MWMS MCR

MWMS Structured Analysis And Insight Workflow 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, Opportunity System, Research Brain, Finance Brain, Experimentation Brain, Ads Brain, 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 Structured Analysis And Insight Workflow Framework.

This framework explains how MWMS turns messy information, raw data, signals, research, test results, reports, notes, and operational evidence into structured insight that can support better decisions.

MWMS will receive large amounts of information from:

  • newsletters
  • courses
  • offer pages
  • research sources
  • ad tests
  • experiment results
  • finance notes
  • Brain Room discussions
  • M development updates
  • AI Employee outputs
  • failure logs
  • outcome logs
  • routed actions
  • dashboards
  • client systems in future AIBS workflows

Information by itself is not enough.

MWMS needs a repeatable way to analyze information so that HeadOffice and the Brains can understand:

  • what happened
  • what changed
  • what matters
  • what pattern is emerging
  • what assumption is being made
  • what risk exists
  • what decision is needed
  • what action should happen next
  • what should be ignored, parked, escalated, or tested

The purpose of structured analysis is to prevent MWMS from turning raw information into vague summaries.

The goal is to turn raw information into decision-ready insight.

Scope

This framework applies to all MWMS workflows where information must be analyzed before a decision, action, report, handoff, or system update.

This includes analysis for:

  • HeadOffice intelligence
  • Newsletter Intelligence
  • Course Absorption
  • Offer Evaluation
  • Research Brain
  • Finance Brain
  • Experimentation Brain
  • Ads Brain
  • Affiliate Brain
  • Content Brain
  • Opportunity System
  • Brain Room requests
  • AI Employee reports
  • failure reviews
  • outcome reviews
  • recurring reports
  • M developer handoff preparation
  • client reporting in future AIBS workflows

This framework applies when MWMS needs to analyze:

  • data
  • text
  • trends
  • signals
  • risks
  • decisions
  • experiments
  • financial assumptions
  • market conditions
  • platform changes
  • workflow performance
  • AI Employee usefulness
  • system failures
  • client workflow information

This framework does not authorize technical development, automated decision-making, external action, paid traffic action, client delivery, or live system changes.

It defines the analysis discipline that must exist before decisions become operational.

Core Definition

Structured Analysis is the repeatable process of turning raw information into decision-ready insight.

A structured analysis workflow moves through the following core sequence:

Problem Definition → Source Review → Data Cleaning → Evidence Extraction → Pattern Detection → Interpretation → Options → Recommendation → Validation → Decision Routing

Structured analysis is not the same as summarization.

A summary says what the source contains.

Structured analysis explains what the source means and what MWMS should do with it.

A strong MWMS analysis should answer:

  • What is the question?
  • What source supports the analysis?
  • Is the source complete?
  • What facts are known?
  • What assumptions exist?
  • What patterns are visible?
  • What risks matter?
  • What options are available?
  • What decision is needed?
  • What action should happen next?

Core Principle

The core principle of this framework is:

Analysis is only valuable when it improves a decision.

MWMS should not analyze for the sake of analysis.

A structured analysis must improve at least one of the following:

  • decision quality
  • action clarity
  • risk reduction
  • prioritization
  • routing accuracy
  • experiment design
  • finance control
  • HeadOffice visibility
  • AI Employee performance
  • client reporting quality
  • system learning

If an analysis does not help MWMS decide, act, validate, route, park, reject, or learn, it is not yet useful.

Structured Analysis Workflow Stages

The MWMS Structured Analysis And Insight Workflow has ten stages:

  1. Define The Analysis Question
  2. Identify Source Material
  3. Check Source Quality
  4. Clean And Normalize Input
  5. Extract Evidence
  6. Detect Patterns
  7. Interpret Meaning
  8. Generate Options
  9. Produce Recommendation
  10. Validate And Route Decision

1. Define The Analysis Question

Every analysis must begin with a clear question.

Examples:

  • Is this course lesson worth absorbing?
  • Is this newsletter signal actionable?
  • Is this affiliate offer worth researching?
  • Is this offer ready for Finance Brain review?
  • Is this experiment result meaningful?
  • Is this failure pattern repeated?
  • Is this recurring report useful?
  • Is this developer handoff safe for M?
  • Is this client report ready for review?
  • Should this item be ACT NOW, TEST, MONITOR, PARK, or REJECT?

