MWMS MCR

MWMS AI Agent Deployment Readiness Checklist

System: MWMS
Document Type: Checklist
Authority Level: MCR Source Of Truth
Status: Draft For MCR
Version: v1.1
Primary Location: MCR
Future Operational Destination: HeadOffice Brain, MWMS Brain, Brain Room, AI Manager, AI Employee Router, Task Executor Systems, Newsletter Intelligence, Course Absorption System, Opportunity System, Automation Brain, AIBS Brain
Parent Page: HeadOffice
Owner: Martyn
Developer Boundary: Do Not Touch M’s Active Build Areas Unless Specifically Assigned
Source Of Truth: MCR
Last Reviewed: 2026-06-18
Source / Origin: MWMS AI Agent Deployment Readiness Checklist v1.0 + AI Automations by Jack — Multi-Agent Orchestration, Model Routing, Independent Review, Rescue Routing, Persistent Agents, External Knowledge, Tool Verification And Session Closure Block
MWMS Classification: Deployment Readiness Checklist / AI Workforce Activation Gate / Agentic Workflow Safety Gate / Automation Readiness Control
Primary Brain: HeadOffice Brain
Supporting Brains: MWMS Brain, Automation Brain, Operations Brain, Data Brain, Risk Brain, Compliance Brain, SIT Brain, AIBS Brain
Related Pages: MWMS AI Agent Operations Core, MWMS AI Employee Role Card Standard, MWMS Agentic Work Unit Standard, MWMS AI Agent Orchestration Framework, MWMS AI Multi Agent Role Design Framework, MWMS AI Workflow Pipeline Standard, MWMS AI Plugin Orchestration Framework, MWMS AI Exchange Zone And Dependency Control Framework, MWMS AI Output Validation Standard, MWMS AI Agent Failure Handling And Escalation Protocol, MWMS AI Ambiguity And Partial Failure Containment Framework, MWMS AI Agent Outcome Measurement Framework, MWMS AI Tool Permission And Access Framework, MWMS AI Agent Memory And Context Framework, MWMS AI Observability Metadata Standard
Source Evidence: The existing checklist defines the readiness gate for moving AI Employees and agentic workflows from concept into manual, assisted, controlled automated or restricted autonomous use. The absorbed material strengthens the gate with model-route readiness, independent review, Rescue Routes, persistent-agent controls, external-knowledge provenance, tool-execution verification, cost visibility, session continuity and knowledge-commitment readiness.

Purpose

The purpose of this document is to define the MWMS AI Agent Deployment Readiness Checklist.

This checklist determines whether an AI Employee, agentic workflow, Brain process, reporting workflow, task workflow, persistent agent, tool-enabled process or future client-facing AI system is ready to move from concept into controlled operation.

MWMS must not deploy AI Employees or workflows merely because the idea sounds useful.

Before deployment, MWMS must confirm:

  • role clarity
  • Brain ownership
  • workflow structure
  • input quality
  • context quality
  • model suitability
  • tool permissions
  • output definition
  • validation
  • independent review
  • handoff controls
  • failure handling
  • Rescue Routing
  • human approval
  • logging
  • cost visibility
  • outcome measurement
  • persistent-agent safeguards
  • knowledge-commitment controls
  • developer boundary safety
  • HeadOffice visibility
  • session continuity

This checklist exists to prevent immature AI workflows from entering operational use too early.

Scope

This checklist applies before activating any meaningful:

  • AI Employee
  • multi-agent workflow
  • Brain automation
  • reporting workflow
  • Task Executor process
  • model-routing system
  • persistent or scheduled agent
  • tool-enabled AI process
  • knowledge retrieval workflow
  • documentation pipeline
  • future AIBS client process

This includes deployment into:

  • 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
  • Automation Brain
  • Operations Brain
  • SIT Brain
  • AIBS Brain
  • Supabase task systems
  • WordPress plugin systems
  • Make.com or n8n workflows
  • future AIBS client systems

This checklist applies to:

  • manual workflows
  • assisted workflows
  • draft-generation workflows
  • supervised automations
  • controlled automations
  • restricted autonomous workflows
  • persistent agents
  • client-facing workflows

A manual workflow may still require deployment readiness when it becomes a repeatable MWMS operating process.

An automated workflow must always pass readiness review before it is trusted.

Core Definition

AI Agent Deployment Readiness means the AI Employee or workflow is sufficiently defined, governed, tested, validated, observable and controlled to operate safely within its intended scope.

Deployment does not always mean fully automated.

Deployment may mean:

  • approved for draft design
  • approved for manual use
  • approved for assisted use
  • approved for queue processing
  • approved for dashboard candidate creation
  • approved for controlled automation
  • approved for supervised client use
  • approved for restricted autonomous low-risk operation
  • approved for future technical build

An AI workflow may be useful but not ready.

A workflow is ready only when its boundaries, controls, failure paths and expected outcomes are clear.

Core Principle

The core principle of this checklist is:

No AI Employee or agentic workflow should become operational until it can be assigned, contextualised, validated, handed off, measured, rescued and safely stopped if it fails.

