MWMS AI Workforce Governance Model

System: MWMS
Document Type: Governance Model
Authority Level: MCR Source Of Truth
Status: Draft For MCR
Version: v1.1
Primary Location: MCR
Future Operational Destination: HeadOffice Brain, MWMS Brain, Brain Room, AI Manager, AI Employee Router, Task Executor Systems, Newsletter Intelligence, Course Absorption System, Opportunity System, Automation Brain, AIBS Brain
Parent Page: HeadOffice
Owner: Martyn
Developer Boundary: Do Not Touch M’s Active Build Areas Unless Specifically Assigned
Source Of Truth: MCR
Last Reviewed: 2026-06-18
Source / Origin: MWMS AI Workforce Governance Model v1.0 + AI Automations by Jack — Multi-Agent Orchestration, Model Routing, Independent Review, Rescue Routing, Persistent Agents, External Knowledge, Tool Governance And Session Continuity Block
MWMS Classification: AI Workforce Governance Model / AI Employee Lifecycle Governance / Multi-Agent Authority Framework / AI Workforce Control Layer
Primary Brain: HeadOffice Brain
Supporting Brains: MWMS Brain, Automation Brain, Operations Brain, Data Brain, Risk Brain, Compliance Brain, SIT Brain, AIBS Brain
Related Pages: MWMS AI Agent Operations Core, MWMS AI Employee Role Card Standard, MWMS AI Employee Capability Stack Framework, MWMS AI Multi Agent Role Design Framework, MWMS AI Agent Orchestration Framework, MWMS Agentic Work Unit Standard, MWMS AI Workflow Pipeline Standard, MWMS AI Plugin Orchestration Framework, MWMS AI Exchange Zone And Dependency Control Framework, MWMS AI Output Validation Standard, MWMS AI Agent Failure Handling And Escalation Protocol, MWMS AI Ambiguity And Partial Failure Containment Framework, MWMS AI Agent Outcome Measurement Framework, MWMS AI Agent Deployment Readiness Checklist, MWMS AI Tool Permission And Access Framework, MWMS AI Agent Memory And Context Framework, MWMS AI Observability Metadata Standard
Source Evidence: The existing model defines how MWMS governs its complete AI workforce across ownership, employee creation, roles, capability stacks, tool permissions, deployment, validation, failure handling, outcomes, improvement and retirement. The absorbed material strengthens this model with model-route governance, independent review, Rescue Agents, persistent-agent controls, external-knowledge separation, execution verification, cost visibility, knowledge commitment, deployment suspension and session continuity.

Purpose

The purpose of this document is to define the MWMS AI Workforce Governance Model.

This governance model establishes how MWMS manages the complete AI workforce across:

Brains
AI Employees
specialist agents
multi-agent role chains
models
skills
capabilities
tools
permissions
workflows
Work Units
memory
validation
reporting
handoffs
failure handling
Rescue Routing
persistent operation
outcome measurement
durable knowledge
future client-facing AIBS systems

MWMS is not being built as one general AI assistant.

MWMS is being built as a governed AI workforce.

That workforce may eventually contain many AI Employees with different:

roles
authorities
capabilities
model requirements
tool permissions
responsibilities
outputs
risks
review requirements
lifecycle states

Without governance, the AI workforce could become:

scattered
duplicated
over-permissioned
unsafe
difficult to measure
difficult to stop
expensive
over-automated
disconnected from business outcomes

This model defines how HeadOffice governs that workforce so MWMS can scale without losing structure, trust, authority or business direction.

Scope

This governance model applies to all:

AI Employees
AI agents
model routes
multi-agent workflows
persistent agents
AI-assisted systems
task pipelines
reports
tool access
memory systems
knowledge systems
handoffs
dashboards
validation systems
Rescue Agents
future AIBS client systems

This includes:

HeadOffice Brain
MWMS Brain
Brain Room
AI Manager
AI Employee Router
Task Executor systems
Dev Console
Newsletter Intelligence
Course Absorption
Opportunity System
Affiliate Brain
Research Brain
Experimentation Brain
Finance Brain
Content Brain
Ads Brain
Data Brain
Strategy Brain
Operations Brain
Automation Brain
Risk Brain
Compliance Brain
SIT Brain
AIBS Brain
Supabase task and event systems
WordPress Brain sites
Make.com and n8n workflows
future AIBS client systems

This governance model applies before, during and after deployment.

