MWMS AI Schema And Decision Ready Output Framework

System: MWMS
Document Type: Framework
Authority Level: MCR Source Of Truth
Status: Draft For MCR
Primary Location: MCR
Future Operational Destination: HeadOffice Brain, MWMS Brain, Brain Room, AI Manager, AI Employee Router, Task Executor Systems, Newsletter Intelligence, Routed Actions, HeadOffice Dashboard, Opportunity System, Supabase Records, Make.com Workflows, AI Business Systems Brain
Parent Page: HeadOffice
Owner: Martyn
Developer Boundary: Do Not Touch M’s Active Build Areas Unless Specifically Assigned
Source Of Truth: MCR

Purpose

The purpose of this document is to define the MWMS AI Schema And Decision Ready Output Framework.

This framework explains how MWMS controls AI-generated outputs that need to become structured records, JSON, reports, dashboard items, queue items, decision records, task records, Supabase rows, Make.com payloads, or future client-facing data.

MWMS cannot rely on AI output simply because it sounds fluent.

AI can write persuasive text, but MWMS systems need structured, consistent, validated, machine-usable output.

This framework exists to ensure AI outputs are:

clean
structured
schema-safe
field-complete
decision-ready
validation-ready
dashboard-ready
routing-ready
automation-safe
human-reviewable
loggable
comparable over time

The goal is not just better writing.

The goal is better operational intelligence.

Scope

This framework applies to any MWMS workflow where AI output must become structured data, a record, a dashboard item, a decision, a task, a report, or a workflow input.

This includes:

Newsletter Intelligence records
Routed Actions
Brain Room task conversion
Agentic Work Units
AI Manager task records
Task Executor records
Supabase inserts
Make.com JSON payloads
n8n workflow payloads
MCR page metadata
Course Absorption records
Structured Analysis records
Operational Decision records
Forecasting and Scenario Planning records
Permission Gatekeeper records
Failure Logs
Outcome Logs
Recurring Reports
KPI dashboards
Finance summaries
Experimentation summaries
Offer Evaluation records
future AIBS client reports
future client dashboard records

This framework applies before any AI output is written into a system, sent into a workflow, displayed on a dashboard, routed to another Brain, handed to M, used in reporting, or considered for automation.

This framework does not authorize technical development, database changes, schema changes, automation, or developer implementation by itself.

It defines the governance standard for schema-safe and decision-ready AI outputs.

Core Definition

An AI Schema is a defined structure that tells AI what fields, formats, values, and rules an output must follow.

A Decision Ready Output is an AI-generated output that contains enough structured information for MWMS to decide, route, validate, log, or act.

A decision-ready output should clearly define:

what the item is
where it came from
what it means
what decision is needed
what action is recommended
who owns the next step
what risk exists
what confidence level applies
what validation is required
what status applies
what should happen next

A schema controls structure.

Decision readiness controls usefulness.

MWMS needs both.

Core Principle

The core principle of this framework is:

AI output is not operationally useful until it is structured, validated, and decision-ready.

A beautifully written AI answer may still be useless to MWMS if it cannot be:

parsed
routed
stored
compared
validated
reviewed
displayed
actioned
audited

MWMS must therefore treat schema design as a core operating discipline.

The stronger the schema, the more reliable the workflow.

The weaker the schema, the more fragile the automation, dashboard, or decision layer becomes.

Schema And Decision Ready Output Workflow

The MWMS schema workflow has ten stages:

Define Output Purpose
Define Destination
Define Required Fields
Define Allowed Values
Define Field Rules
Generate Structured Output
Validate Output
Handle Missing Or Invalid Fields
Route Or Store Output
Log And Improve Schema

1. Define Output Purpose

Every structured output must have a clear purpose.

Examples:

create a newsletter intelligence record
create an Agentic Work Unit
create a failure log
create an outcome log
create a routed action
create a dashboard card
create a decision record
create a scenario planning record
create a client report draft
create a Supabase row
create a Make.com JSON payload

Output Purpose Rule:

Do not create structured output unless MWMS knows what the output is for.

A schema without a purpose creates fields that look organized but do not support decisions.

2. Define Destination

The destination determines the schema.