This protects MWMS from:

  • premature automation
  • vague AI Employees
  • weak task design
  • unsuitable model routes
  • unvalidated outputs
  • unsafe tool access
  • poor handoffs
  • false independent review
  • repeated failure loops
  • noisy persistent agents
  • dashboard clutter
  • developer confusion
  • live-system risk
  • client-facing risk
  • hidden cost
  • false completion
  • unverified knowledge commitment

Deployment readiness is the gate between a promising idea and safe operation.

Deployment Readiness Levels

MWMS uses six deployment readiness levels.

Level 0 — Concept Only

The AI Employee or workflow is only an idea.

Allowed:

  • brainstorm
  • outline
  • compare with existing systems
  • decide whether it is worth developing
  • park for later

Not allowed:

  • live use
  • automation
  • operational task assignment
  • dashboard use
  • client use
  • M implementation request
  • tool-enabled action

Level 1 — Draft Ready

The AI Employee or workflow has enough structure to be documented and tested manually.

Allowed:

  • create Role Card draft
  • create workflow draft
  • create sample output
  • test low-risk examples
  • review with HeadOffice

Not allowed:

  • unsupervised operation
  • live-system changes
  • client-facing use
  • high-risk decision support
  • controlled writes

Level 2 — Manual Operation Ready

The workflow may be used manually with human supervision.

Allowed:

  • manual processing
  • human-reviewed outputs
  • draft reports
  • queue review
  • MCR page drafts
  • controlled internal use

Required:

  • role defined
  • workflow defined
  • output defined
  • human review defined
  • known failure handling
  • next owner defined

Not allowed:

  • autonomous execution
  • live external action
  • direct system changes without approval

Level 3 — Assisted Automation Ready

The workflow may automate selected stages while humans retain key approval.

Allowed:

  • structured intake
  • automated extraction
  • draft task creation
  • queue-item generation
  • dashboard candidate creation
  • AI-assisted routing
  • validation assistance
  • tool-assisted evidence retrieval

Required:

  • stable inputs
  • defined output schema
  • validation checklist
  • logging
  • escalation
  • human review gate
  • failure threshold
  • Rescue Route

Not allowed:

  • high-risk autonomous action
  • irreversible live changes
  • final client delivery without approval
  • autonomous paid traffic or finance decisions

Level 4 — Controlled Automation Ready

The workflow may operate automatically inside approved boundaries.

Allowed:

  • repeatable low-to-medium-risk automation
  • automated classification
  • automated routing to review
  • internal status updates
  • internal reports
  • logging
  • scheduled monitoring with exception review

Required:

  • proven manual workflow
  • predictable input
  • stable output
  • approved model route
  • validation
  • failure handling
  • logging
  • cost visibility
  • human exception review
  • shutdown control
  • HeadOffice visibility

Not allowed:

  • critical-risk autonomous action
  • financial approval
  • paid traffic launch
  • legal or compliance-sensitive final action
  • unapproved live-system changes

Level 5 — Restricted Autonomous Operation Ready

The workflow may operate without immediate human review only inside tightly defined low-risk boundaries.

Allowed:

  • formatting
  • tagging
  • low-risk classification
  • internal cleanup
  • routine status updates
  • non-public draft preparation
  • repeated validated transformations
  • qualified low-risk monitoring alerts

Required:

  • highly stable workflow
  • low risk
  • strong logs
  • known failure modes
  • deterministic stop conditions
  • Rescue Route
  • periodic review
  • cost limits
  • owner
  • shutdown control
  • HeadOffice awareness

This level should remain rare.

Level 6 — Deployment Suspended

Use when an active workflow loses readiness.

Triggers may include:

  • repeated identical failure
  • source quality degradation
  • validation failure
  • missing owner
  • excessive cost
  • tool-permission breach
  • persistent-agent noise
  • client or compliance risk
  • model-route failure
  • unresolved critical dependency

Allowed:

  • diagnosis
  • containment
  • Rescue Review
  • controlled correction
  • revalidation

Not allowed:

  • continued operation
  • wider deployment
  • restored automation without approval

Readiness Principle

Readiness is not permanent.

A workflow may move forward, remain stable, regress or be suspended.

Deployment Readiness Checklist

Before deployment, check each section.

  1. Purpose Readiness

Check:

  • What problem does it solve?
  • Which workflow does it improve?
  • Which Brain benefits?
  • What manual work does it reduce?
  • What decision does it support?
  • What risk does it reduce?
  • What business outcome does it create?
  • Why is an AI Employee or automation needed?

Pass condition:

Purpose is specific, measurable and connected to MWMS value.

Fail condition:

The workflow sounds interesting but has no clear operational benefit.

  1. Brain Ownership Readiness

Check:

  • Which Brain owns it?
  • Which Brains support it?
  • Is one primary owner named?
  • Does HeadOffice require visibility?
  • Is it cross-Brain?
  • Is it governance or operational work?
  • Is ownership recorded?

Pass condition:

One Owning Brain is clearly identified.

Fail condition:

Ownership is shared vaguely or left unresolved.

  1. Role Card Readiness

Check:

  • Employee Name defined
  • Owning Brain defined
  • Authority Level defined
  • Purpose defined
  • Primary Responsibilities defined
  • Non-Responsibilities defined
  • Input Types defined
  • Context Requirements defined
  • Output Types defined
  • Approved Tools defined
  • Forbidden Actions defined
  • Handoff Destinations defined
  • Escalation Rules defined
  • Validation Rules defined
  • Success Metrics defined
  • Failure Modes defined
  • Logging Requirement defined
  • Closure Requirement defined

Pass condition:

The AI Employee has a complete Role Card for the proposed risk level.