It covers the full lifecycle:

Need Identified
→ Proposed
→ Designed
→ Defined
→ Tested
→ Deployment Reviewed
→ Operational
→ Measured
→ Improved
→ Expanded, Limited, Suspended, Merged, Parked Or Retired

Core Definition

The MWMS AI Workforce is the complete collection of AI Employees, specialist agents, workflow roles, validators, reviewers, routers, planners, builders, tool operators, Rescue Agents, persistent monitors, outcome loggers and future client-facing AI roles that support or perform work inside MWMS.

The MWMS AI Workforce Governance Model determines:

who owns workforce governance
how AI Employees are proposed
how roles are approved
how authority is assigned
how models are selected
how capability stacks are governed
how skills are assigned
how tool permissions are controlled
how workflows are designed
how deployments are approved
how output is reviewed and validated
how failures are contained
how repeated failure triggers rescue
how persistent agents remain stoppable
how performance is measured
how knowledge is committed
how capability is expanded
how employees are limited, suspended, merged or retired
how future client systems remain safe

This model turns AI Employees from scattered ideas into governed operational assets.

Core Principle

The core principle of this governance model is:

MWMS AI Employees must be governed as a workforce, not treated as isolated prompts, tools, models or automations.

Every formal AI Employee must have:

an Owning Brain
a Role Card
an authority level
a capability stack
assigned skills
an approved model or capability route
a tool-permission boundary
a workflow position
input requirements
output requirements
validation requirements
handoff destinations
failure rules
Rescue Routing
outcome expectations
review requirements
lifecycle status
closure requirements

This protects MWMS from AI workforce sprawl and uncontrolled authority.

AI Workforce Governance Principles

MWMS applies the following principles.

Principle 1 — Operational Need Before Employee Creation

AI Employees exist to solve repeated operational needs.

Principle 2 — One Owning Brain

Every AI Employee has one primary Owning Brain.

Principle 3 — Authority Must Be Explicit

Technical capability does not automatically grant operational authority.

Principle 4 — Least Capability Required

Assign only the capabilities required for the role.

Principle 5 — Least Permission Required

Grant the lowest tool-access level that safely supports the task.

Principle 6 — Separation Of Duties

High-risk work should separate planning, production, review, validation, approval and execution.

Principle 7 — Human Authority Remains Where Required

High-risk decisions remain subject to human approval.

Principle 8 — Manual Proof Before Automation

Workflows must demonstrate value and control before automation expands.

Principle 9 — Outcomes Before Scale

AI Employees must produce verified outcomes, not merely more output.

Principle 10 — Workforce Size Must Remain Justified

More AI Employees do not automatically create more business value.

AI Workforce Governance Layers

The MWMS AI Workforce Governance Model uses fifteen governance layers.

Workforce Ownership
Employee Creation
Role And Responsibility
Authority
Capability And Skill
Model Route
Tool Permission
Workflow And Deployment
Validation And Independent Review
Exchange Zone And Dependency Control
Failure, Escalation And Rescue
Persistent Agent Governance
Outcome, Cost And Performance
Knowledge Commitment And Continuity
Improvement, Suspension And Retirement
Workforce Ownership Governance

HeadOffice owns AI workforce governance.

Individual Brains may own specific AI Employees.

Examples:

Affiliate Brain may own:

Offer Evaluation Agent
Funnel Analysis Agent
Campaign Review Agent

Research Brain may own:

Evidence Collection Agent
Source Reliability Agent
Market Signal Agent

Finance Brain may own:

Break-Even Analysis Agent
Budget Risk Agent
Scenario Planning Agent

HeadOffice governs whether these AI Employees are:

necessary
correctly defined
permissioned
safe
measurable
aligned with MWMS

Workforce Ownership Rule

Brains may own AI Employees.

HeadOffice governs the workforce.

Employee Creation Governance

AI Employees must not be created casually.

Valid reasons include:

repeated task type
workflow requires role separation
specialisation materially improves quality
independent validation is required
a handoff needs clear ownership
risk requires a dedicated control role
future AIBS packaging requires a defined role

Invalid reasons include:

the name sounds impressive
a course mentioned it
the model can technically do it
the tool vendor promoted it
one task occurred once
the role duplicates an existing Employee

Employee Creation Rule

Create AI Employees to solve repeated operational needs, not to collect agent names.

Role And Responsibility Governance

Every formal AI Employee requires a Role Card.

The Role Card must define:

Employee Name
Owning Brain
Authority Level
Purpose
Primary Responsibilities
Non-Responsibilities
Input Types
Required Context
Output Types
Output Standard
Approved Skills
Approved Tools
Forbidden Actions
Workflow Position
Handoff Destinations
Escalation Rules
Validation Rules
Success Metrics
Failure Modes
Human Review Requirement
Logging Requirement
Closure Requirement

Where roles overlap, HeadOffice must decide whether to:

merge
split
clarify
subordinate one role
park
reject
retire

Role Governance Rule

No AI Employee should perform undefined or materially duplicated work.

