MWMS AI Output Validation Standard

System: MWMS
Document Type: Operating Standard
Authority Level: MCR Source Of Truth
Status: Active
Version: v1.1
Primary Location: MCR
Future Operational Destination: HeadOffice Brain, MWMS Brain, Brain Room, AI Manager, AI Employee Router, Task Executor Systems, 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: 2026-06-17
Source / Origin: MWMS AI Output Validation Standard v1.0 + AI Automations by Jack — Multi-Model Review, Rescue Routing, Persistent Agents, External Knowledge, Tool Verification And Session Closure Block
MWMS Classification: AI Output Quality Standard / Validation Governance Framework / Independent Review Standard / Operational Truth Gate / Outcome Verification Standard
Primary Brain: HeadOffice Brain
Supporting Brains: MWMS Brain, Automation Brain, AIBS Brain, Operations Brain, Data Brain, Risk Brain, Compliance Brain, SIT Brain
Related Pages: MWMS AI Agent Operations Core, MWMS Agentic Work Unit Standard, MWMS AI Employee Role Card Standard, MWMS AI Agent Orchestration Framework, MWMS AI Workflow Pipeline Standard, MWMS AI Agent Failure Handling And Escalation Protocol, MWMS Independent Model Review And Rescue Routing Framework, MWMS AI Agent Memory And Context Framework, MWMS External Knowledge Engine And Reasoning Agent Separation Framework, MWMS AI Work Session Closure And Knowledge Commitment Protocol, MWMS AI Tool Permission And Access Framework, MWMS AI Observability Metadata Standard, MWMS AI Usage And Cost Visibility Standard, MWMS Brain Routing Rule, MWMS Brain To Brain Request Protocol, MWMS Supabase Event Schema
Source Evidence: The existing MWMS AI Output Validation Standard defines the validation levels, checks, decision states, failure handling, logging, dashboard protection, course absorption review, developer-output review and automation-readiness requirements used before AI output is trusted. The newly absorbed AI Automations by Jack material strengthens this standard with independent-model review, deterministic rescue routing, external knowledge provenance, tool-execution verification, persistent-agent validation, cost and observability checks, outcome verification, session closure and durable knowledge-commitment controls.

Purpose

The purpose of this document is to define the MWMS AI Output Validation Standard.

This standard establishes how MWMS checks AI-generated outputs before they are:

accepted
routed
saved
displayed
published
acted upon
executed
committed to durable knowledge
treated as operational truth

MWMS is building a governed AI business ecosystem.

That means AI output cannot be trusted simply because it is:

well written
detailed
confident
fast
technically impressive
produced by an advanced model
generated by several agents
returned from a connected tool
supported by retrieved text
produced by a persistent workflow

AI output must be checked.

Validation determines whether an AI output is:

complete
sufficiently accurate
source-grounded
current
useful
correctly routed
correctly formatted
safe
non-duplicative
permission-compliant
independently reviewed where required
connected to a business outcome
verified after execution
suitable for durable knowledge commitment

This standard exists to prevent MWMS from mistaking AI activity for reliable business progress.

Scope

This standard applies to all important AI outputs created inside MWMS.

This includes outputs from:

HeadOffice Brain
Brain Room
AI Manager
AI Employee Router
Task Executor systems
Dev Console
Newsletter Intelligence
Course Absorption
Offer Evaluation
Affiliate Brain
Research Brain
Experimentation Brain
Finance Brain
Content Brain
Ads Brain
Strategy Brain
Data Brain
Operations Brain
Automation Brain
Risk Brain
Compliance Brain
SIT Brain
AIBS Brain
persistent AI Employees
scheduled workflows
remote-command workflows
external knowledge systems
future client-facing AI workflows

This standard applies to both manual and automated outputs.

Manual outputs include:

MCR page drafts
course absorption reports
strategy notes
research summaries
developer briefs
offer evaluations
HeadOffice recommendations
role cards
workflow designs
Brain-to-Brain handoffs

Automated outputs include:

Supabase task results
newsletter intelligence records
dashboard items
routed actions
queue records
AI Employee results
alerts
monitoring reports
scheduled summaries
tool-generated records
client workflow outputs

Core Definition

AI Output Validation is the process of determining whether an AI-generated result is fit for its intended use.

An output is valid only when it meets the standard required for its:

task
risk level
destination
authority
workflow stage
business purpose
tool impact
client impact
knowledge-commitment status

A low-risk draft may need only a simple sense check.

A high-risk output may require:

source verification
independent review
deterministic testing
compliance review
financial review
security review
human approval
outcome verification
complete logging

Validation does not mean an output is perfect.