Possible destinations:

Supabase table
Make.com module
n8n workflow
WordPress/MCR page
Brain Room
AI Manager
Task Executor
HeadOffice Dashboard
Newsletter Queue Review
Routed Actions
Parking System
Failure Log
Outcome Log
Recurring Report
KPI Dashboard
future client dashboard
future client report

Destination Rule:

Output format must match where the output is going.

A dashboard card, Supabase row, MCR page, developer brief, and client report do not need the same schema.

3. Define Required Fields

Every schema must define required fields.

Required fields are the fields without which the output cannot be safely used.

Examples:

For a newsletter intelligence record:

headline
extracted_insight
primary_brain
action_type
priority
urgency
recommended_action
status

For a decision record:

decision_title
decision_type
source_quality
decision_made
decision_reason
risk_level
owner_next_action
decision_status

For a developer handoff:

site
issue
current_evidence
exact_change
what_not_to_touch
test_steps
expected_result
risk_level

Required Field Rule:

Missing required fields should stop the output from becoming operational.

4. Define Allowed Values

Where possible, schemas must use controlled values.

Controlled values make outputs easier to route, filter, validate, report, and compare.

Examples:

Priority

Low
Medium
High
Critical

Urgency

Low
Medium
High
Immediate

Action Type

ACT NOW
TEST
MONITOR
PARK
REJECT

Status

Draft
Ready For Review
Reviewed
Routed
Parked
Rejected
Completed
Escalated

Risk Level

Low
Medium
High
Critical

Confidence Level

High
Medium
Low
Unknown

Allowed Value Rule:

Use controlled values wherever the field affects routing, filtering, dashboard display, or decision-making.

Free text is useful for explanation.

Controlled values are useful for operations.

5. Define Field Rules

Every important field should have clear rules.

Field rules may define:

required or optional
text or number
allowed values
max length
default value
null handling
confidence requirement
validation requirement
human review requirement
routing consequence
dashboard visibility
logging requirement

Example:

priority must be one of:

Low
Medium
High
Critical

If priority is Critical, human review is required.

Example:

recommended_action must not be empty.

If recommended_action is vague, the item must be returned for revision.

Field Rule:

A field should not exist unless MWMS knows how to interpret it.

6. Generate Structured Output

Only after purpose, destination, required fields, allowed values, and field rules are known should AI generate structured output.

The output should be:

clear
complete
schema-matched
field-labelled
free from unnecessary prose
compatible with validation
compatible with routing
compatible with storage or display

Structured Output Rule:

Ask AI for the exact structure required, not a general answer.

For JSON workflows, AI should produce valid JSON only.

For MCR pages, AI should produce full page output.

For reports, AI should produce the required report format.

For dashboard items, AI should produce concise dashboard-ready fields.

7. Validate Output

Structured output must be validated before use.

Validation checks:

all required fields present
field names correct
allowed values used
no malformed structure
no unsupported claims
no missing recommendation
no vague action
no invalid priority
no wrong Brain routing
no missing owner
no missing status
no missing risk level
no hallucinated source
no schema drift

Validation Rule:

Structured output is not trusted until it passes validation.

A response can be well-structured and still be wrong.

8. Handle Missing Or Invalid Fields

When fields are missing or invalid, MWMS must not force the output forward.

Possible handling decisions:

revise
return for regeneration
mark as draft
route to human review
park
reject
escalate
log failure
request missing source data
downgrade action level

Missing Field Rule:

Missing required fields should trigger containment, not guessing.

If the AI does not know a field, it should mark it as unknown rather than inventing it.

9. Route Or Store Output

After validation, the output may be routed or stored.

Possible destinations:

Supabase
WordPress/MCR
Brain Room
HeadOffice Dashboard
Newsletter Queue Review
Routed Actions
Parking System
Failure Log
Outcome Log
Research Brain
Finance Brain
Experimentation Brain
Ads Brain
M developer handoff
future client review queue

Routing Rule:

Only validated output should move into operational systems.

Draft output may move to review.

Unvalidated output must not become operational truth.

10. Log And Improve Schema

Schemas must improve over time.

Schema improvement may be needed when:

fields are often missing
allowed values are unclear
dashboard filtering fails
Make.com parsing breaks
Supabase insert fails
AI gives vague recommendations
routing is inconsistent
reports are not useful
validation repeatedly fails
users correct the same field often

Schema Improvement Rule:

Repeated output failure should improve the schema, not just the prompt.