Authority Governance

Every AI Employee must have an explicit authority level.

Authority levels include:

Observe

May inspect, retrieve and report.

Recommend

May analyse and recommend but cannot approve.

Draft

May create structured drafts.

Validate

May issue a formal readiness or quality verdict.

Route

May route approved work inside defined rules.

Approve

May approve only within explicitly delegated scope.

Execute

May perform approved actions.

Commit

May write validated learning to an approved durable destination.

Authority Governance Rule

No AI Employee may assume a higher authority level merely because it has the technical ability.

Capability And Skill Governance

Each AI Employee must have a capability stack matching its role.

The capability stack may include:

Role Capability
Input Capability
Context Capability
Reasoning Capability
Research Capability
Model Capability
Tool Capability
Workflow Capability
Output Capability
Validation Capability
Handoff Capability
Escalation Capability
Learning Capability
Outcome Capability
Forbidden Capabilities

Skills define how capabilities are used.

Examples:

Course Absorption Value Extraction Skill
Newsletter Signal Filtering Skill
MCR Duplicate Risk Check Skill
Developer Handoff Precision Skill
Source Authority Verification Skill

Capability Governance Rule

Capability expansion is a governed system change.

It is not a casual prompt adjustment.

Model Route Governance

AI Employees may require different models or capability routes.

Model selection should consider:

task complexity
reasoning depth
context length
multimodal requirements
privacy
cost
speed
coding requirements
independence
fallback requirements

Possible model routes include:

low-cost extraction model
advanced reasoning model
multimodal model
local private model
independent review model
deterministic validator
Rescue Agent model route

Model Route Governance Rule

Use the model route that fits the role, risk, privacy and outcome.

Do not use one default model for every job.

Tool Permission Governance

Tool access must be governed separately from role capability.

Tool permission levels include:

No Tool Access
Provided Input Only
Read Only Reference Access
Draft Creation Access
Controlled Write Access
Supervised External Action Access
Restricted Low-Risk Autonomous Access

High-risk tools include:

WordPress write access
production database write access
Gmail send
Google Ads changes
paid traffic platforms
finance systems
client systems
live code
schema changes
publishing systems

Tool Permission Governance Rule

Grant the lowest permission level that allows the task to be completed safely.

Workflow And Deployment Governance

AI Employees and workflows must pass readiness review before becoming operational.

Deployment levels include:

Concept Only
Draft Ready
Manual Operation Ready
Assisted Automation Ready
Controlled Automation Ready
Restricted Autonomous Operation Ready
Deployment Suspended

Before deployment, HeadOffice must confirm:

Role Card exists
authority is clear
capability stack exists
Work Unit structure exists
pipeline exists
model route exists
tool permissions exist
validation exists
handoff exists
dependencies are controlled
failure handling exists
Rescue Route exists
logging exists
outcome measurement exists
human review exists
shutdown exists where needed

Deployment Governance Rule

Manual discipline first.

Automation second.

Validation And Independent Review Governance

AI output cannot be trusted automatically.

Validation governance defines:

validation level
validation owner
source-verification requirement
Brain-routing requirement
compliance requirement
developer-review requirement
human-review requirement
final readiness verdict

Independent review is required where output affects:

MCR
finance
compliance
security
live systems
client delivery
public output
disputed evidence
repeated failure

Validation Governance Rule

AI output becomes trusted only after it passes the required review and validation level.

Exchange Zone And Dependency Governance

All important transitions must use controlled Exchange Zones.

Exchange Zone governance requires:

sender
receiver
Work Unit ID
source
output transferred
completed work
unresolved work
validation status
risk
dependencies
failure history
next action
next owner
receiver acceptance

Dependency types may include:

input
context
source authority
validation
independent review
approval
tool permission
sequence
risk
failure-state
outcome
knowledge commitment

Exchange Zone Governance Rule

Work must not move forward unless the receiver has what it needs to continue safely.

Failure, Escalation And Rescue Governance

Failure must be treated as part of the system.

Failure categories include:

input failure
source failure
classification failure
Brain-routing failure
role-boundary failure
model-route failure
tool-permission failure
execution failure
output-quality failure
validation failure
handoff failure
outcome failure
governance failure

The standard Rescue Threshold is:

Two materially identical failures without verified progress.