Fail condition:

The AI Employee is only a name, prompt or broad concept.

  1. Work Unit Readiness

Check:

  • Work Unit ID can be created
  • task title can be defined
  • task type can be defined
  • source can be captured
  • Owning Brain can be assigned
  • AI Employee can be assigned
  • input payload can be attached
  • Context Pack can be attached
  • instruction can be written
  • output format can be defined
  • validation standard can be selected
  • handoff destination can be identified
  • expected outcome can be stated
  • priority and risk can be assigned
  • failure count can be tracked
  • closure state can be recorded

Pass condition:

Work can be represented as a structured Agentic Work Unit.

Fail condition:

The workflow relies on vague prompts or undocumented instructions.

  1. Input Readiness

Check:

  • input source is known
  • input authority is known
  • input freshness is known
  • input format is predictable enough
  • required fields are defined
  • missing input can be detected
  • messy input can be normalized
  • source is preserved
  • completeness can be checked
  • sensitive data can be identified
  • client or project boundaries can be enforced

Pass condition:

Input is usable or a controlled normalization stage exists.

Fail condition:

The AI is expected to process incomplete or unpredictable input without safeguards.

  1. Pipeline Readiness

Check:

  • capture stage exists
  • normalization stage exists where needed
  • classification stage exists
  • Work Unit stage exists
  • role assignment exists
  • processing stage is clear
  • review stage exists
  • validation stage exists
  • decision state exists
  • routing stage exists
  • outcome verification exists
  • logging exists
  • knowledge commitment exists where needed
  • closure exists

Pass condition:

The workflow has a complete sequence from input to verified outcome.

Fail condition:

One prompt is expected to perform the entire workflow without control.

  1. Context Readiness

Check:

  • relevant Brain rules are available
  • related standards are known
  • current save point is known where relevant
  • developer boundary is known
  • page rules are known where relevant
  • Source Of Truth is identified
  • previous decisions are attached
  • current workflow state is attached
  • failure history is attached
  • user instructions are preserved

Pass condition:

The AI Employee receives enough current context to avoid isolated or invented output.

Fail condition:

The workflow depends on the AI guessing current MWMS state.

  1. Model Route Readiness

Check:

  • required model capability is defined
  • task complexity matches the model
  • context-window needs are known
  • multimodal needs are known
  • privacy requirements are known
  • cost limits are known
  • local model requirement is known where relevant
  • independent-review model is defined where required
  • Rescue Model route exists
  • fallback route exists

Pass condition:

The selected model or capability route is fit for purpose.

Fail condition:

One default model is used regardless of task, risk, privacy or failure history.

  1. Multi-Agent Role Separation Readiness

Check:

  • planning and building are separated where required
  • research and writing are separated where required
  • producing and reviewing are separated
  • validation is separate from writing
  • approval is separate from execution
  • role overlaps are controlled
  • Coordinator authority is bounded
  • Rescue Agent is independent of the failed route

Pass condition:

Role separation improves control without unnecessary complexity.

Fail condition:

One AI Employee researches, decides, writes, approves and executes high-risk work.

  1. Tool Permission Readiness

Check:

  • approved tools are listed
  • forbidden actions are listed
  • permission level matches authority
  • approved targets are defined
  • credentials are protected
  • live access is restricted
  • database writes require approval where needed
  • email sending requires approval
  • publishing requires approval
  • external action boundaries are clear
  • access can be revoked
  • current access is verified

Pass condition:

Tool access is explicit, narrow and controlled.

Fail condition:

Tool use is assumed or broader than required.

  1. Tool Execution Verification Readiness

Check:

  • requested action can be logged
  • tool response can be captured
  • error state can be checked
  • affected system state can be verified
  • expected state is defined
  • outcome evidence is available
  • partial execution can be detected
  • rollback or containment is possible

Pass condition:

MWMS can verify that the intended action actually occurred.

Fail condition:

A generated command or success response is treated as proof of completion.

  1. External Knowledge Readiness

Check:

  • retrieval question is clear
  • approved sources are defined
  • source authority is recorded
  • freshness is recorded
  • provenance is preserved
  • client or project filtering exists
  • conflicting evidence is visible
  • evidence gaps are visible
  • retrieval and final reasoning are separated
  • weak sources cannot silently become fact

Pass condition:

External evidence can be retrieved without confusing retrieval with decision authority.

Fail condition:

Retrieved material is treated as automatically correct.

  1. Output Readiness

Check:

  • output type is known
  • output format is known
  • required fields are known
  • decision state is defined
  • metadata is defined
  • source references are required
  • known gaps must be shown
  • destination-specific format exists
  • developer format exists where relevant
  • client format exists where relevant

Pass condition:

The AI Employee knows what to produce before execution begins.

Fail condition:

The AI can choose any format or omit required fields.

  1. Review Readiness

Check:

  • reviewer role is defined
  • reviewer receives original task
  • reviewer receives source evidence
  • reviewer is separate from producer where required
  • review verdicts are defined
  • revision route exists
  • reviewer authority is clear

Pass condition:

Important output receives a meaningful quality review.