Analysis Question Rule:

If the question is unclear, the analysis will drift.

Before analyzing, MWMS must know what decision the analysis is meant to support.

2. Identify Source Material

The analysis must identify the source material being used.

Source material may include:

  • uploaded course file
  • transcript
  • newsletter email
  • offer page
  • research source
  • experiment record
  • finance note
  • ad result
  • screenshot
  • WordPress page list
  • Supabase row
  • Brain Room message
  • AI output
  • failure log
  • outcome log
  • client document

Source Material Rule:

Analysis must be source-grounded.

If there is no source, the analysis must be treated as opinion, assumption, or hypothesis.

3. Check Source Quality

Before analysis continues, MWMS must check source quality.

Source quality includes:

  • completeness
  • freshness
  • relevance
  • reliability
  • bias
  • conflict
  • missing data
  • source of truth
  • verification need
  • current evidence status

Source quality statuses:

  • Strong
  • Usable
  • Partial
  • Weak
  • Conflicting
  • Unverified
  • Stale
  • Unknown

Source Quality Rule:

Weak source quality must weaken confidence.

Do not produce high-confidence recommendations from weak or incomplete source material.

4. Clean And Normalize Input

Raw input must be cleaned before analysis.

Cleaning may include:

  • remove filler
  • remove duplicates
  • remove transcript noise
  • remove newsletter sponsor clutter
  • remove irrelevant examples
  • remove tool hype
  • fix broken structure
  • separate source facts from commentary
  • group related items
  • mark missing fields
  • mark assumptions
  • identify noise

Input Cleaning Rule:

Dirty input creates dirty analysis.

A structured analysis workflow should not treat messy input as clean evidence.

5. Extract Evidence

Evidence extraction identifies the useful facts, signals, claims, numbers, patterns, and decision inputs inside the source.

Evidence may include:

  • facts
  • metrics
  • dates
  • costs
  • results
  • user feedback
  • offer claims
  • vendor proof
  • experiment outcomes
  • finance assumptions
  • compliance concerns
  • workflow failures
  • repeated signals
  • source limitations

Evidence Extraction Rule:

Evidence must be separated from claims, opinions, hype, and assumptions.

For MWMS, this is especially important in:

  • offer evaluation
  • newsletter intelligence
  • course absorption
  • finance review
  • client reports
  • AI tool evaluation

6. Detect Patterns

Pattern detection identifies what the evidence may be showing.

Patterns may include:

  • repeated platform changes
  • repeated tool failures
  • repeated newsletter signals
  • recurring MCR duplicate issues
  • recurring AI output weaknesses
  • repeated offer risks
  • experiment performance patterns
  • finance exposure patterns
  • user behaviour patterns
  • content production bottlenecks
  • recurring client workflow issues

Pattern Detection Rule:

A single event may be a signal. Repeated events may be a pattern.

MWMS should not overreact to one weak signal, but it should not ignore repeated signals.

7. Interpret Meaning

Interpretation explains what the evidence and patterns mean for MWMS.

Interpretation should answer:

  • Why does this matter?
  • Which Brain is affected?
  • What system risk exists?
  • What business opportunity exists?
  • What decision is needed?
  • What action may be justified?
  • What should be monitored?
  • What should be ignored?
  • What should be parked?
  • What needs more evidence?

Interpretation Rule:

Interpretation must connect evidence to MWMS business meaning.

Do not stop at “this happened.”

Explain what it means for the system.

8. Generate Options

Structured analysis should present options where a decision is needed.

Possible option types:

  • absorb
  • update existing page
  • create new page
  • park
  • reject
  • route to Research Brain
  • route to Finance Brain
  • route to Experimentation Brain
  • send to M
  • hold until more evidence
  • run a small test
  • monitor
  • stop workflow
  • escalate to HeadOffice
  • create recurring report
  • downgrade to draft
  • block action

Options Rule:

Good analysis gives MWMS decision options, not just commentary.

For high-risk work, options should include conservative alternatives.

9. Produce Recommendation

The analysis should produce a recommendation.