Validation means the output is sufficiently:

accurate
complete
safe
structured
grounded
usable
governed

for its next approved stage.

Core Principle

The core principle of this standard is:

No important AI output becomes operational truth until it passes the required validation.

This protects MWMS from:

hallucinated information
invented structure
wrong Brain routing
vague recommendations
weak course absorption
misleading reports
unsafe developer instructions
poor offer decisions
dashboard noise
compliance risk
financial risk
duplicate pages
automation drift
false confidence
false completion
self-approved high-risk work
repeated failure loops
unverified tool claims
weak retrieval being treated as evidence
unmonitored persistent-agent output
unverified knowledge commitment

AI may:

assist
draft
recommend
retrieve
process
classify
generate
monitor
prepare action

Validation determines whether the output is trusted.

Validation And Operational Truth

MWMS must distinguish between the following states:

Generated

The AI created an output.

Prepared

The output was structured for possible use.

Validated

The output passed the required checks.

Approved

The authorised human or system accepted the output.

Executed

The approved action was performed.

Verified

Evidence confirms the intended action or outcome occurred.

Committed

The validated result was recorded in the approved durable knowledge destination.

Closed

The work, outcome, open issues and next action were recorded.

Rule

Generated is not validated.

Validated is not approved.

Approved is not executed.

Executed is not verified.

Verified is not committed.

Committed is not closed.

Quality Assurance Versus Quality Control

MWMS must separate Quality Assurance from Quality Control.

Both are required.

Quality Assurance

Quality Assurance prevents bad output before generation.

Quality Assurance includes:

clear AI Employee Role Cards
specific Agentic Work Units
correct Brain ownership
strong task instructions
complete Context Packs
suitable model routing
defined agent patterns
output schemas
tool permissions
forbidden actions
document standards
workflow standards
failure thresholds
rescue routes
human-approval requirements
client boundaries
security controls

Quality Assurance asks:

Was the AI set up correctly before it generated the output?

Quality Control

Quality Control checks the output after generation.

Quality Control includes:

validation checklists
source-grounding review
pass/fail tests
format review
risk review
independent review
human approval
rejection reasons
revision requests
tool-result verification
outcome verification
logging
learning capture

Quality Control asks:

Is this output fit for its intended use?

Quality Assurance prevents mistakes.

Quality Control detects mistakes.

MWMS requires both.

Validation Ownership

Every important output must have a named validation owner.

Possible validation owners include:

producing AI Employee for low-risk self-checks
Checking Agent
Independent Model Review Agent
SIT Brain
HeadOffice
relevant specialist Brain
Martyn
M
human subject-matter expert

Validation ownership must define:

what is being checked
which standard applies
whether the reviewer is independent
what evidence is available
who makes the final decision
what happens if reviewers disagree

Rule

An output with no validation owner should remain a draft.

AI Output Validation Levels

MWMS uses five validation levels.

Level 1 — Light Validation

Used for low-risk internal support.

Examples:

brainstorming
rough notes
simple explanations
low-risk rewrites
informal planning ideas

Checks:

does it make sense?
is it useful?
is it obviously wrong?
does it follow the request?
does it stay within scope?

Independent review:

Not required.

Human review:

Optional.

Level 2 — Structured Validation

Used for structured internal work.

Examples:

course absorption notes
internal summaries
Brain Room responses
newsletter review drafts
content drafts
simple research notes
workflow suggestions

Checks:

completeness
specificity
format
Brain mapping
source support
next action
destination
no obvious invention

Independent review:

Recommended where the output may influence later work.

Human review:

Recommended before saving or routing.

Level 3 — Operational Validation

Used for outputs entering MWMS workflows.

Examples:

MCR page drafts
Agentic Work Units
AI Employee Role Cards
pipeline outputs
dashboard items
routed actions
developer briefs
offer evaluations
finance reports

Checks:

source grounding
output schema
Brain ownership
business outcome
handoff
duplication
permission compliance
context sufficiency
MWMS standard alignment
human-review requirement
failure handling
knowledge-commitment status

Independent review:

Required where operational impact is material.

Human review:

Required before operational use.

Level 4 — High-Risk Validation

Used for outputs affecting:

money
compliance
security
development
public content
client delivery
major business decisions
Canon
governance

Examples:

paid traffic recommendations
offer test approvals
compliance reviews
financial decisions
developer instructions
production-change recommendations
client-facing reports
public claims
automation authority changes

Checks:

all Level 3 checks
source verification
independent review
risk review
compliance review where applicable
security review where applicable
developer-boundary review
financial logic
reviewer independence
outcome verification plan
final human approval

Human review:

Mandatory.