Validation Sandwich Model

MWMS uses the Validation Sandwich model for structured AI output.

The model is:

Instruction → Schema → AI Output → Validation → Decision

1. Instruction Layer

The instruction tells AI what task to perform.

Example:

“Analyze this newsletter and extract business-relevant MWMS intelligence.”

2. Schema Layer

The schema tells AI exactly what fields and values to produce.

Example:

headline
extracted_insight
primary_brain
supporting_brains
action_type
priority
urgency
recommended_action
confidence
status

3. AI Output Layer

AI produces structured output matching the schema.

4. Validation Layer

MWMS checks whether the output is complete, valid, grounded, and usable.

5. Decision Layer

The output is accepted, revised, parked, rejected, escalated, routed, or logged.

Validation Sandwich Rule:

AI should be guided before output and checked after output.

Do not rely on post-validation alone if the instruction and schema were weak.

Schema Types

MWMS recognizes the following schema types.

1. Intake Schema

Used when raw information enters MWMS.

Examples:

course upload intake
newsletter intake
offer intake
Brain Room task intake
client document intake

Purpose:

To capture source, type, owner, status, and required next step.

2. Analysis Schema

Used when turning evidence into insight.

Examples:

structured analysis record
offer analysis
finance analysis
experiment analysis
failure analysis

Purpose:

To capture question, source quality, evidence, assumptions, interpretation, recommendation, confidence, and risk.

3. Decision Schema

Used when recording an operational decision.

Examples:

ACT NOW / TEST / MONITOR
route / park / reject
approve / block / downgrade
continue / pause / stop
create / update / replace

Purpose:

To capture decision type, reason, owner, next action, review trigger, and expected outcome.

4. Dashboard Schema

Used when output will appear in dashboards.

Examples:

HeadOffice dashboard card
Routed Actions item
Newsletter signal card
KPI panel
risk alert
action summary

Purpose:

To capture concise, readable, decision-ready information.

5. Reporting Schema

Used for recurring reports and summaries.

Examples:

Weekly HeadOffice Report
Kaizen Digest
AI Employee Outcome Review
Newsletter Intelligence Digest
AIBS Client Weekly Report

Purpose:

To structure repeated reports consistently.

6. Tool Call Schema

Used when AI interacts with tools or workflows.

Examples:

Gmail search/read payload
Supabase insert payload
Make.com JSON payload
WordPress page record
task creation payload

Purpose:

To prevent malformed tool actions.

7. Permission Schema

Used when checking whether actions are allowed.

Examples:

Permission Gatekeeper record
Tool Permission Record
action approval record
controlled write review

Purpose:

To capture permission status, risk, human approval, logging, and decision.

8. Failure Schema

Used when something goes wrong.

Examples:

AI Agent Failure Log
partial failure record
validation failure record
tool failure record

Purpose:

To capture failure type, severity, containment, correction, escalation, and learning.

9. Outcome Schema

Used when measuring whether work created value.

Examples:

AI Agent Outcome Log
task completion outcome
report usefulness outcome
MCR update outcome

Purpose:

To measure usefulness instead of output volume.

10. Client Schema

Used in future AIBS client systems.

Examples:

client report record
client workflow record
client action summary
client review record

Purpose:

To keep client data structured, isolated, reviewed, and safe.

Decision Ready Output Requirements

An output is decision-ready when it contains enough information for MWMS to decide what happens next.

A decision-ready output should include:

Source
Source quality
Brain ownership
Summary or insight
Business meaning
Recommended action
Risk level
Confidence level
Owner
Status
Handoff destination
Human review requirement
Validation status
Next action

Decision Ready Rule:

If the output does not make the next decision easier, it is not decision-ready.

Schema Record Template

Use this template when defining a schema.