Recommendation format should usually include:

  • verdict
  • reason
  • confidence level
  • risk level
  • recommended action
  • owner
  • destination
  • next step
  • what not to do

Recommended verdict formats:

  • Green / Yellow / Red
  • ACT NOW / TEST / MONITOR / PARK / REJECT
  • Proceed / Revise / Park / Reject / Escalate
  • Create / Update / Ignore / Merge / Replace
  • Allow / Draft Only / Read Only / Block / Escalate

Recommendation Rule:

A recommendation must be clear enough to act on or reject.

Vague recommendations create operational drag.

10. Validate And Route Decision

Before the analysis becomes operational, it must be validated and routed.

Validation checks:

  • source grounding
  • completeness
  • logic quality
  • assumptions
  • risk
  • Brain ownership
  • decision clarity
  • action clarity
  • human review requirement
  • handoff destination
  • logging requirement

Routing destinations may include:

  • HeadOffice review
  • MCR
  • Brain Room
  • Research Brain
  • Finance Brain
  • Experimentation Brain
  • Ads Brain
  • Affiliate Brain
  • Newsletter Queue Review
  • Routed Actions
  • Parking System
  • Rejected Archive
  • M developer handoff
  • Future Client Review

Validation And Routing Rule:

Analysis should not become action until it is validated and routed correctly.

Analysis Types

MWMS recognizes the following structured analysis types.

1. Signal Analysis

Used when analyzing external or internal signals.

Examples:

  • newsletter signal
  • platform change
  • market shift
  • tool update
  • compliance note
  • customer trend
  • AI trend

Purpose:

To decide whether the signal should be ACT NOW, TEST, MONITOR, PARK, or REJECT.

2. Offer Analysis

Used when analyzing affiliate, CPA, CPL, PPL, or digital product offers.

Examples:

  • ClickBank product
  • MaxBounty offer
  • lead generation offer
  • recurring product
  • utility offer
  • AI tool offer

Purpose:

To decide whether an offer should move to Research, Finance, Experimentation, Parking, or Rejection.

3. Experiment Analysis

Used when analyzing test results.

Examples:

  • ad test
  • landing page test
  • offer test
  • video hook test
  • audience test
  • funnel test

Purpose:

To decide whether to scale, iterate, pause, stop, or investigate.

4. Finance Analysis

Used when analyzing money, exposure, break-even, cost, and risk.

Examples:

  • ad budget
  • payout
  • EPC
  • CPC
  • CPV
  • CPA
  • break-even point
  • cash exposure
  • tool cost
  • contractor cost

Purpose:

To decide whether an action is financially safe or requires review.

5. Course Absorption Analysis

Used when analyzing course material.

Examples:

  • transcript
  • lesson
  • PDF
  • framework
  • lab
  • walkthrough

Purpose:

To decide whether to create a new page, update an existing page, park, ignore, or reject.

6. Developer Support Analysis

Used when analyzing issues for M.

Examples:

  • screenshot
  • plugin file
  • error message
  • WordPress issue
  • Supabase issue
  • Make.com issue
  • UI problem

Purpose:

To decide what M should do, what evidence is missing, and whether the handoff is safe.

7. Failure Analysis

Used when analyzing breakdowns.

Examples:

  • validation failure
  • tool failure
  • output failure
  • handoff failure
  • routing failure
  • automation failure
  • partial failure

Purpose:

To decide containment, correction, escalation, logging, and Kaizen action.

8. Outcome Analysis

Used when analyzing whether work created value.

Examples:

  • page created
  • report completed
  • task completed
  • failure fixed
  • offer rejected
  • signal routed
  • M task clarified

Purpose:

To decide whether the work produced a useful outcome or only output.

9. Operational Decision Analysis

Used when analyzing recurring decisions.

Examples:

  • continue or stop
  • approve or block
  • route or park
  • test or reject
  • escalate or downgrade
  • automate or keep manual

Purpose:

To support repeatable decision-making under operational pressure.

10. Client Business Analysis

Used for future AIBS clients.

Examples:

  • client workflow report
  • client operational data
  • client bottleneck
  • client risk
  • client weekly report

Purpose:

To create reviewed, safe, useful client-facing business insight.

Structured Analysis Record Template

Use this template when MWMS performs important analysis.