Fail condition:

The producing agent informally approves its own work.

  1. Independent Review Readiness

Check:

  • independent review requirement is defined
  • reviewer independence is genuine
  • different role, model family, Brain or human is used where required
  • high-risk assumptions are challenged
  • source is available to reviewer
  • review verdict is recorded
  • unresolved disagreement can escalate

Pass condition:

High-risk output can be challenged independently.

Fail condition:

The reviewer repeats the producer’s reasoning without independent evidence.

  1. Validation Readiness

Check:

  • validation level is defined
  • checklist exists
  • validation owner is defined
  • source-grounding check exists
  • Brain-routing check exists
  • risk check exists
  • output-format check exists
  • duplication check exists where needed
  • developer-boundary check exists
  • compliance check exists
  • permission check exists
  • completion criteria are defined

Pass condition:

Output can be formally checked before use.

Fail condition:

Output becomes operational because it appears polished.

  1. Exchange Zone And Handoff Readiness

Check:

  • sender is known
  • receiver is known
  • Work Unit ID travels with the output
  • completed work is summarised
  • unresolved work is visible
  • validation status travels
  • risk level travels
  • source context travels
  • failure history travels
  • dependencies travel
  • receiver acceptance state exists
  • next action is clear
  • next owner is clear
  • stop conditions are preserved

Pass condition:

The receiver can continue without guessing.

Fail condition:

Output is passed without sufficient context, state or ownership.

  1. Dependency Readiness

Check:

  • input dependencies are listed
  • context dependencies are listed
  • validation dependencies are listed
  • approval dependencies are listed
  • tool-permission dependencies are listed
  • sequence dependencies are listed
  • Critical Dependencies are identified
  • dependency states can be tracked
  • blocked dependency escalation exists

Pass condition:

Required conditions are visible and satisfied before progression.

Fail condition:

Work continues while material dependencies remain hidden or partially satisfied.

  1. Failure Handling Readiness

Check:

  • likely failure modes are listed
  • failure type can be classified
  • severity is defined
  • stop condition exists
  • containment action exists
  • revision path exists
  • reroute path exists
  • park or reject option exists
  • evidence is preserved
  • failure log exists
  • Kaizen learning path exists

Pass condition:

The workflow can fail safely.

Fail condition:

Failure creates hidden errors, repeated loops or unsafe continuation.

  1. Rescue Routing Readiness

Check:

  • failure threshold is defined
  • standard two-failure rule is applied where appropriate
  • failure count persists across sessions
  • Rescue Packet can be created
  • Rescue Agent is defined
  • Rescue Route is materially different
  • new verification gate is defined
  • human escalation exists where required
  • restart approval is defined

Pass condition:

Repeated failure triggers changed reasoning or execution.

Fail condition:

The workflow repeats the same failed method indefinitely.

  1. Escalation Readiness

Check:

  • escalation to Martyn is defined
  • escalation to HeadOffice is defined
  • escalation to M is defined where appropriate
  • escalation to Research Brain is defined
  • escalation to Finance Brain is defined
  • escalation to Compliance or Risk Review is defined
  • human review triggers are defined
  • automation-stop triggers are defined
  • client escalation is defined where relevant

Pass condition:

High-risk or uncertain work has a clear escalation route.

Fail condition:

The AI continues beyond authority because escalation is vague.

  1. Logging And Observability Readiness

Check:

  • task log exists
  • event log exists
  • model route is recorded
  • tool action is recorded
  • validation is recorded
  • handoff is recorded
  • failure count is recorded
  • rescue is recorded
  • human approval is recorded
  • outcome is recorded
  • status history exists
  • closure is recorded

Pass condition:

The workflow leaves a sufficient audit trail.

Fail condition:

Important activity cannot be reconstructed later.

  1. Cost Visibility Readiness

Check:

  • model cost can be estimated or tracked
  • tool cost can be tracked
  • automation-platform cost is known
  • review cost is recognised
  • retry and rework cost is visible
  • developer cost is visible where relevant
  • cost threshold exists
  • cost-to-outcome review exists

Pass condition:

MWMS can judge whether the workflow creates proportionate value.

Fail condition:

The workflow runs repeatedly without visibility into total cost.

  1. Outcome Measurement Readiness

Check:

  • expected outcome is defined
  • outcome category is known
  • outcome state can be recorded
  • success metric exists
  • failure metric exists
  • quality metric exists
  • business value can be assessed
  • outcome evidence exists
  • output is separated from outcome
  • continuation decision can be made

Pass condition:

MWMS can determine whether the workflow was useful.

Fail condition:

The workflow produces activity but no measurable outcome.

  1. Human Review Readiness

Human review is required when the workflow affects:

  • MCR
  • Brain architecture
  • developer implementation
  • M’s active work
  • live WordPress systems
  • database writes
  • external emails
  • paid traffic
  • finance
  • compliance
  • public content
  • client-facing reports
  • destructive actions
  • high-risk automation

Check:

  • human owner is identified
  • decision required is clear
  • review package is complete
  • approval state can be recorded
  • rejection and revision are supported

Pass condition:

Human review exists wherever risk demands it.

Fail condition:

High-risk work can proceed without accountable human decision.

