MWMS AI Ambiguity And Partial Failure Containment Framework

System: MWMS
Document Type: Framework
Authority Level: MCR Source Of Truth
Status: Draft For MCR
Version: v1.1
Primary Location: MCR
Future Operational Destination: HeadOffice Brain, MWMS Brain, Brain Room, AI Manager, AI Employee Router, Task Executor Systems, Course Absorption System, Newsletter Intelligence, Opportunity System, Automation Brain, AIBS Brain
Parent Page: HeadOffice
Owner: Martyn
Developer Boundary: Do Not Touch M’s Active Build Areas Unless Specifically Assigned
Source Of Truth: MCR
Last Reviewed: 2026-06-17
Source / Origin: MWMS AI Ambiguity And Partial Failure Containment Framework v1.0 + AI Automations by Jack — Multi-Agent Orchestration, Independent Review, Rescue Routing, Persistent Agents, External Knowledge, Tool Verification And Session Closure Block
MWMS Classification: AI Ambiguity Framework / Partial Failure Containment Standard / Graceful Degradation Framework / Hallucination Cascade Protection Layer
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 AI Agent Orchestration Framework, MWMS AI Workflow Pipeline Standard, MWMS AI Output Validation Standard, MWMS AI Agent Failure Handling And Escalation Protocol, MWMS Independent Model Review And Rescue Routing Framework, MWMS AI Employee Handoff Protocol, MWMS AI Agent Memory And Context Framework, MWMS External Knowledge Engine And Reasoning Agent Separation Framework, MWMS AI Tool Permission And Access Framework, MWMS AI Observability Metadata Standard, MWMS AI Work Session Closure And Knowledge Commitment Protocol, MWMS Messy Input Normalization Framework, MWMS AI Agent Outcome Measurement Framework, MWMS AI Agent Skill Library Framework
Source Evidence: The existing framework defines how MWMS detects ambiguity, controls partial workflow failure, degrades safely, prevents hallucination cascades and stops uncertain work from contaminating downstream systems. The newly absorbed course material strengthens it with confidence-state separation, failure counters, deterministic rescue thresholds, independent review, source-provenance controls, persistent-agent containment, tool-execution verification, recovery checkpoints, knowledge-commitment controls and session closure.

Purpose

The purpose of this document is to define the MWMS AI Ambiguity And Partial Failure Containment Framework.

This framework explains how MWMS detects, controls, contains, escalates, corrects and learns from ambiguity and partial failure inside AI workflows.

AI systems do not only fail when they completely break.

They often fail quietly when:

• input is unclear
• instruction is vague
• context is missing
• source is incomplete
• source authority is uncertain
• a handoff loses meaning
• a tool returns partial output
• a workflow step succeeds while another fails
• an AI guesses instead of stopping
• confidence exceeds evidence
• uncertainty is hidden
• a weak assumption flows downstream
• a persistent agent continues after degraded performance
• a result is reported as complete without verification
• unverified material is committed to durable knowledge

These failures are dangerous because they can still produce confident-looking output.

MWMS must not allow unclear, incomplete or partially failed work to continue as if it is reliable.

This framework gives MWMS a governed method for:

• detecting ambiguity early
• separating known facts from assumptions
• containing partial failure
• stopping hallucination cascades
• degrading safely
• triggering rescue
• preserving failure evidence
• preventing false completion
• protecting downstream systems
• converting ambiguity into system learning

Scope

This framework applies to ambiguity and partial failure across:

• HeadOffice Brain
• Brain Room
• AI Manager
• AI Employee Router
• Task Executor systems
• Dev Console
• Newsletter Intelligence
• Course Absorption
• Opportunity System
• Affiliate Brain
• Research Brain
• Experimentation Brain
• Finance Brain
• Content Brain
• Ads Brain
• Sales Brain
• Conversion Brain
• Operations Brain
• Automation Brain
• Risk Brain
• Compliance Brain
• SIT Brain
• AIBS Brain
• Supabase task and event systems
• Make.com workflows
• WordPress Brain sites
• MCR workflows
• developer handoffs
• persistent AI Employees
• scheduled agents
• remote command channels
• external knowledge systems
• future AIBS client systems

This framework applies whenever MWMS receives, processes, retrieves, routes, validates, writes, displays or acts on information that may be:

• incomplete
• unclear
• conflicting
• stale
• unverified
• partially failed
• weakly supported
• misrouted
• improperly permissioned
• falsely marked complete

This framework does not authorise automation or development work.

It defines the containment discipline that must exist before AI workflows become trusted, tool-enabled or automated.

Core Definition

Ambiguity means the workflow lacks enough clarity, evidence, authority or current context to proceed safely without unsupported assumptions.

Partial failure means part of the workflow worked, but another part:

• failed
• degraded
• remained uncertain
• produced weak output
• lost context
• violated a rule
• did not reach the intended outcome

A complete failure is obvious.

A partial failure is often subtle.

Examples:

• Gmail captures a newsletter, but the body is truncated.
• An AI returns valid JSON, but critical fields are vague.
• A page draft is well written, but the parent page is wrong.
• A developer brief explains the issue but lacks the exact file.
• An offer evaluation has good copy analysis but no finance review.
• A course lesson is summarised but adds no MWMS value.
• A dashboard item appears but has no owner.
• A handoff occurs but loses failure history.
• A tool claims success but the target system did not change.
• A persistent agent completes a run but produces stale output.
• A session closes without recording the save point.