Schema Name

Field:
Schema Name:

Schema Type

Field:
Schema Type:

Recommended values:

Intake Schema
Analysis Schema
Decision Schema
Dashboard Schema
Reporting Schema
Tool Call Schema
Permission Schema
Failure Schema
Outcome Schema
Client Schema

Schema Purpose

Field:
Schema Purpose:

Owning Brain

Field:
Owning Brain:

Destination System

Field:
Destination System:

Examples:

Supabase
WordPress/MCR
Brain Room
AI Manager
Task Executor
Make.com
n8n
Dashboard
Report
Human Review Queue
Client Review Queue

Required Fields

Field:
Required Fields:

Optional Fields

Field:
Optional Fields:

Controlled Value Fields

Field:
Controlled Value Fields:

Allowed Values

Field:
Allowed Values:

Field Rules

Field:
Field Rules:

Default Values

Field:
Default Values:

Missing Field Handling

Field:
Missing Field Handling:

Validation Requirement

Field:
Validation Requirement:

Human Review Requirement

Field:
Human Review Requirement:

Routing Rules

Field:
Routing Rules:

Failure Handling

Field:
Failure Handling:

Logging Requirement

Field:
Logging Requirement:

Schema Status

Field:
Schema Status:

Recommended values:

Proposed
Draft
Manual Use
Proven Manual Use
Assisted Use
Controlled Automation Candidate
Active
Parked
Deprecated
Retired

Last Reviewed

Field:
Last Reviewed:

Quick Use Version

Schema Name:
Schema Type:
Schema Purpose:
Owning Brain:
Destination System:
Required Fields:
Optional Fields:
Controlled Value Fields:
Allowed Values:
Field Rules:
Default Values:
Missing Field Handling:
Validation Requirement:
Human Review Requirement:
Routing Rules:
Failure Handling:
Logging Requirement:
Schema Status:
Last Reviewed:

Example 1: Newsletter Intelligence Record Schema

Schema Name:
Newsletter Intelligence Record Schema

Schema Type:
Intake Schema / Analysis Schema / Dashboard Schema

Schema Purpose:
Capture business-relevant newsletter intelligence in a structured format.

Owning Brain:
HeadOffice Brain

Destination System:
Supabase / Newsletter Queue Review / HeadOffice Dashboard candidate

Required Fields:

headline
source_name
source_date
extracted_insight
underlying_pattern
primary_brain
supporting_brains
action_type
priority
urgency
recommended_action
confidence
status

Optional Fields:

source_url
compliance_flag
monetization_relevance
tool_relevance
notes
review_owner

Controlled Value Fields:

action_type
priority
urgency
confidence
status

Allowed Values:

action_type: ACT NOW, TEST, MONITOR, PARK, REJECT
priority: Low, Medium, High, Critical
urgency: Low, Medium, High, Immediate
confidence: High, Medium, Low, Unknown
status: Draft, Ready For Review, Reviewed, Routed, Parked, Rejected

Field Rules:

action_type must not be ACT NOW unless recommended_action is specific.
priority must not be Critical without human review.
extracted_insight must describe a business-relevant signal, not generic news.
primary_brain must be one MWMS Brain.
supporting_brains may include multiple Brains.

Default Values:

confidence: Unknown if unclear
status: Ready For Review
priority: Medium unless evidence supports higher or lower

Missing Field Handling:

If required fields are missing, send to revision or park.

Validation Requirement:
Operational Validation.

Human Review Requirement:
Required before downstream action.

Routing Rules:
Route to Queue Review first. Do not create Routed Action automatically unless reviewed.

Failure Handling:
If output is vague, generic, or malformed, log failure or revise.

Logging Requirement:
Required.

Schema Status:
Manual Use / Assisted Use

Last Reviewed:
YYYY-MM-DD

Example 2: Operational Decision Record Schema

Schema Name:
Operational Decision Record Schema

Schema Type:
Decision Schema

Schema Purpose:
Capture recurring MWMS operational decisions in a structured format.

Owning Brain:
HeadOffice Brain

Destination System:
Decision Log / Routed Actions / HeadOffice Review

Required Fields:

decision_title
decision_type
decision_question
owning_brain
source_quality
evidence_summary
options_considered
risk_level
decision_made
decision_reason
confidence_level
owner_next_action
next_action
decision_status

Optional Fields:

supporting_brains
assumptions
validation_required
human_review_required
handoff_destination
review_trigger
expected_outcome

Controlled Value Fields:

decision_type
source_quality
risk_level
confidence_level
decision_status

Allowed Values:

source_quality: Strong, Usable, Partial, Weak, Conflicting, Unverified, Stale, Unknown
risk_level: Low, Medium, High, Critical
confidence_level: High, Medium, Low, Unknown
decision_status: Draft, Ready For Review, Approved, Routed, Parked, Rejected, Escalated, Completed, Reversed, Retired

Field Rules:

risk_level High or Critical requires human_review_required = Yes.
decision_made must be specific.
owner_next_action must not be empty.
source_quality must affect confidence_level.