  1. Developer Boundary Readiness

Check:

  • does it touch Brain Room systems?
  • does it touch executor hooks?
  • does it touch Opportunity System wiring?
  • does it touch Affiliate workflow systems?
  • does it touch HeadOffice reporting?
  • does it touch live plugin files?
  • does it create instructions for M?
  • are exact files or screens known?
  • is the current save point known?
  • is “what not to touch” explicit?
  • are test steps defined?

Pass condition:

Developer boundary is clear and safe.

Fail condition:

The workflow creates vague build risk or interferes with M’s active area.

  1. Dashboard Readiness

Check:

  • dashboard item is specific
  • action type is clear
  • priority is believable
  • urgency is believable
  • Owning Brain is clear
  • owner is clear
  • status is clear
  • source is clear
  • next action is clear
  • item is not duplicate noise
  • stale items can be removed
  • alerts can be suppressed

Pass condition:

Dashboard output is action-ready.

Fail condition:

Dashboard output is vague, repetitive or ownerless.

  1. Persistent Agent Readiness

Check:

  • persistent-agent owner exists
  • schedule or trigger is defined
  • approved source is defined
  • baseline is known
  • duplicate suppression exists
  • alert threshold exists
  • cost limit exists
  • failure threshold exists
  • stale-context detection exists
  • last-good state is preserved
  • pause and shutdown controls exist
  • human exception review exists
  • restart approval exists

Pass condition:

The persistent agent can run safely, remain observable and stop when degraded.

Fail condition:

The agent continues indefinitely without owner, cost control or shutdown.

  1. Knowledge Commitment Readiness

Check:

  • durable destination is defined
  • source authority is sufficient
  • duplication check exists
  • validation is complete
  • human approval exists where required
  • status is correct
  • version is recorded
  • unresolved assumptions are blocked
  • save point can be created
  • incorrect knowledge can be corrected or deprecated

Pass condition:

Only validated and approved learning becomes durable MWMS knowledge.

Fail condition:

Draft, ambiguous or unverified output can silently become organisational truth.

  1. Session Continuity Readiness

Check:

  • completed work can be recorded
  • incomplete work can be separated
  • decisions can be recorded
  • failures can be recorded
  • current save point can be recorded
  • exact next action can be recorded
  • next owner can be identified
  • status can carry into the next session
  • failure count persists
  • active source references persist

Pass condition:

The next session can resume without repeating completed work.

Fail condition:

Continuity depends on rereading the entire conversation.

  1. Automation Readiness

Check:

  • manual workflow has been tested
  • input pattern is predictable
  • output structure is stable
  • validation exists
  • failure modes are known
  • logs exist
  • human gates exist
  • stop conditions exist
  • Rescue Route exists
  • rollback or correction exists
  • business value is proven
  • automation scope is narrow
  • shutdown is possible

Pass condition:

Automation reduces work without increasing uncontrolled risk.

Fail condition:

Automation is being added because it is technically possible.

  1. Client Readiness

For future AIBS client systems, check:

  • client process is mapped
  • client input is normalized
  • client data is isolated
  • privacy controls exist
  • role ownership is clear
  • permission boundaries exist
  • output is plain-language
  • approval gates exist
  • unsupported claims are blocked
  • audit logs exist
  • human review exists
  • value outcome is measurable
  • failure handling is safe
  • shutdown and revocation exist

Pass condition:

Client-facing AI is useful, safe, understandable and accountable.

Fail condition:

The client receives confusing, unsafe or overconfident AI output.

Critical Readiness Gates

The following items are Critical Gates:

  • Owning Brain
  • Role Card
  • authority boundary
  • Work Unit
  • source and input
  • workflow pipeline
  • model suitability
  • tool permissions
  • validation
  • human approval
  • failure handling
  • Rescue Route
  • outcome measurement
  • developer boundary
  • client isolation where relevant
  • shutdown control for persistent agents

Rule

If any required Critical Gate fails, deployment must not proceed at the requested level.

Deployment Verdicts

Not Ready

Use when:

  • purpose is vague
  • ownership is unclear
  • Role Card is missing
  • workflow is unclear
  • validation is missing
  • risk is uncontrolled
  • outcome is undefined

Decision:

Do not deploy.

Draft Only

Use when:

  • idea is useful
  • structure is incomplete
  • role or pipeline needs more work
  • output format is unproven

Decision:

Continue designing and testing.

Manual Use Only

Use when:

  • purpose is clear
  • output is useful
  • workflow can be supervised
  • automation is premature

Decision:

Use manually and observe.

Assisted Use Approved

Use when:

  • Role Card exists
  • pipeline exists
  • model route is suitable
  • validation exists
  • handoff exists
  • human review remains required
  • failures can be contained

Decision:

Use inside a controlled human-reviewed workflow.

Controlled Automation Approved

Use when:

  • manual version is proven
  • inputs are predictable
  • outputs are stable
  • permissions are controlled
  • logs exist
  • failure threshold exists
  • Rescue Route exists
  • human review handles exceptions
  • shutdown is available

Decision:

Automate within approved boundaries.

Restricted Autonomous Use Approved

Use only when:

  • task is low risk
  • workflow is highly stable
  • output is predictable
  • failures are safe
  • logs are reliable
  • cost is controlled
  • periodic review exists
  • shutdown is available

Decision:

Allow limited autonomous operation.