Partial failure must be contained before it spreads.

Core Principle

The core principle of this framework is:

The goal is not to prevent every failure. The goal is to prevent ambiguity or partial failure from corrupting downstream work.

MWMS should expect ambiguity.

MWMS should expect some workflow stages to fail.

The system must therefore be designed to:

• detect uncertainty
• expose assumptions
• preserve provenance
• stop when required evidence is missing
• degrade safely where possible
• escalate when risk increases
• trigger rescue after repeated failure
• prevent weak output from entering MCR
• prevent weak output from entering dashboards
• prevent weak output from reaching M
• prevent weak output from affecting paid traffic
• prevent weak output from affecting client systems
• prevent unverified output from becoming permanent knowledge
• log failures and improve the system

A contained failure is useful.

An uncontained failure becomes system drift.

Known, Assumed, Unknown And Failed States

MWMS should separate four information states.

Known

Supported by current evidence or authoritative source.

Assumed

Believed for working purposes but not verified.

Unknown

Required information is missing or unavailable.

Failed

The relevant step, source, tool, validation or outcome did not succeed.

Rule

Assumed must not be reported as Known.

Unknown must not be silently filled with invention.

Failed must not be reported as Completed.

Confidence State

Important outputs should identify confidence as:

• High Confidence
• Medium Confidence
• Low Confidence
• Unverified

Confidence should reflect:

• source completeness
• source authority
• source freshness
• context quality
• validation strength
• reviewer independence
• outcome evidence

Rule

Confidence is not a substitute for evidence.

Ambiguity Types

MWMS recognises five primary ambiguity types.

1. Data Ambiguity

Data ambiguity exists when available information is missing, incomplete, conflicting, stale, unverified or unreliable.

Examples:

• expired source
• incomplete transcript
• truncated newsletter body
• missing offer payout
• unverified vendor claims
• partial screenshot
• stale page list
• missing database fields
• unclear finance assumptions
• incomplete client data

Rule

Weak data must be marked before analysis.

2. Instruction Ambiguity

Instruction ambiguity exists when the task request is unclear, mixed, underspecified, contradictory or too broad.

Examples:

• “fix this” without naming the system
• “next” when several valid paths exist
• “absorb this” without clear block boundaries
• page update requested without exact page confirmation
• developer instruction requested without current file evidence
• course absorption mixed with development work
• automation requested before workflow readiness

Rule

Where instruction ambiguity affects material risk, clarify, narrow, park or proceed only with explicitly marked assumptions.

3. Context Ambiguity

Context ambiguity exists when current operational background is missing.

Examples:

• current M save point unknown
• current WordPress parent not confirmed
• current schema unknown
• M’s active build may be affected
• source of truth unclear
• old memory conflicts with visible evidence
• page registry may be stale
• current workflow stage unclear
• failure history missing

Rule

Current evidence overrides old memory.

4. Authority Ambiguity

Authority ambiguity exists when it is unclear who or what may decide, approve, write, publish or execute.

Examples:

• AI Employee attempts final approval
• Brain ownership conflicts
• client approval boundary is unclear
• tool-write authority is missing
• MCR change authority is not confirmed
• model consensus is treated as authority

Rule

Where authority is unclear, execution must pause.

5. Destination Ambiguity

Destination ambiguity exists when the correct next system, Brain, queue, person or knowledge location is unclear.

Examples:

• output could go to MCR or Parking
• alert has no dashboard or owner
• report has no receiving Brain
• course insight has no current page mapping
• knowledge has no approved durable destination

Rule

If destination is unclear, park or escalate rather than route blindly.

Partial Failure Types

MWMS recognises the following partial failure types.

1. Input Partial Failure

The input enters the workflow, but not cleanly or completely.

Examples:

• email body too short
• file expired
• transcript cut off
• screenshot partial
• source reference missing
• retrieved evidence incomplete

Containment:

• mark incomplete
• preserve original input
• request more input
• proceed only with caution where risk allows
• stop high-risk work

2. Extraction Partial Failure

Some useful content is extracted, but critical information is missed.

Examples:

• signal extracted but no action type
• offer details extracted but no risk
• course summary produced but no system value
• developer issue described but no current evidence

Containment:

• revise extraction
• identify omitted fields
• route to reviewer
• do not progress downstream until repaired

3. Classification Partial Failure

The work is understood incorrectly.

Examples:

• developer task treated as strategy
• newsletter noise treated as urgent
• offer routed before required research
• governance work treated as development
• rescue request treated as ordinary retry

Containment:

• reclassify
• correct Brain ownership
• revise Work Unit
• inspect downstream impact

4. Model Routing Partial Failure

The assigned model or capability route is insufficient or inappropriate.

Examples:

• weak model handles high-risk reasoning
• text-only route handles visual evidence
• same model creates and approves high-risk output
• low-cost route causes repeated rework
• privacy-sensitive work uses unsuitable hosted route

Containment:

• stop current route
• preserve output and failure history
• assign suitable model or specialist
• require independent review
• reassess privacy and cost

5. Output Partial Failure

The output is partly useful but not ready.

Examples:

• findings without verdict
• good page with wrong parent
• developer brief without test steps
• dashboard item without owner
• page output without required Change Impact Declaration
• validation notes without pass or fail

Containment:

• mark Draft
• revise before use
• do not save, route or execute
• validate again

6. Handoff Partial Failure

Work moves without enough context, evidence or status.