Analysis Title

Field:
Analysis Title:

Analysis Type

Field:
Analysis Type:

Recommended values:

  • Signal Analysis
  • Offer Analysis
  • Experiment Analysis
  • Finance Analysis
  • Course Absorption Analysis
  • Developer Support Analysis
  • Failure Analysis
  • Outcome Analysis
  • Operational Decision Analysis
  • Client Business Analysis

Analysis Question

Field:
Analysis Question:

Owning Brain

Field:
Owning Brain:

Supporting Brains

Field:
Supporting Brains:

Source Material

Field:
Source Material:

Source Reference

Field:
Source Reference:

Source Quality

Field:
Source Quality:

Recommended values:

  • Strong
  • Usable
  • Partial
  • Weak
  • Conflicting
  • Unverified
  • Stale
  • Unknown

Source Of Truth

Field:
Source Of Truth:

Known Facts

Field:
Known Facts:

Claims Or Assumptions

Field:
Claims Or Assumptions:

Missing Information

Field:
Missing Information:

Evidence Extracted

Field:
Evidence Extracted:

Pattern Detected

Field:
Pattern Detected:

Interpretation

Field:
Interpretation:

Options Considered

Field:
Options Considered:

Recommendation

Field:
Recommendation:

Confidence Level

Field:
Confidence Level:

Recommended values:

  • High
  • Medium
  • Low
  • Unknown

Risk Level

Field:
Risk Level:

Recommended values:

  • Low
  • Medium
  • High
  • Critical

Decision Needed

Field:
Decision Needed:

Human Review Required

Field:
Human Review Required: Yes / No

Handoff Destination

Field:
Handoff Destination:

Logging Required

Field:
Logging Required: Yes / No

Expected Outcome

Field:
Expected Outcome:

Analysis Status

Field:
Analysis Status:

Recommended values:

  • Draft
  • Ready For Review
  • Validated
  • Routed
  • Parked
  • Rejected
  • Escalated
  • Closed

Quick Use Version

Analysis Title:
Analysis Type:
Analysis Question:
Owning Brain:
Supporting Brains:
Source Material:
Source Reference:
Source Quality:
Source Of Truth:
Known Facts:
Claims Or Assumptions:
Missing Information:
Evidence Extracted:
Pattern Detected:
Interpretation:
Options Considered:
Recommendation:
Confidence Level:
Risk Level:
Decision Needed:
Human Review Required:
Handoff Destination:
Logging Required:
Expected Outcome:
Analysis Status:

Example 1: Newsletter Signal Analysis

Analysis Title:
Newsletter Signal Analysis For Google AI Shopping Behaviour Change

Analysis Type:
Signal Analysis

Analysis Question:
Should this newsletter signal be acted on, tested, monitored, parked, or rejected?

Owning Brain:
HeadOffice Brain

Supporting Brains:
Ads Brain, Affiliate Brain, Content Brain, Research Brain

Source Material:
Newsletter body and extracted insight.

Source Reference:
Sender, subject, date.

Source Quality:
Usable, subject to verification if recent platform change.

Source Of Truth:
Newsletter source for signal; current verification needed for action.

Known Facts:
Newsletter reports a platform behaviour shift.

Claims Or Assumptions:
Impact on MWMS campaigns is not yet proven.

Missing Information:
Whether the change is active in target markets and relevant to MWMS traffic strategy.

Evidence Extracted:
Signal affects discovery, search, shopping, or funnel behaviour.

Pattern Detected:
Platform-driven discovery behaviour is shifting.

Interpretation:
This may affect how MWMS evaluates future traffic and landing-page strategy.

Options Considered:
Monitor, research further, test small, route to Ads Brain, park.

Recommendation:
MONITOR / TEST if confirmed by current source.

Confidence Level:
Medium

Risk Level:
Medium

Decision Needed:
Should this be routed to Ads Brain for monitoring or small test planning?

Human Review Required:
Yes before downstream action.

Handoff Destination:
Newsletter Queue Review / Ads Brain / Parking System.

Logging Required:
Yes

Expected Outcome:
Relevant platform signal is monitored without creating dashboard noise.

Analysis Status:
Ready For Review

Example 2: Offer Analysis

Analysis Title:
Affiliate Offer Suitability Analysis

Analysis Type:
Offer Analysis

Analysis Question:
Is this offer suitable for MWMS testing?