Deployment Suspended

Use when:

  • readiness has materially degraded
  • Critical Gate fails
  • repeated failures occur
  • client or compliance risk appears
  • cost exceeds value
  • persistent-agent controls fail

Decision:

Pause, contain, rescue and revalidate before restart.

Deployment Readiness Scorecard

MWMS may score readiness from 0 to 6.

0 — Concept Only
1 — Draft Ready
2 — Manual Operation Ready
3 — Assisted Automation Ready
4 — Controlled Automation Ready
5 — Restricted Autonomous Ready
6 — Deployment Suspended

Default rule:

New AI Employees should begin at Level 1 or Level 2 unless stronger readiness is proven.

No workflow should skip readiness levels merely because the technology appears capable.

Deployment Readiness Template

AI Employee Or Workflow Name:

Readiness Review ID:

Work Unit ID:

Owning Brain:

Supporting Brains:

Purpose:

Expected Business Outcome:

Deployment Type:

Current Readiness Level:

Requested Readiness Level:

Primary Use Case:

Risk Level:

Input Sources:

Source Authority:

Output Types:

Role Card Complete:

Authority Boundary Defined:

Agentic Work Unit Defined:

Pipeline Defined:

Context Pack Defined:

Model Route Defined:

Independent Review Defined:

Tool Permissions Defined:

Execution Verification Defined:

External Knowledge Controls Defined:

Validation Defined:

Handoff Defined:

Dependencies Defined:

Failure Handling Defined:

Failure Threshold Defined:

Rescue Route Defined:

Escalation Path Defined:

Human Review Required:

Logging Available:

Cost Tracking Available:

Outcome Measurement Defined:

Persistent Agent Controls Defined:

Knowledge Commitment Defined:

Session Continuity Defined:

Developer Boundary Checked:

Dashboard Impact:

Client Impact:

Critical Gates Passed:

Risks:

Missing Pieces:

Deployment Verdict:

Approved Scope:

Forbidden Scope:

Next Action:

Reviewer:

Approval Owner:

Date:

Review Expiry Or Next Review Date:

Application To Newsletter Intelligence

Before deployment or expansion, check:

  • newsletter source is stable
  • body extraction is reliable
  • cleaning rules exist
  • signal extraction is specific
  • source is preserved
  • Brain routing exists
  • action types are defined
  • validation catches generic output
  • queue review exists
  • dashboard noise is controlled
  • human review precedes material action
  • logging exists
  • failure states are visible
  • duplicate suppression exists
  • persistent monitoring can be paused

Recommended readiness:

Manual, Assisted or Controlled Automation depending on proven stability.

No autonomous major downstream action.

Newsletter Deployment Rule

Newsletter AI may extract and queue intelligence.

It must not create major operational actions without review.

Application To Course Absorption

Before deployment, check:

  • course source is complete
  • source references are preserved
  • value filter exists
  • MCR comparison exists
  • duplicate check exists
  • update-before-create rule exists
  • page format is known
  • status is preserved
  • human review is mandatory
  • weak material can be parked or rejected
  • session save point can be recorded
  • failure count persists

Recommended readiness:

Manual Use or Assisted Use.

Controlled Automation only after repeated proof.

Course Deployment Rule

Course absorption should remain human-reviewed because MCR quality and page control matter.

Application To Brain Room

Before deployment, check:

  • messages can be captured
  • mixed requests can be normalized
  • Owning Brain can be assigned
  • Work Units can be created
  • AI Manager route exists
  • validation exists
  • human review applies to medium and high risk
  • event logs work
  • duplicate task creation is controlled
  • M’s active work is protected
  • session continuity is preserved

Recommended readiness:

Assisted Use first.

Controlled Automation only for low-risk task drafts.

Application To Offer Evaluation

Before deployment, check:

  • intake fields exist
  • claims are separated from evidence
  • current research rules exist
  • Research Brain handoff exists
  • Compliance review exists
  • Finance review exists
  • Experimentation review exists
  • HeadOffice verdict exists
  • human review is mandatory before spend
  • no autonomous approval exists

Recommended readiness:

Manual or Assisted Use only.

Application To M Developer Support

Before deployment, check:

  • exact site is known
  • exact file or screen is known
  • evidence is current
  • current save point is known
  • exact instruction is defined
  • what not to touch is defined
  • test steps exist
  • expected result exists
  • human review is mandatory
  • implementation outcome is verified
  • no autonomous live changes exist

Recommended readiness:

Manual Use only.

Assisted drafting may be used.

Application To Persistent Agents

Before deployment, check:

  • owner exists
  • schedule exists
  • source is approved
  • last-good state is known
  • alert threshold exists
  • duplicate suppression exists
  • cost limit exists
  • failure threshold exists
  • shutdown exists
  • human escalation exists
  • restart requires approval

Recommended readiness:

Assisted or Controlled Automation only after manual proof.

Application To AIBS Client Systems

Before deployment, check:

  • client process is mapped
  • AI Employee roles are clear
  • inputs are understood
  • client data is isolated
  • permissions are narrow
  • approval gates exist
  • report format is simple
  • risk is assessed
  • human review exists
  • logs exist
  • value is measurable
  • failure handling is safe
  • shutdown and revocation exist

Recommended readiness:

Manual or Assisted client pilot first.