Examples:

• M receives vague instruction
• Research Brain lacks research question
• Finance Brain lacks assumptions
• MCR handoff lacks registry requirement
• Rescue Agent receives no failure history
• next session receives no save point

Containment:

• rebuild Handoff Package
• return to sender
• preserve attempt history
• require acceptance confirmation

7. Tool Partial Failure

A tool technically runs but produces incomplete, malformed or misleading results.

Examples:

• JSON parses but fields are weak
• record inserts with missing action
• workflow succeeds using truncated source
• stale search result used
• command runs but expected file is not created
• tool reports success but outcome is unverified

Containment:

• treat result as unvalidated
• inspect actual target state
• check schema and completeness
• stop automation where needed
• log tool failure

8. Validation Partial Failure

Validation occurs but misses the real problem.

Examples:

• polished output passes despite poor grounding
• duplicate risk missed
• wrong parent not detected
• unsafe developer instruction passes
• dashboard item passes despite no action
• self-review is mistaken for independence

Containment:

• strengthen checklist
• assign independent reviewer
• revalidate affected output
• inspect related outputs

9. Permission Partial Failure

The task is valid, but permissions are incomplete, excessive or unclear.

Examples:

• read access exists but write is attempted
• task transfer is treated as permission transfer
• client boundary is unclear
• remote command has excessive authority
• credentials are embedded in a skill

Containment:

• block execution
• reduce or revoke access
• confirm approved permission
• protect credentials
• log boundary failure

10. Persistent Agent Partial Failure

A scheduled or background agent continues but its quality, context, cost or reliability has degraded.

Examples:

• repeated stale reports
• duplicate alerts
• silent errors
• retry loop
• rising cost
• missing shutdown control
• background action based on old instructions

Containment:

• pause agent
• preserve last good state
• inspect schedule, context and permissions
• require approval before restart

11. Outcome Partial Failure

The output exists but intended business value does not occur.

Examples:

• report with no decision
• page drafted but never routed
• dashboard item with no action
• developer brief unusable by M
• action performed but result unverified
• AI activity changes nothing

Containment:

• record actual outcome state
• revise, route, park or reject
• stop counting output as progress

12. Knowledge Commitment Partial Failure

The work may be useful but is stored incorrectly, incompletely or without validation.

Examples:

• draft treated as Canon
• decision lost in chat
• stale information stored as current
• duplicate memory created
• save point omitted
• wrong Brain receives durable knowledge

Containment:

• stop commitment
• verify authority and destination
• correct status
• record exact save point
• remove or deprecate incorrect knowledge

Ambiguity Detection Checklist

Before proceeding with important AI work, check:

• Is the source complete?
• Is the source current?
• Is the source authoritative?
• Is provenance preserved?
• Is the user instruction clear?
• Is the task type clear?
• Is the Owning Brain clear?
• Is authority clear?
• Is the required output clear?
• Is the destination clear?
• Is the workflow stage clear?
• Is the current save point known?
• Are required files available?
• Are screenshots sufficient?
• Are tool permissions clear?
• Are assumptions marked?
• Are unknowns visible?
• Is confidence appropriate?
• Is risk known?
• Is human review required?
• Is independent review required?
• Is the next action safe?
• Could this affect M’s active build?
• Could this affect MCR?
• Could this affect money?
• Could this affect compliance?
• Could this affect clients?
• Could this become durable knowledge?

If several answers are unclear, ambiguity exists.

Ambiguity Response Levels

MWMS uses five ambiguity response levels.

Level 1 — Proceed

Use when:

• ambiguity is low
• risk is low
• outcome is easily reversible

Action:

• proceed normally
• perform basic validation

Level 2 — Proceed With Assumptions Marked

Use when:

• ambiguity is manageable
• risk is low to medium
• assumptions do not determine high-impact action

Action:

• state assumptions
• state known gaps
• reduce confidence
• continue cautiously

Level 3 — Clarify Or Normalize First

Use when ambiguity may materially affect quality.

Action:

• normalise input
• retrieve missing context
• clarify task
• do not produce final output yet

Level 4 — Stop And Escalate

Use when ambiguity affects high-risk work.

Action:

• stop
• preserve current state
• escalate to authorised owner
• request current evidence
• block downstream routing

Level 5 — Reject, Park Or Rescue

Use when ambiguity makes the work unsafe, repeatedly unsuccessful or not worth continuing.

Action:

• reject
• park
• request reupload
• trigger rescue
• log where important

Partial Failure Containment Model

When partial failure is detected, MWMS should follow:

Detect
→ Freeze
→ Isolate
→ Classify
→ Contain
→ Preserve Evidence
→ Escalate Or Rescue
→ Correct
→ Revalidate
→ Verify Outcome
→ Log
→ Learn
→ Close

1. Detect

Identify what did not fully work.

Detection signals include:

• missing field
• vague output
• incomplete source
• wrong Brain
• weak handoff
• tool mismatch
• validation gap
• unverified outcome
• permission uncertainty
• stale context

Rule

Do not ignore small failures because the overall output looks polished.

2. Freeze

Preserve the current task and prevent further propagation.

Freeze may include:

• stop routing
• stop execution
• stop retries
• mark Draft
• pause agent
• retain logs

Rule

The workflow state must be preserved before correction begins.