Owning Brain:
Affiliate Brain

Supporting Brains:
Research Brain, Ads Brain, Finance Brain, Experimentation Brain, HeadOffice Brain

Source Material:
Offer page and affiliate network details.

Source Reference:
Offer name, network, payout, available metrics.

Source Quality:
Partial until payout, proof, refund risk, and traffic assumptions are verified.

Source Of Truth:
Current offer page and verified network data.

Known Facts:
Offer exists and makes specific claims.

Claims Or Assumptions:
Vendor claims may not be proven. Traffic costs may be unknown.

Missing Information:
EPC, refund rate, compliance risk, break-even cost, audience fit, competitor landscape.

Evidence Extracted:
Offer niche, mechanism, promise, funnel type, payout if available.

Pattern Detected:
Potential market demand may exist, but financial viability is unconfirmed.

Interpretation:
Offer cannot move to testing until Research and Finance review are complete.

Options Considered:
Research, Finance review, Experimentation review, park, reject.

Recommendation:
Route to Research Brain first. Do not move toward spend yet.

Confidence Level:
Medium

Risk Level:
High

Decision Needed:
Should Research Brain verify the offer?

Human Review Required:
Yes before spend.

Handoff Destination:
Research Brain / Finance Brain / Parking System.

Logging Required:
Yes

Expected Outcome:
Weak offers are stopped early; promising offers receive proper evidence review.

Analysis Status:
Ready For Review

Example 3: Course Absorption Analysis

Analysis Title:
Course Lesson Absorption Analysis

Analysis Type:
Course Absorption Analysis

Analysis Question:
Does this lesson add enough value to MWMS to create or update MCR structure?

Owning Brain:
HeadOffice Brain

Supporting Brains:
AI Business Systems Brain, Operations Brain, relevant specialist Brain.

Source Material:
Course transcript or lesson block.

Source Reference:
Course name, lesson title, file name.

Source Quality:
Mostly Complete.

Source Of Truth:
Course source for lesson content; MCR for MWMS governance.

Known Facts:
Lesson introduces a workflow or concept.

Claims Or Assumptions:
Course framing may include hype. MWMS value must be judged independently.

Missing Information:
Whether a similar page already exists.

Evidence Extracted:
Reusable framework, operational rule, workflow model, or tool practice.

Pattern Detected:
Concept reinforces or expands AI Agent Operations.

Interpretation:
If the concept adds a genuinely new capability, create a page. If it overlaps, update existing page or park.

Options Considered:
Create new page, update existing page, park, ignore, reject.

Recommendation:
Create only if it adds non-duplicative MWMS system value.

Confidence Level:
Medium to High

Risk Level:
Medium

Decision Needed:
Create, update, park, or reject.

Human Review Required:
Yes before MCR save.

Handoff Destination:
MCR / Registry Update / Parking System.

Logging Required:
Yes if absorbed.

Expected Outcome:
MWMS Brain improves without creating duplicate documentation.

Analysis Status:
Ready For Review

Example 4: Developer Support Analysis

Analysis Title:
Developer Handoff Safety Analysis

Analysis Type:
Developer Support Analysis

Analysis Question:
Can this issue be safely handed to M?

Owning Brain:
HeadOffice Brain

Supporting Brains:
Operations Brain, Dev Console, relevant technical system.

Source Material:
Screenshot, file content, current user instruction, current save point.

Source Reference:
Site, file path, screenshot, plugin file, task note.

Source Quality:
Strong only if current file path and evidence are supplied.

Source Of Truth:
Current screenshot and current file content.

Known Facts:
A development issue or change request exists.

Claims Or Assumptions:
Any missing file path or hidden system state is assumption.

Missing Information:
Exact path, current code, test steps, or live risk may be missing.

Evidence Extracted:
Visible issue, requested change, affected site or file.

Pattern Detected:
Developer risk increases when instructions are vague.

Interpretation:
If M would need to guess, do not hand off.

Options Considered:
Prepare handoff, request more evidence, park, escalate.

Recommendation:
Prepare developer brief only when exact evidence is available.

Confidence Level:
Depends on current evidence.

