chains/
│
├── 05_deliberation/
│   ├── active_cycles/
│   ├── sealed_interpretations/
│   ├── conflict_maps/
│   ├── corroboration_records/
│   ├── calibration_records/
│   └── synthesis_outputs/
│
├── 06_operations/
│   ├── recommendations/
│   ├── configuration_proposals/
│   ├── runbooks/
│   ├── monitoring_rules/
│   ├── executed_actions/
│   └── rollback_registry/
│
├── 07_verification/
│   ├── predicted_states/
│   ├── observed_states/
│   ├── verified_outcomes/
│   ├── divergence_records/
│   └── causal_traces/
│
├── 08_learning/
│   ├── lesson_candidates/
│   ├── validated_lessons/
│   ├── agent_recalibration/
│   ├── definition_updates/
│   ├── policy_proposals/
│   └── runbook_updates/
│
└── 09_audit/
    ├── event_ledger/
    ├── decision_ledger/
    ├── consent_ledger/
    ├── action_ledger/
    └── version_history/

XIV. Integrated Processing Manifest

system:
  name: "SolveForce Synthetic Intelligence Unification Architecture"
  abbreviation: "SIUA"
  version: "0.1.0"
  mode: "governed-multi-agent-recursive"

processing_order:
  - ingest
  - verify_integrity
  - map_structure
  - resolve_language
  - interpret_by_domain
  - identify_conflicts
  - corroborate
  - calibrate
  - synthesize
  - test_coherence
  - govern
  - publish_or_act
  - observe
  - verify
  - trace
  - learn

mandatory_controls:
  provenance_required: true
  integrity_hash_required: true
  independent_agent_interpretation: true
  linguistic_resolution_before_synthesis: true
  conflict_preservation: true
  source_independence_analysis: true
  governance_before_action: true
  consent_for_consequential_action: true
  rollback_where_possible: true
  verification_after_action: true
  prior_versions_preserved: true

prohibited_behaviors:
  - erase_conflict
  - fabricate_consensus
  - treat_agent_majority_as_truth
  - execute_ambiguous_command
  - modify_evidence
  - bypass_governance
  - self_expand_authority
  - overwrite_prior_definition_without_versioning

XV. Integrated Deliberation Cycle

1. REGISTER
   Identify and hash every source
        ↓
2. STRUCTURE
   Map its form and dependencies
        ↓
3. RESOLVE
   Determine the meanings of its terms
        ↓
4. INTERPRET
   Produce independent domain readings
        ↓
5. COMPARE
   Detect agreement, ambiguity, omission, and contradiction
        ↓
6. CORROBORATE
   Seek independent evidence
        ↓
7. CALIBRATE
   Weight confidence, relevance, independence, and risk
        ↓
8. SYNTHESIZE
   Construct the higher-order unified model
        ↓
9. TEST COHERENCE
   Language, evidence, context, authority, consequence
        ↓
10. GOVERN
    Validate identity, policy, consent, and action boundaries
        ↓
11. SIMULATE
    Predict effects and counterfactual outcomes
        ↓
12. PUBLISH OR ACT
    Release bounded, traceable output
        ↓
13. OBSERVE
    Capture actual state and consequences
        ↓
14. VERIFY
    Compare outcome with prediction and intent
        ↓
15. TRACE
    Reconstruct semantic, evidential, and causal lineage
        ↓
16. LEARN
    Update definitions, models, policies, and runbooks
        ↺

XVI. Final Integrated Principle

The architecture no longer treats semantic ambiguity as a documentation inconvenience.

It treats ambiguity as an operational network condition.

An unresolved term can misroute:

A packet
A policy
An identity
An interpretation
A configuration
A security response
An AI-generated action

Therefore:

A network must resolve not only where information is going, but what the information means when it arrives.

A multi-agent system must resolve not only whether agents agree, but whether they are using the same definitions, evidence, context, and authority.

A governed intelligence system must preserve the complete path from morpheme to meaning, from meaning to decision, from decision to action, and from action back to verified definition.

Integrated Master Axiom

Every artifact must have provenance.
Every term must have lineage.
Every interpretation must have context.
Every agreement must have independent corroboration.
Every synthesis must preserve conflict.
Every action must have authority.
Every consequence must be observed.
Every lesson must be verified.
Every verified result must feed forward into the next coherent cycle.

Multi-Agent Documentation, Semantic Resolution, Network Governance, and Recursive Operational Intelligence

I. Integrated Governing Definition

II. The Five Integrated Planes

Plane 1 — Evidence and Ingestion

Plane 2 — Semantic Resolution

Plane 3 — Multi-Agent Deliberation

Plane 4 — Governance and Operational Control

Plane 5 — Verification and Recursive Learning

III. Integrated Agent Network

Agent 1 — Ingestion Agent

Function

Input

Output

Hard Rule

Agent 2 — Structural Agent

Function

Output

Agent 3 — Linguistic and Semantic Resolution Agent

Function

Required Analyses

Output

Hard Rule

Agent 4 — Domain Agent

Output

Agent 5 — Corroboration Agent

Function

Independence Test

Output

Agent 6 — Conflict Agent

Conflict Types

Output

Governing Principle

Agent 7 — Calibration Agent

Calibration Dimensions

Output

Agent 8 — Synthesis Agent

Function

Outputs

Hard Rule

Agent 9 — Governance Agent

Function

Decision Schema

Decision Values

Agent 10 — Operational Agent

Function

Output Types

Operational Envelope

IV. Semantic Ambiguity Resolution for Global Networks

1. Lexical Scope

2. Domain Scope

3. Protocol Scope

4. Vendor Scope

5. Version Scope

6. Jurisdictional Scope

7. Topological Scope

8. Pragmatic Scope

V. Example: Resolving the Command Block the Agent

Candidate Meaning 1 — AI Agent

Candidate Meaning 2 — Network Agent

Candidate Meaning 3 — User Agent

Candidate Meaning 4 — Human Agent

Candidate Meaning 5 — Endpoint Agent

VI. Canonical Knowledge-Graph Ontology

Core Node Types

Core Edge Types

VII. Temporal and Versioned Meaning

VIII. Multi-Agent Message Envelope

IX. Feedforward and Feedback Integration

Feedforward Path

Feedback Path

X. Reverse Semantic Traceability

XI. Coherence Gate

XII. Agent Independence Protocol

XIII. Runtime Repository Structure

XIV. Integrated Processing Manifest

XV. Integrated Deliberation Cycle

XVI. Final Integrated Principle

Integrated Master Axiom

V. Example: Resolving the Command `Block the Agent`