At the threshold:

stop the original route
freeze the Work Unit
preserve source and attempt history
create a Rescue Packet
assign a materially different route
define a new verification gate

Failure Governance Rule

Failure must be detected, contained, escalated, logged and converted into system learning.

Persistent Agent Governance

Persistent and scheduled AI Employees require additional controls.

Required controls include:

owner
approved purpose
source
trigger or schedule
baseline
cost limit
duplicate suppression
failure threshold
stale-context detection
last-good state
alert route
pause control
shutdown control
restart approval
human exception review

Persistent Agent Governance Rule

No persistent AI Employee may operate indefinitely without an owner, cost boundary, failure threshold and shutdown control.

Outcome, Cost And Performance Governance

AI Employees must be measured by outcomes, not output volume.

Outcome categories include:

Decision Outcome
Action Outcome
Risk Reduction Outcome
Time Saving Outcome
Quality Improvement Outcome
Revenue Support Outcome
Learning Outcome
System Reliability Outcome
Client Value Outcome

Performance governance asks:

Did the Employee create useful work?
Did it reduce risk?
Did it save time?
Did it improve a decision?
Did it route work correctly?
Did it create learning?
Did it create noise?
Did the outcome justify the cost?

Cost may include:

model use
API use
tool subscriptions
automation platform
human review
retries
rework
developer time

Performance Governance Rule

AI Employees must justify their place through useful, proportionate and verified outcomes.

Knowledge Commitment And Continuity Governance

AI Employees must not commit unverified output as durable knowledge.

Before knowledge commitment, confirm:

source authority
validation
correct destination
duplication check
approval
status
version
unresolved assumptions
Change Log requirement

Durable destinations may include:

MCR
Brain Canon
Decision Records
Failure Logs
Outcome Logs
project save points
approved client knowledge stores

Session continuity should preserve:

completed work
incomplete work
decisions
failures
current save point
active Work Unit
exact next action
next owner

Knowledge Governance Rule

Only validated and approved learning may become durable organisational truth.

Improvement, Suspension And Retirement Governance

AI Employees must not remain operational merely because they were created.

Reasons to improve include:

repeated validation failure
weak output
poor routing
missing context
excessive cost
weak outcome
unclear role boundary
tool-permission problems

Reasons to suspend include:

Critical Gate failure
repeated identical failure
source degradation
permission breach
client risk
compliance risk
persistent-agent noise
no active owner
cost exceeding value

Reasons to merge include:

overlapping roles
duplicate workflows
similar outputs
governance complexity

Reasons to park include:

useful later
workflow not ready
tool not ready
M build stage not ready
client system not ready

Reasons to retire include:

low usefulness
duplicate role
high failure rate
unsafe role
no measurable outcome
workflow no longer needed

Improvement Governance Rule

The AI workforce must evolve through Kaizen, not expand endlessly.

AI Workforce Lifecycle

Stage 1 — Proposed

Required:

proposed name
possible purpose
possible Owning Brain
reason for creation
duplicate check

Allowed:

brainstorm
compare
park

Not allowed:

operational use
automation
tool access

Stage 2 — Drafted

Required:

draft Role Card
responsibilities
non-responsibilities
likely inputs
likely outputs
rough workflow position

Allowed:

low-risk manual testing
sample outputs
refinement

Not allowed:

live use
write access
autonomous operation

Stage 3 — Defined

Required:

Role Card
authority level
capability stack
skills
model route
Tool Permission Record
validation requirement
handoff destination
failure modes
outcome expectation

Allowed:

supervised manual use
draft outputs
controlled testing

Stage 4 — Operational

Required:

deployment-readiness review
approved scope
validation
logging
escalation
Rescue Route
human-review controls
outcome measurement
HeadOffice visibility

Allowed:

approved workflow operation
queue support
reporting support
assisted automation where approved

Stage 5 — Measured

Required:

outcome history
cost history
failure history
validation history
usefulness review
improvement recommendations

Allowed:

continued operation
limited expansion
capability adjustment
automation consideration

Stage 6 — Improved

Possible improvements:

role clarification
skill improvement
model-route adjustment
Context Pack improvement
validation improvement
handoff improvement
tool-permission adjustment
cost reduction
outcome improvement

Stage 7 — Expanded Or Restricted

Expansion requires:

proven need
updated Role Card
updated capability stack
updated permissions
increased risk review
HeadOffice approval

Restriction may reduce:

authority
model access
tool access
workflow scope
autonomy
client access

Stage 8 — Suspended

Use when readiness is lost.

Required:

containment
reason
affected workflows
failure evidence
restart conditions
review owner

No operational activity should continue while suspended.