Level 5 — Critical Validation

Used for irreversible, external, live or legally sensitive action.

Examples:

database write
live WordPress update
external email send
client delivery
financial transaction
ad campaign launch
production deployment
destructive action
permission expansion
legal or regulated communication

Checks:

all Level 4 checks
explicit approval
exact action scope
credential and permission confirmation
live-state confirmation
rollback or correction path
named owner
duplicate prevention
event logging
outcome verification
emergency shutdown
post-action review

Human review:

Mandatory before execution.

Independent review:

Mandatory unless a verified deterministic control provides equivalent assurance.

Validation Dimensions

Every important AI output should be checked across the following dimensions.

Task Completeness

Does the output answer the complete task?

Check:

all requested sections
all constraints
all required deliverables
all required decisions
all required next actions

Failure sign:

The output is polished but incomplete.

Source Grounding

Is the output supported by actual source material?

Check:

uploaded files
MCR
current user instruction
verified data
approved external sources
source provenance

Failure sign:

The output sounds convincing but cannot be traced to evidence.

Source Authority

Is the source authoritative for the claim?

Check:

MCR versus project memory
current evidence versus stale notes
official source versus commentary
client record versus generic retrieval
approved database versus cached output

Failure sign:

Relevant information is mistaken for authoritative information.

Source Freshness

Is the source current enough for the decision?

Check:

date
version
current status
last review
superseded standards
changed tool behaviour
changed policies
changed system state

Failure sign:

Correct historical information is used as current truth.

Provenance

Can MWMS identify where the claim came from?

Check:

source name
location
version
date
record ID
client
retrieval path

Failure sign:

Evidence exists but cannot be audited.

Specificity

Is the output specific enough to use?

Check:

named Brain
named page
named workflow
named role
exact recommendation
clear next action
measurable condition

Failure sign:

The output sounds intelligent but does not move work forward.

Format Compliance

Does the output follow the required structure?

Check:

full page output
report format
role-card format
task schema
JSON schema
dashboard structure
decision format
closure format

Failure sign:

Useful content is not operationally usable.

Brain Routing

Is the output assigned to the correct Brain?

Check:

Owning Brain
Supporting Brains
HeadOffice visibility
correct destination
correct source-of-truth location

Failure sign:

Good output is stored or acted on in the wrong place.

Business Outcome

Does the output support a real outcome?

Check:

decision
task
test
report
risk reduction
revenue support
workflow improvement
knowledge update
client value
rejection or parking

Failure sign:

Output exists, but nothing useful happens.

Handoff

Does the output have a clear next destination?

Check:

reviewer
Brain
queue
MCR
task system
dashboard
archive
parking
client
developer

Failure sign:

The output floats without ownership.

Risk

What damage could happen if the output is wrong?

Check:

financial impact
live-system impact
client impact
public impact
compliance impact
development impact
Canon impact
irreversible action

Failure sign:

High-risk work is treated like an internal note.

Compliance

Does the output create legal, advertising, privacy, financial or platform risk?

Check:

unsupported claims
misleading expectations
sensitive data
platform policy
disclosure requirements
jurisdiction-specific concerns
regulated communication

Failure sign:

Useful output is unsafe to use.

Security

Does the output expose or misuse system access?

Check:

credentials
tokens
session cookies
private endpoints
client data
tool permissions
local-system exposure
remote-command risk
destructive commands

Failure sign:

The output is operationally useful but insecure.

Duplication

Does the output duplicate existing MWMS structure?

Check:

existing page
existing framework
existing Role Card
existing task standard
existing workflow
existing lesson

Failure sign:

A new page is created where an update should occur.

Naming And Structure

Does the output follow MWMS naming and hierarchy rules?

Check:

exact title
parent page
document type
Brain name
hierarchy
metadata
status
version placement

Failure sign:

Good content creates MCR or WordPress structural disorder.

Developer Boundary

Does the output affect M’s active work?

Check:

active system area
current save point
exact file or screen
scope
what not to touch
testing
rollback

Failure sign:

Good technical advice is given at the wrong time or without current evidence.

Tool Permission

Did the AI operate within approved tool boundaries?

Check:

tool was actually available
permission level
endpoint
data scope
client boundary
human approval
logging
credential custody

Failure sign:

The AI implies access or action it did not have.

Model Suitability

Was the chosen model or capability appropriate?

Check:

reasoning depth
modality
context length
privacy
cost
tool compatibility
reviewer independence

Failure sign:

The output fails because the wrong capability was assigned.