3. Isolate

Determine where the failure occurred.

Possible locations:

• input
• extraction
• classification
• model route
• reasoning
• output
• validation
• handoff
• tool use
• permissions
• persistent execution
• outcome
• knowledge commitment

Rule

Fix the failed stage rather than changing the entire system blindly.

4. Classify

Assign:

• ambiguity type
• partial failure type
• severity
• confidence state
• risk level

Rule

Classification determines whether to revise, reroute, rescue, park, reject or escalate.

5. Contain

Prevent the failure from spreading.

Possible containment actions:

• do not save to MCR
• do not send to M
• do not display on dashboard
• do not approve spend
• do not write to live systems
• do not deliver to client
• mark output Draft
• remove from active queue
• pause persistent agent
• block knowledge commitment

Rule

The first priority is preventing weak work from becoming operational truth.

6. Preserve Evidence

Preserve:

• original source
• Work Unit
• current output
• failure details
• tool responses
• model route
• validation results
• prior attempts
• user correction
• current system state

Rule

Failure evidence must not disappear during recovery.

7. Escalate Or Rescue

Escalate when:

• authority is unclear
• risk is high
• human judgement is needed
• current route lacks capability

Trigger rescue when:

• two materially identical failures occur without verified progress
• a loop is detected
• the current model route is unsuitable
• a materially different approach is required

8. Correct

Correct the cause.

Examples:

• request better source
• revise instruction
• change model
• correct Brain
• rebuild handoff
• reduce permissions
• update validation
• pause automation
• change skill procedure
• correct destination

Rule

Correct the cause, not only the visible symptom.

9. Revalidate

Corrected work must pass the failed gate again.

Revalidation may include:

• source grounding
• source authority
• completeness
• format
• Brain routing
• permissions
• duplicate check
• human review
• independent review
• tool-result verification

10. Verify Outcome

Where action occurred, confirm:

• intended state changed
• task was completed
• page was saved
• tool action succeeded
• client outcome occurred
• alert was resolved

Rule

Correction is not complete until the intended result is verified.

11. Log

Important ambiguity and partial failure should be logged.

Possible destinations:

• Failure Log
• Outcome Log
• SIT log
• Decision Record
• System Change Log
• project save point
• Brain-specific record

12. Learn

Turn meaningful failure into improvement.

Possible improvements:

• new checklist item
• improved skill
• stronger Context Pack
• better permission boundary
• clearer handoff
• new stop condition
• stronger source rule
• better dashboard filter
• improved rescue path

13. Close

Closure should record:

• final decision
• verified result
• unresolved issues
• knowledge committed
• exact next action
• closure owner
• closure date

Deterministic Failure Threshold

The standard MWMS rescue threshold is:

Two materially identical failures without verified progress.

At the threshold:

• the current route stops
• task state is frozen
• failure history is preserved
• a Rescue Packet is created
• a materially different route is assigned
• the next verification gate is defined

The count does not reset because:

• wording changed
• a new session began
• the same model claims understanding
• the same action was reformatted
• confidence increased

Rule

Repeated failure must trigger rescue rather than endless retry.

Graceful Degradation Rules

Graceful degradation means MWMS continues safely at a reduced level when full completion is not possible.

Rule 1 — Draft Instead Of Final

Where confidence is insufficient, produce Draft status.

Rule 2 — Park Instead Of Misroute

Where destination is unclear, park the work.

Rule 3 — Summary Instead Of Decision

Where evidence is incomplete, summarise known facts without pretending a decision is justified.

Rule 4 — Review Queue Instead Of Action

Where the signal is promising but uncertain, route to review.

Rule 5 — Human Review Instead Of Automation

Where risk is material, require human approval.

Rule 6 — Request Evidence Instead Of Guessing

Where current state matters, obtain current evidence.

Rule 7 — Reject Weak Material Instead Of Absorbing

Where course material adds no value, reject or park it.

Rule 8 — Stop Developer Handoff If M Must Guess

Where exact current evidence is absent, do not hand off to M.

Rule 9 — Read Only Instead Of Write

Where permission is unclear, remain in read or draft mode.

Rule 10 — Pause Persistent Agent Instead Of Allowing Drift

Where repeated uncertainty exists, pause the agent.

Rule 11 — Preserve Existing Knowledge Instead Of Overwriting

Where authority or freshness is unclear, do not overwrite Canon or durable memory.

Hallucination Cascade Protection

A hallucination cascade occurs when one unsupported assumption causes further unsupported actions.

Example:

AI assumes a page exists.

AI recommends updating it.

A duplicate page is created.

A registry is updated incorrectly.

Future workflows treat the duplicate as valid.

MWMS prevents hallucination cascades through:

• source-of-truth hierarchy
• current evidence
• assumption marking
• unknown-state marking
• validation gates
• duplicate checks
• independent review
• handoff controls
• failure thresholds
• stop conditions
• human approval
• knowledge-commitment controls

Hallucination Cascade Rule

One uncertain assumption must not become system truth.

Independent Review Rule

Independent review is required where ambiguity or partial failure affects:

• MCR
• governance
• finance
• compliance
• security
• live systems
• client output
• destructive action
• disputed evidence
• repeated model failure

The producing model or AI Employee must not be the sole final reviewer.

Independent review may use:

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

Rule

Self-review may assist correction.

It does not satisfy mandatory independent review.