Default Values:

confidence_level: Unknown if source quality is weak
decision_status: Draft

Missing Field Handling:

If owner_next_action or decision_made is missing, record cannot be routed.

Validation Requirement:
Structured Validation.

Human Review Requirement:
Required for medium/high-risk decisions.

Routing Rules:
Route to HeadOffice Review, assigned Brain, or Parking System.

Failure Handling:
If decision is vague, send back for revision.

Logging Requirement:
Required for important decisions.

Schema Status:
Manual Use

Last Reviewed:
YYYY-MM-DD

Example 3: Agentic Work Unit Schema

Schema Name:
Agentic Work Unit Schema

Schema Type:
Intake Schema / Task Schema

Schema Purpose:
Convert raw requests into structured AI work units.

Owning Brain:
HeadOffice Brain

Destination System:
Brain Room / AI Manager / Task Executor candidate

Required Fields:

work_unit_title
source_request
task_type
owning_brain
assigned_ai_employee
required_context
required_input
expected_output
risk_level
validation_requirement
human_review_requirement
status

Optional Fields:

supporting_brains
related_standards
tool_permission_boundary
handoff_destination
stop_conditions
due_date
owner

Controlled Value Fields:

task_type
risk_level
validation_requirement
status

Allowed Values:

risk_level: Low, Medium, High, Critical
status: Draft, Ready For Review, Assigned, In Progress, Waiting For Input, Waiting For Validation, Completed, Parked, Rejected, Escalated

Field Rules:

high-risk work requires human review.
assigned_ai_employee must match task_type.
required_context must not be empty.
expected_output must be defined.

Default Values:

status: Draft
human_review_requirement: Yes if risk is High or Critical

Missing Field Handling:

If assigned_ai_employee or required_context is missing, route to clarification.

Validation Requirement:
Operational Validation.

Human Review Requirement:
Required for medium/high-risk work.

Routing Rules:
Route to AI Manager or HeadOffice Review only after required context exists.

Failure Handling:
If task is unclear, park or request clarification.

Logging Requirement:
Required if task is created.

Schema Status:
Manual Use / Future Automation Candidate

Last Reviewed:
YYYY-MM-DD

Example 4: Dashboard Insight Card Schema

Schema Name:
Dashboard Insight Card Schema

Schema Type:
Dashboard Schema

Schema Purpose:
Create concise, decision-ready dashboard items.

Owning Brain:
HeadOffice Brain

Destination System:
HeadOffice Dashboard / Brain Dashboard

Required Fields:

card_title
card_type
insight_summary
business_meaning
recommended_action
owner
priority
urgency
risk_level
status

Optional Fields:

source_reference
supporting_brains
confidence_level
due_date
review_required
related_record_id

Controlled Value Fields:

card_type
priority
urgency
risk_level
status

Allowed Values:

card_type: ACT NOW, TEST, MONITOR, RISK, BLOCKER, DECISION NEEDED, OUTCOME, FAILURE
priority: Low, Medium, High, Critical
urgency: Low, Medium, High, Immediate
risk_level: Low, Medium, High, Critical
status: Draft, Ready For Review, Active, Parked, Completed, Rejected

Field Rules:

card_title must be short.
insight_summary must be readable within seconds.
recommended_action must be specific.
owner must not be empty for active cards.
high urgency requires review.

Default Values:

status: Draft
priority: Medium unless evidence supports otherwise

Missing Field Handling:

If recommended_action or owner is missing, do not display as active dashboard item.

Validation Requirement:
Dashboard Readiness Validation.

Human Review Requirement:
Required for ACT NOW, RISK, BLOCKER, and DECISION NEEDED cards.

Routing Rules:
Dashboard candidate first, active dashboard only after review.

Failure Handling:
If vague, return to revision or park.