Independent Review

Was the output independently checked where required?

Check:

different model family
separate specialist
another Brain
deterministic validator
human review

Failure sign:

The producing route approves its own high-risk work.

Outcome Verification

Did the intended result actually occur?

Check:

page published
record written
test passed
message sent
task completed
file created
workflow updated
user confirmed
system state changed

Failure sign:

Prepared work is reported as completed work.

Knowledge Commitment

Should the output become durable knowledge?

Check:

authority
source
destination
approval
duplicate check
version
status
provenance
client boundary

Failure sign:

Unverified output becomes permanent organisational memory.

Closure

Is the work formally closed?

Check:

final status
outcome
verification
handoff
knowledge committed
open issues
next action
closure date

Failure sign:

The session ends without continuity.

Default MWMS Validation Checklist

Every important AI output should be checked against:

Task Complete:

Source Grounded:

Source Authoritative:

Source Current:

Provenance Preserved:

Specific Enough:

Correct Format:

Correct Brain:

Correct Parent Or Destination:

Business Outcome Clear:

Handoff Clear:

Risk Classified:

Compliance Checked:

Security Checked:

Duplication Checked:

Naming Checked:

Developer Boundary Checked:

Tool Permission Confirmed:

Model Route Suitable:

Independent Review Completed:

Human Review Completed:

Outcome Verified:

Knowledge Commitment Approved:

Closure Recorded:

Validation Decision States

Every important AI output should receive one of the following states.

Accepted

The output is fit for its intended use.

Accepted With Minor Edits

The output is substantially correct but requires small changes.

Revise

The output contains useful value but is not ready.

Common reasons:

incomplete
too generic
wrong format
missing context
missing evidence
unclear destination
insufficient detail
weak risk control

Park

The output may be useful later but should not be acted on now.

Common reasons:

not current priority
depends on unfinished systems
overlaps future work
needs stronger evidence
useful but not yet operational

Reject

The output should not be used.

Common reasons:

weak value
unsupported claims
duplication
wrong direction
unsafe recommendation
poor source
misleading conclusion
outside scope

Escalate

The output requires review by:

HeadOffice
Martyn
M
SIT Brain
Compliance Brain
Risk Brain
Finance Brain
another specialist
human expert

Rescue Required

The producing route has reached the failure threshold.

Execution Blocked

The output may be valid as analysis but cannot proceed because:

approval is missing
permission is missing
risk is too high
live state is unverified
rollback is absent
client authority is absent

Validation Evidence Standard

A validation decision should be supported by evidence.

Possible evidence includes:

source file
citation
MCR page
database record
screenshot
test result
tool response
system log
reviewer report
client approval
human confirmation
current system state

Rule

A validation label without evidence is only an opinion.

Independent Review Standard

Independent review is required where output affects:

MCR
Canon
governance
finance
compliance
security
live systems
public communication
external action
client delivery
irreversible action
high-value decisions

Independent review must be sufficiently separate from the producing route.

Acceptable independent reviewers include:

different model family
specialist AI Employee
separate Brain
deterministic validator
human reviewer

Independent review should examine:

evidence
reasoning
assumptions
missing context
contradictions
permissions
risk
destination
business outcome

No-Self-Approval Rule

The producing AI Employee may perform self-checks.

It may not be the sole final approver of its own high-risk output.

Disagreement Handling

Where the producing route and independent reviewer disagree:

Preserve both positions.
Identify the disputed claim.
Compare source evidence.
Determine whether the disagreement affects risk or action.
Escalate to HeadOffice or human review where material.
Do not silently merge incompatible conclusions.
Record the final decision and authority.

Failure Counter And Rescue Rule

Validation failures must be counted.

The standard rescue threshold is:

Two materially identical failures without verified progress.

A failure may include:

repeated missing section
repeated wrong format
repeated unsupported claim
repeated wrong Brain
repeated tool failure
repeated code error
repeated validation rejection
repeated ignored user correction
repeated false completion claim

At the threshold:

the current route stops
failure history is preserved
the Work Unit becomes Rescue Required
a Rescue Packet is created
a materially different reasoning or technical route is assigned
a new verification gate is defined

The failure count does not reset because:

the wording changed
a new chat began
the same model claims better understanding
the same action was reformatted
confidence increased

Rule

Repeated validation failure triggers rescue.

It does not justify endless regeneration.

Tool Execution Validation

Where an AI output claims a tool action occurred, validation must confirm:

tool identity
action requested
permission
target
actual response
resulting state
failure or success
external effect
rollback or correction path
event log

Examples:

A draft email is not a sent email.