Stage 9 — Retired, Merged Or Parked

Required:

final status
reason
workflow impact
destination of responsibilities
documentation update
registry update where confirmed
final learning

AI Workforce Authority Levels

Advisory Employee

May analyse, explain and recommend.

Cannot execute or commit.

Drafting Employee

May create structured drafts.

Operational Support Employee

May operate within controlled workflows and produce reviewable outputs.

Reviewer Employee

May assess quality and recommend accept, revise, reject, park or escalate.

Validation Employee

May issue formal pass, fail or revision verdicts.

Routing Employee

May route approved work within defined rules.

Controlled Execution Employee

May perform limited approved actions inside logged workflows.

Knowledge Commitment Employee

May commit validated learning to approved destinations.

Restricted Autonomous Employee

May perform tightly defined low-risk actions without immediate review.

This level must be rare.

AI Workforce Separation Of Duties

High-risk workflows should separate:

Planner
Researcher
Analyst
Builder Or Writer
Reviewer
Validator
Approver
Tool Operator
Outcome Verifier
Knowledge Commitment Agent

Rule

The same AI Employee should not research, create, approve, execute and commit high-risk work in one uncontrolled pass.

AI Workforce Governance Board

HeadOffice acts as the AI Workforce Governance Board.

The Board reviews:

new Employee proposals
duplicate roles
role overlap
capability expansion
model-route changes
tool-access requests
deployment readiness
validation failures
repeated failure
Rescue Routing
persistent-agent approval
outcome and cost reports
automation expansion
suspension
restart
retirement
future AIBS client AI packages

At the current stage, Martyn acts through HeadOffice governance authority.

Later, governance may be supported by formal dashboards and review records.

AI Workforce Registry Requirement

MWMS should maintain an AI Workforce Registry when operational need justifies it.

The registry may track:

AI Employee ID:

AI Employee Name:

Owning Brain:

Supporting Brains:

Lifecycle Status:

Authority Level:

Readiness Level:

Primary Role:

Role Card Status:

Capability Stack Status:

Assigned Model Route:

Approved Skills:

Tool Permission Level:

Workflow Position:

Validation Requirement:

Independent Review Requirement:

Human Review Requirement:

Failure Threshold:

Rescue Route:

Persistent Agent Status:

Outcome Score:

Cost Status:

Last Reviewed:

Next Review:

Current Status:

Notes:

The Registry should be created or expanded only when enough active AI Employees exist to justify operational maintenance.

AI Workforce Statuses

Recommended statuses:

Proposed
Drafted
Defined
Manual Use
Assisted Use
Controlled Automation
Restricted Autonomous
Suspended
Rescue Review
Parked
Retired
Merged
Deprecated

Status must remain visible.

Draft or suspended AI Employees must not be treated as operational.

AI Workforce Review Cycle

Per Work Unit

Review:

role compliance
output quality
validation result
escalation behaviour
outcome
cost
failure state

Weekly

Review:

active AI Employees
failed outputs
noisy workflows
handoff failures
repeated failures
persistent-agent alerts
emerging overlaps
required improvements

Monthly

Review:

workforce usefulness
Brain alignment
model costs
permission safety
outcome trends
automation candidates
restriction candidates
suspension candidates
retirement candidates

Quarterly Or At Major Architecture Change

Review:

workforce design
role architecture
governance gaps
client readiness
model strategy
tool strategy
persistent-agent portfolio
total workforce cost
employee consolidation
retirement and replacement

AI Workforce Risk Controls

High-risk AI Employees include those involved in:

paid traffic
finance
compliance
developer implementation
live systems
MCR
client-facing reports
external communication
database writes
public content
persistent autonomous operation

Required controls may include:

human approval
explicit tool permission
independent review
formal validation
escalation path
Rescue Route
logging
cost visibility
outcome measurement
periodic review
shutdown control

Risk Control Rule

The higher the risk, the lower the autonomy.

AI Workforce And M Build Relevance

This governance model may later inform:

AI Employee registry tables
employee configuration files
role-based task assignment
AI Manager permissions
AI Employee Router logic
Task Executor statuses
Brain Room task conversion
validation queues
Rescue Queues
HeadOffice dashboards
employee outcome reports
tool-access controls
model routing
persistent-agent controls

For now, this page is governance only.

It does not authorise immediate technical development.

Any implementation must be handled in the correct development project through an exact developer handoff.

AI Workforce And AIBS Client Systems

This governance model is essential for future AIBS delivery.