Tool Execution Verification Rule

Where a tool action is claimed, MWMS must verify:

• tool used
• action requested
• target
• permission
• actual response
• resulting state
• error state
• outcome evidence

Examples:

• generated SQL is not a database change
• drafted email is not a sent email
• page output is not a published page
• command execution is not proof of intended outcome
• file reference is not proof the file exists

Rule

No tool action should be treated as successful without evidence from the affected system.

Persistent Agent Containment Rule

Persistent or scheduled agents require additional containment controls.

Required controls:

• owner
• trigger
• current context
• failure count
• retry limit
• cost boundary
• validation
• alerting
• shutdown
• stale-instruction detection
• last-good state

A persistent agent should pause when:

• source quality degrades
• failure threshold is reached
• context is stale
• permissions are unclear
• cost limit is exceeded
• output becomes repetitive or noisy
• required human review is unresolved

Rule

Persistence does not justify continuing degraded work.

External Knowledge Ambiguity Rule

Where external knowledge retrieval is used, ambiguity may arise from:

• weak similarity match
• stale source
• conflicting evidence
• incomplete source collection
• missing provenance
• wrong client filter
• low-authority source

The reasoning agent must receive:

• source identity
• authority
• freshness
• provenance
• conflicts
• evidence gaps

Rule

Retrieved information must not be presented as final truth merely because it was returned by a knowledge system.

Knowledge Commitment Containment Rule

Before ambiguous or partially failed work becomes durable knowledge, confirm:

• source is known
• authority is clear
• ambiguity is resolved or explicitly retained
• validation is complete
• destination is correct
• duplication is checked
• approval exists
• version and status are clear

Rule

Ambiguous output must not silently become permanent organisational memory.

Ambiguity And Partial Failure Record Template

Record ID:

Record Title:

Date Detected:

Work Unit ID:

Workflow:

Workflow Stage:

Owning Brain:

AI Employee:

Model Or Capability Route:

Tools Used:

Source:

Source Authority:

Source Freshness:

Ambiguity Type:

Partial Failure Type:

Severity:

Confidence State:

What Is Unclear Or Failed:

Known Facts:

Known Gaps:

Assumptions Detected:

Unknowns:

Failure Count:

Risk Level:

Current Task State:

Containment Action:

Evidence Preserved:

Tool Execution Status:

Permission Status:

Independent Review Required:

Escalation Required:

Escalation Destination:

Rescue Required:

Rescue Route:

Correction Required:

Revalidation Required:

Outcome Verification Required:

Knowledge Commitment Blocked:

Final Decision:

Learning Captured:

Next Action:

Owner:

Status:

Closure Notes:

Quick Use Version

Record ID:

Record Title:

Date Detected:

Work Unit ID:

Workflow:

Owning Brain:

Source:

Ambiguity Type:

Partial Failure Type:

Severity:

Confidence State:

What Is Unclear Or Failed:

Known Facts:

Known Gaps:

Assumptions Detected:

Failure Count:

Risk Level:

Containment Action:

Evidence Preserved:

Independent Review Required:

Escalation Required:

Escalation Destination:

Rescue Required:

Correction Required:

Revalidation Required:

Final Decision:

Learning Captured:

Next Action:

Owner:

Status:

Examples

Example 1 — WordPress Duplicate Cleanup Ambiguity

Record Title:

Duplicate Page Keeper Logic Ambiguity

Workflow:

MCR Page Cleanup Workflow

Owning Brain:

HeadOffice Brain

Ambiguity Type:

Context Ambiguity

Partial Failure Type:

Validation Partial Failure

What Is Unclear Or Failed:

The initial decision relied too heavily on internal dates instead of current parent placement and content identity.

Known Facts:

Two pages shared a title.

Parent placement was visible.

Known Gaps:

Content identity was not confirmed early enough.

Assumptions Detected:

Newer date was treated as stronger evidence than current structure.

Risk Level:

Medium

Containment Action:

Stop cleanup until parent and content are compared.

Correction Required:

Correct parent takes precedence. Unique useful content must be preserved before deletion.

Revalidation Required:

Yes

Final Decision:

Correct And Revalidate

Learning Captured:

Current visible parent placement and page content must be checked before date-based assumptions.

Status:

Closed

Example 2 — Newsletter Body Partial Failure

Record Title:

Newsletter Body Incomplete Despite Successful Workflow

Workflow:

Newsletter Intelligence Workflow

Owning Brain:

HeadOffice Brain

Ambiguity Type:

Data Ambiguity

Partial Failure Type:

Input Partial Failure / Tool Partial Failure

What Is Unclear Or Failed:

The workflow completed, but the newsletter body may be truncated.

Known Facts:

The email was processed.

Known Gaps:

Whether the complete body was used.

Assumptions Detected:

Successful execution was treated as complete source capture.

Risk Level:

Medium

Containment Action:

Block dashboard routing and verify source completeness.

Final Decision:

Normalize First

Learning Captured:

Tool success does not guarantee complete input.

Status:

Monitoring

Example 3 — Developer Handoff Missing Evidence

Record Title:

Developer Brief Missing Current File Evidence

Workflow:

Developer Support Workflow

Owning Brain:

HeadOffice Brain

Ambiguity Type:

Context Ambiguity / Data Ambiguity

Partial Failure Type:

Handoff Partial Failure

What Is Unclear Or Failed:

Exact file path or current contents are missing.