A generated page is not a published page.

A SQL statement is not a changed database.

A deployment command is not a successful deployment.

A file path in a response is not proof the file exists.

Rule

AI must not claim execution without evidence from the execution system.

Persistent Agent Output Validation

Persistent and scheduled AI Employees require additional validation.

Checks should include:

correct trigger
current context
active permissions
correct schedule
duplicate prevention
cost boundary
retry count
failure threshold
alert quality
shutdown state
stale-instruction detection
outcome verification

Rule

A persistent agent must not continue producing unvalidated output merely because it operates automatically.

External Knowledge Validation

Where output uses an external knowledge engine, validate:

source identity
source authority
source freshness
retrieval relevance
provenance
client boundary
conflicting sources
missing source gaps
whether retrieval was complete enough

Rule

Retrieved context supports reasoning.

It does not become truth merely because similarity search returned it.

Cost Validation

For serious or repeated workflows, validate whether the output justified its cost.

Check:

model cost
tool cost
number of agent calls
validation cost
rescue cost
business value
whether a simpler route would work

Rule

A valid output may still come from an inefficient workflow.

Cost inefficiency should become a Kaizen item.

Validation Examples

Example 1 — Course Absorption Output

Check:

Does it extract reusable MWMS value?
Does it compare against current MCR?
Does it update before creating?
Does it avoid duplication?
Does it preserve exact Brain names?
Is the parent page verified?
Does it ignore hype?
Does it respect the development boundary?
Is the full file format correct?
Is the Change Impact Declaration complete?

Possible decisions:

Accept
Revise
Park
Reject
Merge With Existing Page

Example 2 — Newsletter Intelligence Output

Check:

Is the signal specific?
Is it business-relevant?
Is the Brain route correct?
Is priority believable?
Is the source current?
Is the action classification correct?
Is there a useful next step?
Is the item noise?

Possible decisions:

Route
Monitor
Park
Reject
Convert To Action

Example 3 — Offer Evaluation Output

Check:

Is the verdict evidence-based?
Are vendor claims separated from verified evidence?
Is mechanism clear?
Is traffic fit considered?
Is compliance checked?
Is finance logic included?
Is testing readiness clear?
Is the verdict YES, Conditional YES or NO?
Is the next action clear?

Possible decisions:

NO
Conditional YES
YES
Research Required
Finance Review Required
Compliance Escalation

Example 4 — Developer Instruction Output

Check:

Exact site?
Exact page or file?
Current visible evidence?
Exact insertion or replacement point?
Full file output where required?
Current save point?
What not to touch?
Testing steps?
Rollback?
Does it avoid relying on memory alone?

Possible decisions:

Send To M
Revise
Escalate
Hold For Current Evidence

Example 5 — MCR Page Output

Check:

Exact title?
Correct parent?
Correct status?
Correct Brain names?
No duplicate page?
Source evidence?
Current version?
Required metadata?
Governance Role?
Drift Protection?
Architectural Intent?
Change Log?
Change Impact Declaration?
Correct end marker?

Possible decisions:

Save To MCR
Revise
Merge With Existing Page
Park
Reject

Example 6 — Persistent Monitoring Output

Check:

Did the approved schedule trigger?
Were current checks used?
Were permissions read-only?
Is the alert specific?
Is severity correct?
Is it a duplicate alert?
Did failure count increase?
Was the correct owner notified?
Is the monitoring workflow still within cost?
Is shutdown available?

Possible decisions:

Accept And Route Alert
Suppress Duplicate
Revise Severity
Rescue Required
Disable Workflow

Validation Roles

Self-Validation

The producing AI Employee checks:

completeness
format
obvious omissions
role compliance
basic source support

Self-validation is useful for low-risk work.

It is insufficient for high-risk work.

Checking Agent

A separate Checking Agent reviews:

structure
completeness
destination
standard compliance
duplication
risk indicators

Independent Model Review Agent

Reviews:

reasoning
evidence
assumptions
contradictions
risk
unsupported conclusions

SIT Brain

May validate:

role-card compliance
permission compliance
failure thresholds
reviewer independence
observability
execution claims
outcome verification
knowledge commitment

HeadOffice

Validates:

governance
strategic fit
Brain routing
system impact
business value
final disposition

Martyn

Reviews:

MCR changes
strategic direction
major standards
course absorption creation
offer-test decisions
paid traffic
high-impact workflow changes

Reviews technical implementation where:

code
plugins
APIs
Supabase
WordPress
infrastructure
live system behaviour

are involved.

