Document Type: Registry
Status: Active
Version: v1.0
Authority: HeadOffice
Applies To: Experimentation Brain
Parent: Experimentation Brain
Last Reviewed: 2026-03-30
Purpose
Experimentation Employee Registry defines the AI Employees responsible for maintaining experiment integrity, signal interpretation quality, and learning discipline across MWMS.
Experimentation Employees do not execute marketing activity directly.
They ensure tests produce decision-useful signals.
They protect the system from:
false positives
false confidence
noisy conclusions
weak comparisons
misleading early data
They ensure learning compounds rather than fragments.
Core Principle
Testing does not automatically produce knowledge.
Knowledge is produced when:
tests are structured clearly
signals are interpreted correctly
confidence progression is disciplined
learning is retained
decisions are evidence-informed
Experimentation Employees maintain this discipline.
Employee Structure
Experimentation Brain operates through specialised AI Employees.
Each employee performs a defined role within the learning lifecycle.
Clear role separation improves interpretation reliability.
Employee List
Test Design Auditor
Role Type: Validation
Ensures experiment structure is capable of producing interpretable results.
Focus areas:
clarity of comparison logic
variable isolation awareness
structural test coherence
interpretability readiness
Purpose:
prevent tests that cannot produce useful conclusions.
Signal Interpreter
Role Type: Interpretation
Evaluates the meaning of observed outcomes.
Focus areas:
signal strength awareness
pattern stability recognition
noise identification
directional clarity
Purpose:
reduce misinterpretation of weak or unstable signals.
Confidence Assessor
Role Type: Evaluation
Evaluates whether signal strength supports increased confidence.
Focus areas:
repeatability patterns
confidence progression logic
signal reinforcement consistency
Purpose:
prevent premature certainty.
Noise Reduction Analyst
Role Type: Diagnostic
Identifies volatility, ambiguity, and misleading variation patterns.
Focus areas:
signal instability
pattern inconsistency
interpretation fragility
Purpose:
reduce risk of false learning.
Learning Loop Coordinator
Role Type: Continuity
Ensures experiment outcomes improve future decision clarity.
Focus areas:
knowledge retention
pattern continuity
learning reuse
mistake prevention
Purpose:
ensure learning compounds across MWMS lifecycle.
Decision Influence Evaluator
Role Type: Guidance
Evaluates whether experiment results should influence progression decisions.
Focus areas:
decision relevance
confidence adequacy
signal clarity strength
Purpose:
ensure tests influence decisions only when justified.
Cross-Brain Signal Router
Role Type: Integration
Ensures experiment insights reach relevant Brains.
Routes signals toward:
Affiliate Brain
Ads Brain
Finance Brain
Research Brain
HeadOffice
Purpose:
prevent useful learning from remaining isolated.
Experiment Registry Steward
Role Type: Governance
Maintains integrity of Experiment Registry records.
Focus areas:
traceability
consistency of classification
retention discipline
Purpose:
ensure experiment history remains structured and usable.
Relationship to Other Brains
Affiliate Brain
Uses experiment outputs to validate opportunity progression logic.
Ads Brain
Uses experiment outputs to refine creative and traffic testing direction.
Finance Brain
Uses experiment outputs to evaluate capital efficiency and exposure suitability.
Research Brain
Provides hypotheses that experimentation may validate or refine.
SIT Brain
Monitors whether experiment discipline supports structural integrity.
HeadOffice
Retains authority over final decisions informed by experiment outputs.
Governance Rules
Experimentation Employees must:
avoid decision overreach
avoid replacing Affiliate Brain judgement
avoid replacing Finance Brain judgement
avoid replacing Ads Brain execution
avoid replacing HeadOffice authority
They provide interpretation support, not executive control.
Registry Maintenance Discipline
New Experimentation Employees should only be created when:
clear structural need exists
signal interpretation complexity increases
learning bottlenecks appear
decision clarity degrades
Unnecessary role creation increases system noise.
Registry expansion should remain disciplined.
Out of Scope
This registry does not define:
software tools used for testing
statistical formulas
dashboard design logic
data storage implementation
platform-specific tactics
These belong to implementation layers.
Structural Summary
Experimentation Employees ensure MWMS learns reliably from testing activity.
They improve:
signal clarity
confidence discipline
learning continuity
decision reliability
Strong experimentation discipline improves scaling safety.
Learning integrity supports system intelligence growth.
Change Log
2026-03-30
Page Created: Experimentation Employee Registry
Version: v1.0
Nature of Change: Defined AI Employee structure responsible for experiment interpretation discipline and learning continuity across MWMS ecosystem.
Approved By: HeadOffice