Known Facts:

A technical change is requested.

Known Gaps:

Current file, path and side effects.

Assumptions Detected:

Any file location would be guesswork.

Risk Level:

High

Containment Action:

Do not send to M.

Escalation Required:

Yes

Escalation Destination:

Martyn / M / HeadOffice

Final Decision:

Stop Workflow

Learning Captured:

If M must guess, the handoff has failed.

Status:

Waiting For Input

Example 4 — Offer Evaluation Missing Finance Review

Record Title:

Offer Evaluation Missing Financial Viability

Workflow:

Offer Evaluation Workflow

Owning Brain:

Affiliate Brain

Ambiguity Type:

Data Ambiguity

Partial Failure Type:

Output Partial Failure / Classification Partial Failure

What Is Unclear Or Failed:

The evaluation lacks payout, cost and break-even logic.

Known Facts:

The offer may have market interest.

Known Gaps:

Financial viability.

Assumptions Detected:

Revenue potential is inferred without cost reality.

Risk Level:

High

Containment Action:

Do not move toward testing.

Escalation Destination:

Finance Brain / Research Brain

Final Decision:

Escalate

Learning Captured:

No offer moves toward spend without financial review.

Status:

Open

Example 5 — Course Absorption Weak Value

Record Title:

Course Lesson Summarized Without MWMS Value

Workflow:

Course Absorption Workflow

Owning Brain:

HeadOffice Brain

Ambiguity Type:

Instruction Ambiguity / Data Ambiguity

Partial Failure Type:

Outcome Partial Failure

What Is Unclear Or Failed:

The lesson is understandable but adds no distinct reusable system value.

Known Facts:

Course content exists.

Known Gaps:

No unique improvement beyond current MCR.

Assumptions Detected:

Available content is assumed to deserve absorption.

Risk Level:

Medium

Containment Action:

Do not create a page.

Final Decision:

Park Or Reject

Learning Captured:

Course availability is not absorption value.

Status:

Closed

Ambiguity And Partial Failure Readiness Checklist

Before allowing a workflow to proceed, check:

• Has source completeness been checked?
• Has source authority been checked?
• Has source freshness been checked?
• Is provenance preserved?
• Has instruction been classified?
• Is the Owning Brain assigned?
• Is authority clear?
• Are known gaps visible?
• Are assumptions marked?
• Are unknowns marked?
• Is confidence appropriate?
• Is risk assigned?
• Is required output clear?
• Is destination clear?
• Is validation status clear?
• Are dependencies clear?
• Is human review required?
• Is independent review required?
• Are tool permissions clear?
• Are stop conditions known?
• Is failure count known?
• Is rescue route defined?
• Can the workflow degrade safely?
• Can it be stopped safely?
• Could the failure affect downstream systems?
• Could it create MCR clutter?
• Could it confuse M?
• Could it affect money, compliance or clients?
• Could it corrupt durable knowledge?
• Should it be parked instead of forced forward?

If several answers remain unclear, containment is required.

Common Failure Modes

Ambiguity and partial failure containment has failed when:

• missing input is ignored
• current state is guessed
• partial source is treated as complete
• unverified claims become facts
• vague instruction becomes final output
• polished output is accepted without grounding
• wrong Brain receives the work
• self-review is treated as independent review
• human review is skipped
• M receives instructions requiring guesswork
• dashboard item has no owner
• weak course material becomes MCR
• tool run is treated as correct because it completed
• persistent agent continues after repeated degradation
• failure count is reset without progress
• rescue repeats the failed route
• ambiguous material becomes durable knowledge
• partial failure is not logged
• session closes without a save point
• one assumption becomes downstream truth

Failure And Rescue Rule

At the first material ambiguity or partial failure:

• freeze affected work
• preserve source and output
• classify the problem
• identify required correction
• retry only where a corrected route exists

At the second materially identical failure without verified progress:

• stop the original route
• preserve full attempt history
• create a Rescue Packet
• assign a different model, specialist, Brain or human
• define a new verification gate

Rule

Repeated ambiguity must trigger rescue, not repeated guessing.

Manual Use Rule

This framework should be used manually before containment becomes technical infrastructure.

Manual use helps MWMS learn:

• where ambiguity appears
• which workflows fail partially
• which failures require stopping
• which failures can degrade safely
• where validation is weak
• where Context Packs need improvement
• where tool output is unreliable
• where M needs stronger evidence
• which patterns may become status fields
• where persistent agents should pause
• where rescue routes are required

Manual containment proof comes before automation.

Future Plugin Or UI Relevance

This framework may later support:

• ambiguity detection checklist
• partial failure review screen
• AI Manager stop-condition logic
• Task Executor failure states
• Brain Room clarification workflow
• Exchange Zone blocker status
• HeadOffice failure dashboard
• developer handoff safety gate
• newsletter completeness check
• Course Absorption value gate
• persistent-agent pause control
• rescue routing
• AIBS client safety gate

Possible future fields:

ambiguity_record_id

work_unit_id

record_title

date_detected

workflow

workflow_stage

owning_brain

ai_employee

model_route

tools_used

source

source_authority

source_freshness

ambiguity_type

partial_failure_type

severity

confidence_state

unclear_or_failed

known_facts

known_gaps

assumptions_detected

unknowns

failure_count

risk_level

current_task_state

containment_action

evidence_preserved

tool_execution_status

permission_status

independent_review_required

escalation_required

escalation_destination

rescue_required

rescue_route

correction_required

revalidation_required

outcome_verification_required

knowledge_commitment_blocked

final_decision

learning_captured

next_action

owner

status

closure_notes

created_at

updated_at

No technical build is authorised by this framework alone.