Risk Level:
High

Decision Needed:
Can this go to M or should more evidence be requested?

Human Review Required:
Always.

Handoff Destination:
M / Dev Console / HeadOffice Review.

Logging Required:
Yes for meaningful development work.

Expected Outcome:
M receives safe instructions or the task is paused until evidence exists.

Analysis Status:
Waiting For Evidence if incomplete.

Example 5: Failure Analysis

Analysis Title:
AI Output Failure Analysis

Analysis Type:
Failure Analysis

Analysis Question:
Why did this AI output fail and what should MWMS change?

Owning Brain:
HeadOffice Brain

Supporting Brains:
Relevant affected Brain.

Source Material:
Failed output, user correction, validation notes.

Source Reference:
Task, page title, workflow, timestamp.

Source Quality:
Usable.

Source Of Truth:
User correction and current evidence.

Known Facts:
Output failed or required correction.

Claims Or Assumptions:
Cause may need investigation.

Missing Information:
Whether failure was caused by source, instruction, context, validation, or handoff.

Evidence Extracted:
Failure type, affected workflow, user correction.

Pattern Detected:
May be recurring if similar failures happened before.

Interpretation:
Failure should become system improvement.

Options Considered:
Revise output, update skill, update validation checklist, create failure log, park.

Recommendation:
Contain, correct, revalidate, log, and update rule if repeated.

Confidence Level:
Medium

Risk Level:
Medium to High

Decision Needed:
Should this become a Kaizen improvement?

Human Review Required:
Yes for significant failures.

Handoff Destination:
Failure Log / Kaizen Digest / relevant standard update.

Logging Required:
Yes

Expected Outcome:
MWMS becomes harder to break next time.

Analysis Status:
Ready For Review

Structured Analysis Readiness Checklist

Before accepting an analysis, check:

  1. Is the analysis question clear?
  2. Is the analysis type identified?
  3. Is the owning Brain clear?
  4. Are supporting Brains listed where relevant?
  5. Is source material identified?
  6. Is source reference recorded?
  7. Is source quality assessed?
  8. Is source of truth clear?
  9. Are known facts separated from claims?
  10. Are assumptions marked?
  11. Is missing information listed?
  12. Is evidence extracted?
  13. Is pattern detection reasonable?
  14. Is interpretation connected to MWMS?
  15. Are options considered?
  16. Is recommendation clear?
  17. Is confidence level assigned?
  18. Is risk level assigned?
  19. Is decision needed stated?
  20. Is human review requirement clear?
  21. Is handoff destination known?
  22. Is logging requirement clear?
  23. Is expected outcome stated?
  24. Is the analysis actionable?
  25. Would this improve a decision?

If several answers are unclear, the analysis is not ready.

Common Structured Analysis Failure Modes

Structured analysis has failed if:

  1. It summarizes but does not interpret.
  2. It lists facts but does not support a decision.
  3. It treats claims as evidence.
  4. It hides missing information.
  5. It overstates confidence.
  6. It ignores risk.
  7. It has no recommendation.
  8. It gives a recommendation with no owner.
  9. It has no handoff destination.
  10. It does not identify source quality.
  11. It routes work to the wrong Brain.
  12. It creates action from weak evidence.
  13. It ignores finance or compliance risk.
  14. It creates output but no outcome.
  15. It does not improve a decision.

Manual Use Rule

Structured analysis should be manually proven before it becomes automated.

Manual use helps MWMS learn:

  • which analysis questions repeat
  • which source types are reliable
  • which source types are noisy
  • which analysis formats help decisions
  • which recommendations are useful
  • which recommendations are too vague
  • which Brains need specialist analysis
  • which analysis outputs may later become reports, dashboards, or AI Employee workflows

Manual analysis discipline comes before dashboards, recurring reports, or automated decision support.