Controlled Automation only after proof.

Restricted autonomous use only for low-risk internal actions.

Deployment Failure Modes

Common failure modes include:

  • deploying without a Role Card
  • automating before manual stability
  • using an unsuitable model
  • allowing output without validation
  • giving tools before permissions
  • treating tool response as outcome
  • using self-review as independent review
  • omitting failure thresholds
  • repeating failed routes without rescue
  • creating dashboard noise
  • skipping human review
  • sending M vague instructions
  • creating client output without approval
  • failing to measure outcomes
  • ignoring total cost
  • operating persistent agents without shutdown
  • overlapping AI Employee responsibilities
  • treating a good prompt as a complete system
  • treating a course concept as deployment-ready
  • moving from MCR concept to build too quickly
  • forgetting M’s active development boundary
  • losing session continuity
  • committing unverified output as knowledge

Any deployment showing these signs should be paused.

Deployment Gate

Before any AI Employee or workflow is deployed, HeadOffice should confirm:

Purpose is clear.

Owning Brain is clear.

Role Card exists.

Authority boundary exists.

Work Unit structure exists.

Pipeline exists.

Input process is understood.

Context is available.

Model route is suitable.

Tool permissions exist.

Execution can be verified.

External knowledge controls exist where required.

Output format exists.

Review exists.

Independent review exists where required.

Validation exists.

Handoff exists.

Dependencies are controlled.

Failure handling exists.

Rescue Route exists.

Escalation exists.

Human review is defined.

Logging exists.

Cost visibility exists.

Outcome measurement exists.

Persistent-agent controls exist where relevant.

Knowledge commitment is controlled.

Session continuity exists.

Developer boundary is safe.

Dashboard impact is controlled.

Client impact is safe.

Automation level matches risk.

HeadOffice approves the readiness level.

If any required Critical Gate is missing, deployment must not proceed.

Manual Use Rule

This checklist should be applied manually before deployment-readiness review becomes technical infrastructure.

Manual use helps MWMS determine:

  • which controls are genuinely necessary
  • which workflows remain too vague
  • where model routing matters
  • where independent review adds value
  • where Rescue Routes are required
  • where persistent agents create risk
  • where cost exceeds value
  • which workflows may progress
  • which workflows should remain manual
  • which workflows should be suspended

Manual readiness proof comes before automated readiness scoring.

Future Plugin Or UI Relevance

This checklist may later support:

  • Deployment Readiness Review screen
  • AI Employee activation gate
  • AI Manager readiness control
  • Task Executor deployment state
  • HeadOffice AI Workforce Dashboard
  • persistent-agent approval control
  • Rescue Routing status
  • model-route approval
  • tool-permission verification
  • client workflow readiness panel

Possible future fields:

readiness_review_id

work_unit_id

ai_employee_workflow_name

owning_brain

supporting_brains

purpose

expected_business_outcome

deployment_type

current_readiness_level

requested_readiness_level

risk_level

source_authority

role_card_complete

authority_boundary_defined

work_unit_defined

pipeline_defined

context_pack_defined

model_route_defined

independent_review_defined

tool_permissions_defined

execution_verification_defined

external_knowledge_controls_defined

validation_defined

handoff_defined

dependencies_defined

failure_handling_defined

failure_threshold_defined

rescue_route_defined

escalation_defined

human_review_required

logging_available

cost_tracking_available

outcome_measurement_defined

persistent_agent_controls_defined

knowledge_commitment_defined

session_continuity_defined

developer_boundary_checked

dashboard_impact

client_impact

critical_gates_passed

risks

missing_pieces

deployment_verdict

approved_scope

forbidden_scope

next_action

reviewer

approval_owner

review_date

next_review_date

created_at

updated_at

No technical build is authorised by this checklist alone.

Governance Role

HeadOffice owns the MWMS AI Agent Deployment Readiness Checklist.

HeadOffice is responsible for:

  • approving readiness levels
  • approving deployment scope
  • preventing premature automation
  • requiring Role Cards
  • requiring authority boundaries
  • requiring model-route suitability
  • requiring validation
  • requiring independent review where needed
  • governing tool permissions
  • governing Rescue Routes
  • governing persistent agents
  • requiring cost visibility
  • requiring outcome measurement
  • protecting M’s active build
  • protecting MCR
  • protecting dashboards from noise
  • protecting client systems
  • suspending degraded deployments
  • approving restart after suspension

Individual Brains may propose deployments.

HeadOffice governs readiness for:

  • cross-Brain workflows
  • high-risk workflows
  • tool-enabled workflows
  • automated workflows
  • persistent agents
  • developer workflows
  • MCR workflows
  • client-facing workflows

Relationship To SIT Brain

SIT Brain may:

  • verify Critical Gates
  • verify Role Card completeness
  • verify authority boundaries
  • verify model-route suitability
  • verify tool permissions
  • verify execution evidence
  • verify independent review
  • verify validation status
  • verify failure thresholds
  • trigger Rescue Routing
  • pause persistent agents
  • detect false completion
  • block unsafe deployment
  • verify outcome evidence
  • verify knowledge-commitment controls
  • verify closure

Relationship To Data Brain

Data Brain supports:

  • readiness review records
  • Work Unit linkage
  • current readiness level
  • requested level
  • Critical Gate states
  • model routes
  • permission records
  • validation results
  • failure counts
  • Rescue Records
  • approval history
  • cost records
  • outcome evidence
  • deployment suspension records
  • restart records
  • review history

Relationship To Other MWMS Standards

This checklist supports and must align with:

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

This checklist acts as the deployment gate before those standards become operational workflows.