Future client AI workforces may include:

Business Intake Agent
Document Cleaning Agent
Customer Inquiry Agent
Sales Support Agent
Operations Reporting Agent
Finance Summary Agent
Approval Gate Agent
Client Dashboard Agent
Manager Review Agent
Persistent Monitoring Agent

Each client AI Employee must have:

Role Card
authority level
capability stack
model route
approved skills
tool permissions
client-data boundary
human review rule
output standard
failure handling
Rescue Route
outcome metric
cost visibility
shutdown control
audit trail

AIBS Rule

Client AI workforces must be governed, permissioned, measurable, reviewable and stoppable.

AI Workforce Creation Checklist

Before creating a new AI Employee, check:

What repeated need does it solve?
Which Brain owns it?
Does a similar Employee already exist?
What role does it perform?
What does it not do?
What authority does it require?
What inputs does it handle?
What outputs does it produce?
What context does it need?
What model capability does it need?
What skills does it need?
What tools does it need?
What tools must it not use?
What workflow stage does it support?
What validation applies?
Is independent review required?
What handoff destination exists?
What failure modes are likely?
What Rescue Route exists?
What outcome should it create?
How will cost and success be measured?
Is human review required?
Is it ready now or should it be parked?
Does HeadOffice approve?

AI Workforce Expansion Checklist

Before expanding an AI Employee, check:

Why is expansion needed?
Does it still fit the role?
Is a Role Card update required?
Is a capability-stack update required?
Is a model-route change required?
Is new tool access required?
Does authority increase?
Does risk increase?
Does it affect live systems?
Does it affect MCR?
Does it affect M’s work?
Does it affect money, compliance, public output or clients?
Is validation still sufficient?
Is independent review required?
Is logging sufficient?
Is cost justified?
Does HeadOffice approve?

AI Workforce Suspension Checklist

Before suspending an AI Employee, check:

What readiness control failed?
Is failure repeated?
Is risk immediate?
Is permission compromised?
Is source quality degraded?
Is cost disproportionate?
Is owner missing?
Is client or compliance risk present?
Which workflows are affected?
What containment is required?
What is the restart condition?
Who approves restart?

AI Workforce Retirement Checklist

Before retiring or merging an AI Employee, check:

Is the role duplicated?
Is it underused?
Is output weak?
Is failure rate high?
Is outcome value low?
Is the role unsafe?
Has the workflow changed?
Can another Employee absorb the role?
Should it be parked instead?
Does retirement affect active workflows?
Does documentation require updating?
Does a confirmed registry require updating?
Does HeadOffice approve?

AI Workforce Failure Modes

MWMS must watch for workforce-level failure.

Common failure modes include:

too many AI Employees created too quickly
role overlap
missing Role Cards
missing capability stacks
unclear authority
unsuitable model routes
tool-access expansion without review
draft Employees treated as operational
no independent review
repeated failure without rescue
persistent agents without shutdown
output without outcome
cost without value
skipped validation
human review removed too early
weak Employees remaining active
client roles deployed without isolation
M receiving vague role concepts
HeadOffice losing workforce visibility
workforce appearing impressive but producing little value

Any of these signs should trigger governance review.

Manual Use Rule

This governance model should be applied manually before workforce governance becomes technical infrastructure.

Manual use helps MWMS identify:

which roles are genuinely required
which roles overlap
which models suit which work
where independent review adds value
where Rescue Agents are required
where persistent agents create value or noise
which permissions are excessive
which Employees deserve expansion
which Employees should be suspended or retired
which registry fields are truly needed

Manual governance proof comes before automation.

Future Plugin Or UI Relevance

This model may later support:

AI Workforce Registry
AI Employee lifecycle dashboard
Role Card management
authority-level management
capability-stack management
skill assignment
model-route assignment
tool-permission controls
deployment-readiness gate
validation history
Rescue Routing
persistent-agent control
outcome and cost reporting
suspension and restart
retirement and merge controls
AIBS client workforce templates

Possible future fields:

ai_employee_id

ai_employee_name

owning_brain

supporting_brains

lifecycle_status

authority_level

readiness_level

primary_role

role_card_status

capability_stack_status

assigned_model_route

approved_skills

tool_permission_level

workflow_position

validation_requirement

independent_review_requirement

human_review_requirement

failure_threshold

rescue_route

persistent_agent_status

outcome_score

cost_status

last_reviewed

next_review

current_status

suspension_reason

restart_condition

retirement_reason

notes

created_at

updated_at

No technical build is authorised by this governance model alone.

Governance Role

HeadOffice owns the MWMS AI Workforce Governance Model.