Validation Failure Handling

When an output fails validation, MWMS must handle the failure explicitly.

If Output Is Incomplete

Action:

identify missing sections
revise task instruction
return for correction
preserve failure reason

If Output Is Too Generic

Action:

require specific MWMS application
require Brain mapping
require next action
reject if no operational value exists

If Output Is Unsupported

Action:

request source evidence
verify current data
mark assumptions
reject unsupported claims

If Output Is Misrouted

Action:

correct ownership
route to correct Brain
update routing rule if recurring

If Output Is Duplicative

Action:

update existing page
merge with current standard
reject unnecessary creation
log duplication failure

If Output Is High Risk

Action:

block execution
escalate
require independent review
require human approval
define outcome verification

If Output Claims Unverified Execution

Action:

change status to Prepared or Execution Unverified
retrieve tool evidence
verify current system state
block completion claim

If Output Reveals A Workflow Weakness

Action:

create Kaizen item
update Role Card
update Work Unit
update pipeline
update validation checklist
improve model route
improve context
improve rescue logic

Validation Logging

Important validation decisions must be logged.

A Validation Record should include:

Validation Record ID:

Output Title:

Work Unit ID:

Source:

Owning Brain:

Producing AI Employee:

Producing Model:

Validation Level:

Validation Owner:

Independent Reviewer:

Source Evidence:

Checks Performed:

Validation Result:

Failure Reasons:

Required Revision:

Risk Level:

Human Approval:

Final Decision:

Outcome Verification:

Knowledge Commitment Status:

Date:

Learning Captured:

Validation logging helps MWMS identify:

unreliable workflows
weak AI Employees
poor model routing
repeated failure modes
routing problems
unclear standards
tool-permission problems
false completion
automation readiness
cost inefficiency
training needs

Automation Readiness Rule

A workflow should not be automated until its outputs can be validated reliably.

Before automation, confirm:

input is predictable enough
output format is stable
validation checklist exists
independent review is defined where required
failure modes are known
rescue route exists
human-review rule exists
logging exists
observability exists
risk is acceptable
outcome is clear
outcome verification exists
rollback or correction exists
shutdown exists

Rule

If MWMS cannot validate the output, MWMS should not automate the workflow.

Dashboard Validation Rule

A dashboard should display validated, prioritised and useful intelligence.

Before an item appears, check:

specific
current
business-relevant
source-grounded
correct priority
correct action type
correct Brain route
next action clear
duplicate status
validation status
owner

Rule

A dashboard filled with unvalidated output becomes clutter, not command intelligence.

Course Absorption Validation Rule

Course absorption must be judged by MWMS value, not course enthusiasm.

A lesson is worth absorbing only if it improves:

MWMS Brain
MWMS Blueprint
AI Employee design
workflow structure
validation
orchestration
reporting
governance
AIBS delivery
future expansion

Weak, duplicated, hype-based or overly tool-specific material should be ignored or parked.

Rule

Do not absorb content because it exists.

Absorb it because it improves MWMS.

Developer Output Validation Rule

Developer outputs must be held to a high standard.

Any instruction for M or live systems must include:

exact site
exact file or page
current visible evidence
exact insertion or replacement location
complete file output where required
current save point
what not to touch
testing
rollback
expected result
verification method

Rule

Vague developer instructions are not valid MWMS outputs.

Knowledge Commitment Validation Rule

Before output becomes durable organisational knowledge, confirm:

source
authority
current status
correct destination
duplication check
approval
version
provenance
client boundary
uncertainty
relationship to existing Canon

Rule

Generated output must not silently become permanent memory.

Session Closure Validation Rule

Before a work session is treated as complete, confirm:

objective recorded
completed work recorded
failed work recorded
decisions recorded
current save point recorded
unresolved items recorded
next action recorded
knowledge committed
final status clear

Rule

A conversation ending is not the same as work being closed.

Governance Role

HeadOffice owns the MWMS AI Output Validation Standard.

HeadOffice is responsible for:

defining validation expectations
setting validation levels
assigning validation ownership
governing independent review
deciding when human review is required
preventing unvalidated output from becoming operational truth
protecting dashboards from noise
protecting MCR from weak or duplicate pages
protecting M’s active build
governing outcome verification
governing knowledge commitment
ensuring validation evidence is logged
maintaining validation discipline across Brains

Individual Brains may add stronger validation rules.

They must not remove the core validation requirements of this standard.