Governance Role

HeadOffice owns the MWMS AI Ambiguity And Partial Failure Containment Framework.

HeadOffice is responsible for:

• defining ambiguity rules
• defining containment standards
• deciding when ambiguity requires clarification
• deciding when work must stop
• governing rescue thresholds
• ensuring partial failures do not become truth
• protecting MCR from weak content
• protecting dashboards from unclear signals
• protecting M from ambiguous handoffs
• protecting paid traffic, finance and compliance
• protecting client workflows
• protecting durable organisational knowledge
• ensuring repeated patterns become Kaizen improvements
• deciding when containment controls are ready for technical implementation

Individual Brains may manage low-risk ambiguity within their own workflows.

HeadOffice governs ambiguity involving:

• cross-Brain work
• MCR
• governance
• development
• tools
• automation
• persistent agents
• money
• compliance
• clients
• durable knowledge

Relationship To SIT Brain

SIT Brain may:

• detect unresolved ambiguity
• verify confidence state
• detect hidden assumptions
• detect false completion
• enforce validation gates
• verify failure counts
• trigger rescue
• block unsafe routing
• verify tool execution
• verify permission boundaries
• pause persistent agents
• prevent unverified knowledge commitment
• verify correction before restart

Relationship To Data Brain

Data Brain supports:

• ambiguity records
• Work Unit linkage
• source provenance
• failure counts
• model routes
• tool records
• status history
• containment actions
• rescue records
• outcome evidence
• closure records
• trend analysis

Relationship To Other MWMS Standards

This framework supports and must align with:

• MWMS AI Agent Operations Core
• MWMS AI Multi Agent Role Design Framework
• MWMS AI Exchange Zone And Dependency Control Framework
• MWMS AI Agent Skill Library Framework
• MWMS AI Plugin Orchestration Framework
• MWMS AI Documentation Automation Pipeline Framework
• MWMS Messy Input Normalization Framework
• MWMS Messy Input Normalization Record
• MWMS AI Agent Memory And Context Framework
• MWMS AI Agent Context Pack Template
• MWMS AI Output Validation Standard
• MWMS AI Output Validation Checklist
• MWMS AI Employee Handoff Protocol
• MWMS AI Employee Handoff Package Template
• MWMS AI Agent Failure Handling And Escalation Protocol
• MWMS AI Agent Failure Log Record
• MWMS Independent Model Review And Rescue Routing Framework
• 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 AI Observability Metadata Standard
• MWMS External Knowledge Engine And Reasoning Agent Separation Framework
• MWMS AI Work Session Closure And Knowledge Commitment Protocol
• MWMS AI Agent Deployment Readiness Checklist
• MWMS AI Agent Deployment Readiness Review Template
• MWMS Brain Routing Rule
• MWMS Brain To Brain Request Protocol
• MWMS Supabase Event Schema
• AIBS Brain Blueprint

This framework adds ambiguity detection, graceful degradation and partial failure containment to the MWMS AI Agent Operations Core.

Drift Protection

This framework protects MWMS from:

• acting on unclear input
• treating partial sources as complete
• allowing assumptions to become facts
• allowing one failed stage to corrupt downstream work
• accepting polished but unsupported output
• routing unclear work incorrectly
• creating MCR pages from weak material
• sending vague instructions to M
• letting dashboard noise accumulate
• allowing tool success to hide outcome failure
• skipping human review
• automating unresolved failure
• letting persistent agents drift
• losing failure history
• resetting failure counts
• treating self-review as independence
• committing ambiguous output as knowledge
• closing work without save points
• treating output volume as success

Ambiguity And Partial Failure Drift Signals

MWMS should watch for:

• source incomplete
• source authority unclear
• provenance missing
• instruction vague
• context stale
• authority unclear
• destination unclear
• confidence not stated
• assumptions hidden
• unknowns hidden
• failure count missing
• tool result unverified
• permission uncertain
• reviewer not independent
• persistent agent still running after repeated degradation
• rescue route absent
• outcome unverified
• knowledge commitment not blocked
• no closure state

Rule

Unresolved ambiguity must not receive more operational authority.

Minimum Compliance Standard

An important ambiguity or partial failure record is compliant only when it defines:

• Record ID
• Work Unit ID
• workflow
• workflow stage
• Owning Brain
• AI Employee
• model route
• tools
• source
• source authority
• source freshness
• ambiguity type
• partial failure type
• severity
• confidence
• known facts
• known gaps
• assumptions
• unknowns
• failure count
• risk
• task state
• containment action
• preserved evidence
• tool execution status
• permission status
• independent review
• escalation
• rescue
• correction
• revalidation
• outcome verification
• knowledge-commitment status
• final decision
• learning
• next action
• owner
• status
• closure

Architectural Intent

The architectural intent of the MWMS AI Ambiguity And Partial Failure Containment Framework is to make MWMS resilient.