HeadOffice is responsible for:

approving Employee creation
approving Role Cards
approving authority levels
approving capability stacks
approving model routes
approving tool permissions
approving deployment levels
governing independent review
governing failure thresholds
governing Rescue Agents
governing persistent agents
reviewing validation failures
reviewing outcome and cost records
approving capability expansion
approving restriction
approving suspension and restart
approving merging and retirement
protecting MCR
protecting M’s active build
protecting client-facing systems
ensuring AI Employees improve MWMS rather than create noise

Individual Brains may propose, operate and improve AI Employees within their scope.

HeadOffice governs the workforce as a whole.

Relationship To SIT Brain

SIT Brain may:

verify Role Card completeness
verify authority boundaries
detect duplicate or overlapping roles
verify deployment-readiness level
verify model-route suitability
verify tool permissions
verify independent review
verify failure thresholds
trigger Rescue Routing
pause persistent agents
detect false completion
verify outcome evidence
block unapproved knowledge commitment
verify suspension and restart conditions

Relationship To Data Brain

Data Brain supports:

AI Employee records
lifecycle history
authority records
capability records
model routes
skill assignments
tool permissions
deployment reviews
validation records
failure counts
Rescue Records
persistent-agent status
cost records
outcome records
suspension records
retirement records
review history

Relationship To Other MWMS Standards

This governance model supports and must align with:

MWMS AI Agent Operations Core
MWMS Agentic Work Unit Standard
MWMS AI Employee Role Card Standard
MWMS AI Employee Capability Stack Framework
MWMS AI Agent Orchestration Framework
MWMS AI Multi Agent Role Design Framework
MWMS AI Workflow Pipeline Standard
MWMS AI Plugin Orchestration Framework
MWMS AI Exchange Zone And Dependency Control Framework
MWMS AI Output Validation Standard
MWMS Messy Input Normalization Framework
MWMS Agentic Reporting Standard
MWMS AI Employee Handoff Protocol
MWMS AI Agent Failure Handling And Escalation Protocol
MWMS AI Ambiguity And Partial Failure Containment Framework
MWMS AI Agent Outcome Measurement Framework
MWMS AI Agent Deployment Readiness Checklist
MWMS AI Tool Permission And Access Framework
MWMS AI Agent Memory And Context Framework
MWMS AI Observability Metadata Standard
MWMS Brain Routing Rule
MWMS Brain To Brain Request Protocol
MWMS Supabase Event Schema
AIBS Brain Blueprint

This model sits above the individual standards and governs how they manage the total AI workforce.

Drift Protection

This governance model protects MWMS from:

creating AI Employees without operational need
creating overlapping agents
treating Employees as prompts
giving capability without governance
granting tool access too early
using unsuitable models
deploying before readiness
skipping independent review
repeated failure without rescue
persistent agents without shutdown
measuring output instead of outcomes
ignoring cost
keeping weak Employees active
removing human review too early
unmanaged Brain-specific roles
unsafe client deployments
asking M to build from vague concepts
workforce growth outpacing HeadOffice control
mistaking more roles for more value
failing to suspend, merge or retire weak roles
committing unverified output as organisational knowledge

AI Workforce Governance Drift Signals

MWMS should watch for:

no Owning Brain
no Role Card
no authority level
capability stack incomplete
skills missing
model route unexplained
permissions too broad
workflow position unclear
validation missing
reviewer not independent
handoff destination missing
failure threshold absent
Rescue Route absent
persistent agent lacks owner
shutdown missing
output volume rising without outcomes
cost rising without value
status unclear
suspended Employee still operating
knowledge commitment uncontrolled
review cycle missed
retirement never considered

Rule

Workforce governance drift must be corrected before further authority, access or automation is granted.

Minimum Compliance Standard

A formal AI Employee is compliant only when it defines:

AI Employee ID
AI Employee Name
Owning Brain
Supporting Brains
lifecycle status
authority level
Role Card
capability stack
approved skills
model route
tool permissions
workflow position
input types
output types
validation requirement
independent-review requirement
human-review requirement
handoff destination
failure modes
failure threshold
Rescue Route
persistent-agent controls where relevant
logging
cost tracking where relevant
outcome measurement
knowledge-commitment boundary
review date
next review
suspension condition
retirement or merge path

Architectural Intent

The architectural intent of the MWMS AI Workforce Governance Model is to make the AI workforce scalable, controllable and useful.

MWMS is moving toward a future where many AI Employees support many Brains and business workflows.

That future requires governance.