Relationship To SIT Brain

SIT Brain may:

enforce validation requirements
verify reviewer independence
inspect tool-permission compliance
detect repeated failures
trigger rescue
detect false completion
verify outcome evidence
inspect persistent-agent validation
block unsafe execution
inspect knowledge commitment
test shutdown and revocation

Relationship To Data Brain

Data Brain supports:

validation records
source provenance
output versions
event logs
status history
reviewer identity
outcome evidence
client isolation
knowledge-commitment records
archival and retention

Relationship To Other MWMS Standards

This document supports and must align with:

MWMS AI Agent Operations Core
MWMS Agentic Work Unit Standard
MWMS AI Employee Role Card Standard
MWMS AI Agent Orchestration Framework
MWMS AI Workflow Pipeline Standard
MWMS AI Agent Failure Handling And Escalation Protocol
MWMS Independent Model Review And Rescue Routing Framework
MWMS AI Agent Memory And Context Framework
MWMS External Knowledge Engine And Reasoning Agent Separation Framework
MWMS AI Work Session Closure And Knowledge Commitment Protocol
MWMS AI Tool Permission And Access Framework
MWMS AI Observability Metadata Standard
MWMS AI Usage And Cost Visibility Standard
MWMS Brain Routing Rule
MWMS Brain To Brain Request Protocol
MWMS AI Output Standard Full File Delivery Rule
MWMS Brain Header Schema Standard
MWMS Page Naming Standard
MWMS Document Structure Standard
MWMS Architecture Registry
MWMS Brain Interaction Map
MWMS System Data Flow Map
MWMS Supabase Event Schema
HeadOffice Newsletter Intelligence Operating Protocol
HeadOffice Newsletter Intelligence Output Validation Protocol
MWMS Course Absorption Operating Rule
MWMS Opportunity System Operating Protocol
Experimentation Brain Canon
AIBS Brain Blueprint

Drift Protection

This standard protects MWMS from:

trusting confident AI language
accepting summaries with no action
saving weak course content
creating duplicate pages
routing to the wrong Brain
displaying dashboard noise
acting on unsupported claims
automating before validation exists
allowing self-approval of high-risk work
sending vague instructions to M
making financial or compliance decisions without review
treating drafts as final
losing validation history
allowing tool hype into Canon
mistaking output volume for progress
claiming execution without evidence
allowing repeated validation loops
treating retrieval as authority
allowing persistent agents to produce unchecked output
committing weak output to memory
closing sessions without save points

AI Output Validation Drift Signals

MWMS should watch for:

no validation level
no validation owner
no source evidence
stale source
no provenance
output too generic
wrong Brain
wrong parent
no destination
no business outcome
risk not classified
tool permission unclear
model unsuitable
producer self-approves high-risk output
repeated failure not counted
rescue not triggered
execution claimed without proof
outcome unverified
knowledge committed without approval
persistent output not monitored
session closed without final status

Rule

Validation drift must be corrected before output receives more authority.

Minimum Compliance Standard

An important AI output is compliant only when:

task is identified
Work Unit is known
producer is identified
source is known
source authority is assessed
source freshness is assessed
provenance is preserved
validation level is assigned
validation owner is assigned
required checks are completed
risk is classified
tool permissions are confirmed
model suitability is checked
independent review occurs where required
human approval occurs where required
failure count is tracked
rescue is triggered where required
destination is clear
business outcome is clear
execution is verified where applicable
validation is logged
knowledge commitment is controlled
closure is recorded

Architectural Intent

The architectural intent of the MWMS AI Output Validation Standard is to make MWMS trustworthy.

As MWMS grows, AI will produce more:

reports
decisions
drafts
tasks
alerts
dashboard items
recommendations
analyses
workflows
page outputs
client materials
monitoring results
tool actions
knowledge updates

The danger is not lack of output.

The danger is unvalidated output.

MWMS must be able to trust:

what enters the system
what gets routed
what gets displayed
what gets saved
what gets executed
what gets committed
what gets called complete

Validation is the control layer that makes that trust possible.

A mature MWMS validation system should be able to answer:

What output was created?
Which task did it answer?
Who or what created it?
Which model produced it?
What source was used?
Was the source authoritative?
Was it current?
What validation level applied?
Who reviewed it?
Was the reviewer independent?
What failed?
What decision was made?
Was human approval required?
Was anything executed?
Was the outcome verified?
Where did it go?
What was logged?
What knowledge was committed?
How was the work closed?

When MWMS can answer those questions consistently, it can scale AI work without losing:

quality
control
trust
safety
continuity

Strategic Summary

The v1.1 upgrade expands the MWMS AI Output Validation Standard from a general quality-control checklist into a complete operational-truth gate.