A governed AI workforce must know:

• how to continue safely when things are unclear
• how to reduce authority when confidence falls
• how to stop when continuing would create risk
• how to preserve the work state
• how to change reasoning route after repeated failure
• how to verify recovery
• how to prevent ambiguous output becoming durable truth

The long-term goal is that every meaningful workflow can answer:

• What is unclear?
• What is known?
• What is assumed?
• What is unknown?
• What failed?
• Which stage failed?
• Which model and tools were involved?
• What confidence applies?
• Can the workflow continue safely?
• Should it degrade to draft, review, parking or summary?
• Should it stop?
• Should it trigger rescue?
• What evidence was preserved?
• What correction is needed?
• What must be revalidated?
• Was the outcome verified?
• Should knowledge commitment remain blocked?
• What learning should be captured?
• How will the work close?

When MWMS can answer these questions consistently, ambiguity becomes manageable and partial failure becomes system intelligence instead of hidden risk.

Strategic Summary

The v1.1 upgrade expands the MWMS AI Ambiguity And Partial Failure Containment Framework from a basic ambiguity-control model into a complete degraded-state, containment, rescue and recovery framework.

The upgraded framework now governs:

• Known, Assumed, Unknown and Failed states
• confidence states
• authority ambiguity
• destination ambiguity
• model-routing partial failure
• permission partial failure
• persistent-agent partial failure
• knowledge-commitment partial failure
• task freezing
• evidence preservation
• deterministic rescue thresholds
• independent review
• tool-execution verification
• persistent-agent pausing
• external knowledge ambiguity
• outcome verification
• formal closure

The key shift is:

MWMS should not merely notice uncertainty.

It must reduce authority, contain propagation, preserve evidence, change route when needed and verify recovery before work resumes.

Final Rule

The goal is not to prevent every failure.

The goal is to prevent ambiguity or partial failure from corrupting downstream work.

No source clarity, no reliable analysis.

No authority clarity, no execution.

No confidence state, no honest interpretation.

No containment, no safe continuation.

No failure history, no valid rescue.

No independent review, no high-risk recovery approval.

No tool evidence, no execution claim.

No verified outcome, no completion.

No controlled commitment, no durable knowledge.

Change Log

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

Change:

Updated the MWMS AI Ambiguity And Partial Failure Containment Framework using the AI Automations by Jack block covering multi-agent orchestration, independent review, rescue routing, persistent agents, external knowledge systems, tool verification and session closure.

Added:

• Known, Assumed, Unknown And Failed States
• Confidence State
• Authority Ambiguity
• Destination Ambiguity
• Model Routing Partial Failure
• Permission Partial Failure
• Persistent Agent Partial Failure
• Knowledge Commitment Partial Failure
• expanded Partial Failure Containment Model
• Freeze step
• Preserve Evidence step
• Rescue step
• Outcome Verification step
• Closure step
• Deterministic Failure Threshold
• Independent Review Rule
• Tool Execution Verification Rule
• Persistent Agent Containment Rule
• External Knowledge Ambiguity Rule
• Knowledge Commitment Containment Rule
• expanded Ambiguity And Partial Failure Record Template
• Failure And Rescue Rule
• Relationship To SIT Brain
• Relationship To Data Brain
• Drift Signals
• Minimum Compliance Standard
• Strategic Summary
• Final Rule

Expanded:

• Purpose
• Scope
• Core Definition
• Core Principle
• Ambiguity Detection Checklist
• Response Levels
• Graceful Degradation Rules
• Hallucination Cascade Protection
• Readiness Checklist
• Common Failure Modes
• Future UI Fields
• Governance Role
• Drift Protection
• Architectural Intent

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

Purpose of update:

To evolve the MWMS AI Ambiguity And Partial Failure Containment Framework from a basic uncertainty-management model into the complete degraded-state and recovery control layer for ambiguous input, incomplete output, model failure, tool failure, persistent-agent drift, rescue routing, outcome verification and durable knowledge protection.

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

Change:

Created the MWMS AI Ambiguity And Partial Failure Containment Framework to define how MWMS detects ambiguity, handles unclear data, unclear instructions, missing context, partial workflow failures, hallucination cascade risk, graceful degradation, containment, escalation, correction, revalidation, logging and learning.

Change Impact Declaration

This v1.1 update expands the AI Ambiguity And Partial Failure Containment Framework from a general ambiguity and graceful degradation model into a complete degraded-state, evidence-preservation, rescue-routing, execution-verification, persistent-agent, knowledge-protection and recovery framework.

Pages Created

None

Pages Updated

MWMS AI Ambiguity And Partial Failure Containment Framework

Pages Deprecated

None

Standalone Pages Not Created

MWMS AI Confidence State Standard

MWMS AI Degraded Operation Protocol

MWMS Partial Tool Failure Framework

MWMS Persistent Agent Containment Standard

MWMS Ambiguity Rescue Protocol

MWMS AI Recovery Verification Standard

MWMS Knowledge Commitment Containment Rule

Registries Requiring Update

HeadOffice Page Registry

MWMS Canon Index

MWMS Course Absorption Decision Registry

Canon Version Update Required

No

Change Log Entry Required

Yes

Strategic Absorption Result

MWMS gains a stronger containment framework that distinguishes facts, assumptions, unknowns and failures; stops ambiguous work from receiving operational authority; triggers rescue after repeated failure; verifies tool execution and outcomes; pauses degraded persistent agents; and prevents uncertain material from corrupting MCR, dashboards, developer work, client systems or durable knowledge.

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