The long-term goal is that MWMS can answer for every AI Employee:

Why does it exist?
Which Brain owns it?
What role does it perform?
What authority does it hold?
What can it do?
What can it not do?
Which skills does it use?
Which model route does it require?
Which tools can it access?
What workflow does it support?
What outputs does it produce?
How is its work reviewed?
How is its work validated?
Where does its work go?
What happens if it fails?
What triggers rescue?
Can it be stopped?
What outcome should it create?
What does it cost?
How is it measured?
What knowledge may it commit?
Should it be expanded, restricted, suspended, parked, merged or retired?

When MWMS can answer these questions across the whole workforce, it has the foundation for a real governed AI business operating ecosystem.

Strategic Summary

The v1.1 update expands the MWMS AI Workforce Governance Model from a ten-layer workforce framework into a complete AI workforce lifecycle and control model.

The upgraded model now governs:

authority levels
model routes
specialist role separation
independent review
Exchange Zones
dependency states
deterministic Rescue Routing
persistent agents
external knowledge roles
tool-execution verification
cost visibility
knowledge commitment
session continuity
deployment suspension
restart controls
workforce consolidation and retirement

The key shift is:

AI workforce governance is not only about defining roles.

It is about controlling authority, capability, model selection, permissions, deployment, failure, recovery, cost, outcomes and lifecycle across the entire MWMS AI workforce.

Final Rule

MWMS AI Employees must be governed as a workforce, not treated as isolated prompts, tools, models or automations.

No operational need, no new Employee.

No Owning Brain, no accountability.

No Role Card, no formal role.

No authority boundary, no safe action.

No model fit, no reliable execution.

No tool permission, no tool use.

No independent review, no high-risk trust.

No failure threshold, no controlled recovery.

No shutdown, no persistent agent.

No verified outcome, no workforce value.

No approved knowledge commitment, no durable truth.

No lifecycle review, no justified continued operation.

Change Log

Version: v1.1
Date: 2026-06-18
Author: HeadOffice

Change:

Updated the MWMS AI Workforce Governance Model using the AI Automations by Jack block covering multi-agent orchestration, model routing, independent review, Rescue Routing, persistent agents, external knowledge systems, tool verification, cost visibility and session continuity.

Added:

AI Workforce Governance Principles
Authority Governance
Model Route Governance
Exchange Zone And Dependency Governance
Persistent Agent Governance
Outcome, Cost And Performance Governance
Knowledge Commitment And Continuity Governance
Expanded AI Workforce Lifecycle
Expanded Authority Levels
Separation Of Duties
Suspension lifecycle stage
AI Workforce Suspension Checklist
Manual Use Rule
Future Plugin Or UI Relevance
Relationship To SIT Brain
Relationship To Data Brain
Governance Drift Signals
Minimum Compliance Standard
Strategic Summary
Final Rule

Expanded:

Purpose
Scope
Core Definition
governance layers
employee creation
role governance
capability governance
tool permission governance
workflow and deployment governance
validation governance
failure and escalation governance
performance governance
improvement and retirement governance
registry requirements
review cycles
risk controls
M build relevance
AIBS relevance
creation, expansion and retirement checklists
failure modes
governance role
drift protection
architectural intent

Corrected canonical references from AI Business Systems Brain to AIBS Brain.

Purpose of update:

To evolve the MWMS AI Workforce Governance Model from a high-level AI Employee governance framework into the complete lifecycle, authority, model, tool, validation, rescue, performance, persistent-agent and knowledge-control model for the full MWMS AI workforce.

Version: v1.0
Date: Initial Draft
Author: HeadOffice

Change:

Created the MWMS AI Workforce Governance Model as the high-level governance model for managing the full AI workforce across MWMS.

Change Impact Declaration

This v1.1 update expands the AI Workforce Governance Model from a ten-layer AI Employee framework into a complete workforce-control model covering authority, model routing, independent review, dependency control, Rescue Routing, persistent agents, cost visibility, knowledge commitment, suspension, restart and retirement.

Pages Created

None

Pages Updated

MWMS AI Workforce Governance Model

Pages Deprecated

None

Standalone Pages Not Created

MWMS AI Workforce Authority Model

MWMS AI Workforce Model Routing Standard

MWMS AI Workforce Suspension Protocol

MWMS Persistent Agent Governance Standard

MWMS AI Workforce Cost Governance Standard

MWMS AI Workforce Knowledge Commitment Standard

Registries Requiring Update

None confirmed by the supplied source.

Canon Version Update Required

Change Log Entry Required

Yes

Strategic Absorption Result

MWMS gains a stronger workforce governance model that controls why AI Employees exist, who owns them, what authority they hold, which models and tools they use, how they are validated, how repeated failure triggers rescue, how persistent agents remain stoppable, how performance and cost are measured, and when roles should be expanded, restricted, suspended, merged or retired.

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