Logging Requirement:
Required for active dashboard items.

Schema Status:
Draft / Manual Use

Last Reviewed:
YYYY-MM-DD

Example 5: Permission Gatekeeper Schema

Schema Name:
Permission Gatekeeper Record Schema

Schema Type:
Permission Schema

Schema Purpose:
Check whether a requested AI action is allowed.

Owning Brain:
HeadOffice Brain

Destination System:
Permission Log / AI Manager / Task Executor candidate

Required Fields:

permission_check_title
requesting_role_or_system
requested_action
action_type
tool_or_system
workflow
approved_permission_level
requested_permission_level
risk_level
validation_status
human_approval_required
stop_conditions_triggered
gatekeeper_decision
decision_reason
status

Optional Fields:

source_material
source_completeness
context_readiness
logging_destination
owner_next_action
next_action

Controlled Value Fields:

action_type
risk_level
validation_status
human_approval_required
gatekeeper_decision
status

Allowed Values:

gatekeeper_decision: Allow, Allow As Draft Only, Allow Read Only, Require Human Review, Require Validation First, Downgrade, Park, Block, Escalate, Log Failure
risk_level: Low, Medium, High, Critical

Field Rules:

High or Critical risk cannot be allowed without human review.
Requested permission must not exceed approved permission.
Blocked or downgraded decisions must include decision_reason.
stop_conditions_triggered must be checked before allow.

Default Values:

status: Draft
gatekeeper_decision: Require Validation First if unclear

Missing Field Handling:

If requested_action, permission level, or risk level is missing, block or park.

Validation Requirement:
High Risk Validation when action affects tools, writes, MCR, M, money, clients, or public output.

Human Review Requirement:
Required for high-risk actions.

Routing Rules:
Allowed actions proceed only within permission boundary. Blocked actions go to review or failure log.

Failure Handling:
If AI attempted an unauthorized action, log failure.

Logging Requirement:
Required.

Schema Status:
Manual Use / Future Automation Candidate

Last Reviewed:
YYYY-MM-DD

Schema Validation Checklist

Before accepting structured AI output, check:

Is the output purpose clear?
Is the destination system known?
Are all required fields present?
Are field names correct?
Are allowed values used?
Are free-text fields specific enough?
Are risk and confidence included where needed?
Is source quality included where needed?
Is recommended action specific?
Is owner or next action included?
Is status included?
Is human review requirement included?
Is validation status included?
Is the output grounded in source material?
Are assumptions marked?
Are missing fields handled safely?
Is malformed output rejected or revised?
Is dashboard output concise?
Is JSON valid where JSON is required?
Is the output ready for routing, storage, review, or action?

If several answers are unclear, the output is not schema-ready.

Common Schema And Output Failure Modes

Schema and decision-ready output has failed if:

AI output sounds good but cannot be parsed.
JSON is malformed.
Required fields are missing.
Field names change between runs.
Allowed values are not respected.
Priority or urgency is inflated.
Recommended action is vague.
Owner is missing.
Status is missing.
Source quality is missing.
Confidence level is missing.
Human review requirement is missing.
The output cannot be routed.
Dashboard item is too wordy.
Supabase insert fails.
Make.com parser fails.
AI invents missing values.
Structured output bypasses validation.
Output is stored but not useful.
Schema is not updated after repeated failures.

Manual Use Rule

Schemas should be manually proven before they are used in automation.

Manual use helps MWMS learn:

which fields are essential
which fields are noise
which allowed values work
which fields AI often misses
which schemas break Make.com or Supabase
which dashboard fields help HeadOffice decide
which records are useful over time
which outputs need human review
which schemas may later become database tables
which schemas should be parked or retired

Manual schema discipline comes before automated writes, dashboards, reports, or Task Executor integration.

Future Plugin Or UI Relevance

This framework may later support:

Schema Registry
AI output schema validator
Make.com JSON validation rules
Supabase schema design
Brain Room task schema
AI Manager task schema
Task Executor payload schema
HeadOffice Dashboard card schema
Newsletter Intelligence schema controls
Routed Actions schema controls
Operational Decision Log schema
AIBS client report schema
client dashboard schema

Possible future fields:

schema_id
schema_name
schema_type
schema_purpose
owning_brain
destination_system
required_fields
optional_fields
controlled_value_fields
allowed_values
field_rules
default_values
missing_field_handling
validation_requirement
human_review_requirement
routing_rules
failure_handling
logging_requirement
schema_status
last_reviewed
created_at
updated_at

No technical build is authorized by this framework alone.