The upgraded standard now governs:

validation ownership
source authority
source freshness
provenance
model suitability
independent review
disagreement handling
deterministic failure thresholds
rescue routing
tool-execution verification
persistent-agent validation
external knowledge validation
cost validation
outcome verification
knowledge commitment
session closure

The key shift is:

AI output is not trusted because it was generated.

It is trusted only after the required evidence, review, verification and approval exist.

Final Rule

No important AI output becomes operational truth until it passes validation.

No high-risk output should be approved only by its producer.

No repeated validation failure should continue without rescue.

No tool action should be claimed without execution evidence.

No retrieved information should be treated as authoritative without source review.

No persistent agent output should bypass monitoring.

No AI output should become durable knowledge without approval.

No work should be called complete without outcome verification and closure.

The final standard is:

No evidence, no trust.

No validation, no operational use.

No independent review, no high-risk approval.

No verified execution, no completion claim.

No controlled commitment, no durable knowledge.

No closure record, no finished work.

Change Log

Version: v1.1
Date: 2026-06-17
Author: HeadOffice

Change:

Updated the MWMS AI Output Validation Standard using the AI Automations by Jack block covering multi-model review, independent validation, rescue routing, persistent agents, external knowledge systems, tool verification, cost visibility and session closure.

Added Validation And Operational Truth state separation:

Generated
Prepared
Validated
Approved
Executed
Verified
Committed
Closed

Added Validation Ownership.

Expanded AI Output Validation Levels.

Added validation dimensions for:

Source Authority
Source Freshness
Provenance
Security
Model Suitability
Independent Review
Outcome Verification
Knowledge Commitment
Closure

Expanded the Default MWMS Validation Checklist.

Added Validation Evidence Standard.

Added Independent Review Standard.

Added No-Self-Approval Rule.

Added Disagreement Handling.

Added Failure Counter And Rescue Rule.

Added Tool Execution Validation.

Added Persistent Agent Output Validation.

Added External Knowledge Validation.

Added Cost Validation.

Added Persistent Monitoring validation example.

Added Knowledge Commitment Validation Rule.

Added Session Closure Validation Rule.

Expanded Validation Logging.

Added Relationship To SIT Brain.

Added Relationship To Data Brain.

Expanded Governance Role, Drift Protection, Drift Signals, Minimum Compliance Standard, Architectural Intent, Strategic Summary and Final Rule.

Aligned this update with:

MWMS AI Agent Operations Core
MWMS Agentic Work Unit Standard
MWMS AI Employee Role Card Standard
MWMS AI Agent Orchestration Framework
MWMS AI Workflow Pipeline Standard
MWMS AI Agent Failure Handling And Escalation Protocol
MWMS Independent Model Review And Rescue Routing Framework
MWMS AI Agent Memory And Context Framework
MWMS External Knowledge Engine And Reasoning Agent Separation Framework
MWMS AI Work Session Closure And Knowledge Commitment Protocol
MWMS AI Tool Permission And Access Framework
MWMS AI Observability Metadata Standard
MWMS AI Usage And Cost Visibility Standard

Purpose of update:

To evolve the MWMS AI Output Validation Standard from a general output-quality framework into the complete operational-truth gate for evidence-based, independently reviewed, tool-verified, outcome-verified and knowledge-controlled AI output across MWMS and future AIBS client systems.

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

Change:

Created the MWMS AI Output Validation Standard as the core quality-control framework for checking AI-generated outputs before they are accepted, routed, saved, displayed, automated or acted upon.

Change Impact Declaration

This v1.1 update expands the AI Output Validation Standard from a general checklist into a complete operational-truth, independent-review, rescue, execution-verification, outcome-verification and knowledge-commitment standard.

Pages Created

None

Pages Updated

MWMS AI Output Validation Standard

Pages Deprecated

None

Standalone Pages Not Created

MWMS AI Independent Output Review Standard
MWMS Tool Execution Verification Framework
MWMS Persistent Agent Output Validation Standard
MWMS External Knowledge Validation Standard
MWMS AI Outcome Verification Framework
MWMS Validation Rescue Protocol
MWMS Session Closure Validation Standard

Registries Requiring Update

HeadOffice Page Registry
MWMS Canon Index
MWMS Course Absorption Decision Registry

Canon Version Update Required

Change Log Entry Required

Strategic Absorption Result

MWMS gains a stronger validation standard that separates generated output from validated truth, requires independent review for high-risk work, verifies claimed tool execution, stops repeated failure loops, validates persistent-agent output, protects durable knowledge and requires verified outcomes before work is called complete.

END OF FULL FILE OUTPUT