Future Plugin Or UI Relevance

This framework may later support:

  • Structured Analysis Registry
  • HeadOffice analysis dashboard
  • AI Manager analysis workflow
  • Research Brain analysis queue
  • Finance Brain analysis queue
  • Experimentation Brain result analysis panel
  • Offer evaluation analysis report
  • Newsletter signal analysis workflow
  • AI Employee analysis role templates
  • AIBS client analysis workflow

Possible future fields:

  • analysis_id
  • analysis_title
  • analysis_type
  • analysis_question
  • owning_brain
  • supporting_brains
  • source_material
  • source_reference
  • source_quality
  • source_of_truth
  • known_facts
  • claims_or_assumptions
  • missing_information
  • evidence_extracted
  • pattern_detected
  • interpretation
  • options_considered
  • recommendation
  • confidence_level
  • risk_level
  • decision_needed
  • human_review_required
  • handoff_destination
  • logging_required
  • expected_outcome
  • analysis_status
  • created_at
  • updated_at

No technical build is authorized by this framework alone.

Governance Role

HeadOffice owns the MWMS Structured Analysis And Insight Workflow Framework.

HeadOffice is responsible for:

  • defining structured analysis standards
  • ensuring analysis supports decisions
  • preventing vague summaries from becoming operational intelligence
  • ensuring claims are separated from evidence
  • ensuring assumptions are marked
  • ensuring source quality affects confidence
  • ensuring risk and confidence are visible
  • ensuring recommendations have owners and destinations
  • ensuring high-risk analysis receives human review
  • protecting M’s active build areas
  • protecting MCR source of truth
  • protecting future AIBS client analysis quality

Individual Brains may run local analysis workflows, but HeadOffice governs cross-Brain, high-risk, finance-related, developer-related, client-facing, and automation-related analysis.

Relationship To Other MWMS Standards

This framework supports and must align with:

  • 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 Multi Agent Role Design Framework
  • MWMS AI Exchange Zone And Dependency Control Framework
  • MWMS AI Ambiguity And Partial Failure Containment Framework
  • MWMS AI Agent Skill Library Framework
  • MWMS Agentic Reporting Standard
  • MWMS Agentic Reporting Template
  • MWMS AI Output Validation Standard
  • MWMS AI Output Validation Checklist
  • MWMS AI Agent Outcome Measurement Framework
  • MWMS AI Agent Outcome Log Record
  • MWMS AI Agent Failure Handling And Escalation Protocol
  • MWMS AI Agent Failure Log Record
  • MWMS Brain Routing Rule
  • MWMS Brain To Brain Request Protocol
  • MWMS Supabase Event Schema
  • AI Business Systems Brain Blueprint

This framework adds the structured analysis and insight layer to the MWMS AI Agent Operations Core.

Drift Protection

This framework protects MWMS from:

  1. Treating summaries as analysis
  2. Treating claims as evidence
  3. Making decisions from weak source material
  4. Ignoring missing information
  5. Hiding assumptions
  6. Overstating confidence
  7. Creating recommendations without owners
  8. Routing work without decision logic
  9. Creating reports that do not improve decisions
  10. Acting on single weak signals without pattern review
  11. Skipping Finance Brain where money is involved
  12. Skipping Research Brain where evidence is missing
  13. Skipping human review where risk is high
  14. Creating client-facing insights without validation
  15. Measuring analysis volume instead of decision quality

Any analysis that does not improve a decision should be revised, parked, or rejected.

Architectural Intent

The architectural intent of the MWMS Structured Analysis And Insight Workflow Framework is to make MWMS a decision-intelligence system.

MWMS will collect more information than any one person can manually process.

The value is not in collecting information.

The value is in turning information into structured insight that improves decisions.

The long-term goal is that every important MWMS analysis can answer:

  • What question are we answering?
  • What source are we using?
  • How good is the source?
  • What facts are known?
  • What assumptions exist?
  • What is missing?
  • What pattern is visible?
  • What does it mean?
  • What options are available?
  • What is the recommendation?
  • How confident are we?
  • What risk exists?
  • What decision is needed?
  • Where does this go next?

When MWMS can answer these questions consistently, the ecosystem becomes smarter, safer, and more operationally useful.

Change Log

v1.0 — Initial Draft

Created the MWMS Structured Analysis And Insight Workflow Framework to define how MWMS turns raw data, messy evidence, signals, research, experiment results, finance assumptions, AI outputs, failures, and operational information into decision-ready insight.

This framework establishes structured analysis stages, analysis types, analysis record templates, examples, readiness checks, failure modes, future plugin/UI relevance, governance role, drift protection, and architectural intent.

It establishes that analysis is only valuable when it improves a decision.