Governance Role

HeadOffice owns the MWMS AI Schema And Decision Ready Output Framework.

HeadOffice is responsible for:

defining schema governance
preventing malformed AI output from entering systems
ensuring required fields are known
ensuring allowed values are controlled
ensuring decision-ready fields are present
ensuring AI output is validated before operational use
ensuring schemas support routing and reporting
ensuring schema failures become Kaizen improvements
protecting Make.com, Supabase, dashboards, Brain Room, AI Manager, Task Executor, MCR, and future client systems from bad structured output
deciding when schemas are ready for technical implementation

Individual Brains may propose local schemas, but HeadOffice governs cross-Brain, database-related, dashboard-related, automation-related, MCR-related, developer-related, and client-facing schemas.

Relationship To Other MWMS Standards

This framework supports and must align with:

MWMS Structured Analysis And Insight Workflow Framework
MWMS Operational Decision Intelligence Framework
MWMS Forecasting And Scenario Planning Framework
MWMS AI Agent Operations Core
MWMS AI Context Routing Framework
MWMS AI Permission Gatekeeper Framework
MWMS AI Command And Trigger Framework
MWMS Recurring Intelligence And Reporting Pipeline Framework
MWMS AI Documentation Automation Pipeline Framework
MWMS AI Output Validation Standard
MWMS AI Output Validation Checklist
MWMS Agentic Reporting Standard
MWMS Agentic Reporting Template
MWMS AI Agent Failure Handling And Escalation Protocol
MWMS AI Agent Failure Log Record
MWMS AI Agent Outcome Measurement Framework
MWMS AI Agent Outcome Log Record
MWMS AI Tool Permission And Access Framework
MWMS AI Tool Permission Record Template
MWMS Supabase Event Schema
MWMS Brain Routing Rule
MWMS Brain To Brain Request Protocol
AI Business Systems Brain Blueprint

This framework adds the schema-safe and decision-ready output layer to the MWMS AI Agent Operations Core.

Drift Protection

This framework protects MWMS from:

Accepting fluent AI text as operational output
Sending malformed JSON into Make.com
Writing incomplete records into Supabase
Creating dashboard items with missing owners
Allowing vague recommended actions
Letting fields change between runs
Inflating priority and urgency
Missing human review requirements
Routing records without decision status
Creating reports that cannot be compared over time
Allowing AI to invent missing field values
Letting schema errors become automation failures
Creating dashboards from dirty records
Creating client reports from unvalidated schema output
Treating structured formatting as proof of correctness

Any structured output that fails schema validation should remain draft, be revised, parked, rejected, escalated, or logged as a failure.

Architectural Intent

The architectural intent of the MWMS AI Schema And Decision Ready Output Framework is to make AI output operationally usable.

MWMS is not just asking AI to write.

MWMS is building systems that need clean, structured, reliable intelligence.

The long-term goal is that every important AI output can answer:

What schema does this follow?
What destination is it for?
Are required fields complete?
Are allowed values respected?
Is the source clear?
Is source quality stated?
Is risk visible?
Is confidence visible?
Is the recommendation specific?
Is the owner clear?
Is the status clear?
Is human review required?
Has the output been validated?
Can this be routed, stored, displayed, or acted upon safely?

When MWMS controls schema and decision-ready output, it can move from chat-based work into reliable workflows, dashboards, reports, and future automation without losing control.

Change Log

v1.0 — Initial Draft

Created the MWMS AI Schema And Decision Ready Output Framework to define how MWMS governs structured AI output, schemas, required fields, allowed values, field rules, validation, missing field handling, routing, storage, dashboard readiness, reporting readiness, and decision-ready output.

This framework establishes the Validation Sandwich model, schema types, decision-ready output requirements, schema record templates, examples, validation checks, failure modes, future plugin/UI relevance, governance role, drift protection, and architectural intent.

It establishes that AI output is not operationally useful until it is structured, validated, and decision-ready.