Drift Protection

This checklist protects MWMS from:

  • deploying AI roles before defining them
  • automating vague workflows
  • trusting outputs without validation
  • skipping independent review
  • using unsuitable models
  • granting tools without permissions
  • failing to verify execution
  • creating live-system risk
  • creating dashboard noise
  • creating client-facing AI before safety gates
  • confusing course concepts with deployable systems
  • asking M to build from incomplete standards
  • measuring output instead of outcomes
  • hiding cost
  • operating persistent agents without shutdown
  • omitting Rescue Routes
  • losing session continuity
  • committing unverified knowledge
  • moving from governance to implementation without readiness review

Deployment Readiness Drift Signals

MWMS should watch for:

  • no Readiness Review ID
  • no Work Unit ID
  • no Owning Brain
  • vague purpose
  • Role Card incomplete
  • authority unclear
  • input source unclear
  • pipeline incomplete
  • context missing
  • model route unexplained
  • permissions missing
  • expected system state undefined
  • execution unverified
  • reviewer not independent
  • validation missing
  • handoff missing
  • dependencies hidden
  • failure threshold absent
  • Rescue Route absent
  • logging missing
  • cost invisible
  • outcome undefined
  • persistent agent lacks shutdown
  • client isolation missing
  • knowledge destination unclear
  • session closure missing

Rule

Deployment drift must be corrected before additional authority or automation is granted.

Minimum Compliance Standard

A formal deployment-readiness review is compliant only when it defines:

  • Readiness Review ID
  • Work Unit ID
  • workflow or AI Employee
  • Owning Brain
  • purpose
  • expected outcome
  • deployment type
  • current level
  • requested level
  • risk
  • source authority
  • Role Card status
  • authority boundary
  • Work Unit status
  • pipeline status
  • context status
  • model route
  • independent review
  • tool permissions
  • execution verification
  • external knowledge controls
  • output definition
  • validation
  • handoff
  • dependencies
  • failure handling
  • failure threshold
  • Rescue Route
  • escalation
  • human review
  • logging
  • cost visibility
  • outcome measurement
  • persistent-agent controls
  • knowledge commitment
  • session continuity
  • developer boundary
  • dashboard impact
  • client impact
  • Critical Gate result
  • deployment verdict
  • approved scope
  • forbidden scope
  • next action
  • reviewer
  • approval owner
  • review date
  • next review date

Architectural Intent

The architectural intent of the MWMS AI Agent Deployment Readiness Checklist is to create a controlled bridge between AI system design and AI system operation.

MWMS will continue creating powerful:

  • AI Employees
  • role chains
  • workflows
  • standards
  • tool orchestrations
  • persistent agents
  • automation opportunities

Not all should be deployed immediately.

The system must distinguish between:

  • idea
  • draft
  • manual workflow
  • assisted workflow
  • controlled automation
  • restricted autonomous operation
  • suspended deployment

The long-term goal is that every AI Employee and workflow can answer:

  • Is it clearly defined?
  • Is it owned?
  • Is authority bounded?
  • Is the Work Unit structured?
  • Is input reliable?
  • Is context sufficient?
  • Is the model suitable?
  • Are tools permissioned?
  • Can execution be verified?
  • Is external knowledge controlled?
  • Is output defined?
  • Is review independent where required?
  • Is validation present?
  • Are handoffs safe?
  • Are dependencies satisfied?
  • Can failure be contained?
  • Is rescue defined?
  • Is escalation clear?
  • Is human review present?
  • Is logging available?
  • Is cost visible?
  • Are outcomes measurable?
  • Can persistent operation stop safely?
  • Is knowledge commitment controlled?
  • Can the next session resume correctly?
  • Is deployment safe at the requested level?

When MWMS can answer those questions, it can expand its AI workforce without losing control.

Strategic Summary

The v1.1 update expands the MWMS AI Agent Deployment Readiness Checklist from a broad operational-readiness gate into a full deployment lifecycle control.

The upgraded checklist now governs:

  • model-route readiness
  • multi-agent role separation
  • execution verification
  • independent review
  • Exchange Zone readiness
  • dependency readiness
  • deterministic Rescue Routing
  • observability
  • cost visibility
  • persistent-agent safeguards
  • external knowledge controls
  • knowledge commitment
  • session continuity
  • deployment suspension
  • restart readiness

The key shift is:

Deployment readiness is not proof that an AI can produce an output.

It is proof that the workflow can operate, fail, recover, stop, remain observable and create a verified outcome inside controlled authority boundaries.

Final Rule

No AI Employee or workflow should become operational until it can be assigned, contextualised, validated, handed off, measured, rescued and safely stopped if it fails.

No purpose, no deployment.

No owner, no accountability.

No Role Card, no formal AI Employee.

No model fit, no reliable execution.

No permission, no tool action.

No execution evidence, no completion claim.

No independent review, no high-risk trust.

No failure threshold, no controlled recovery.

No shutdown, no persistent agent.

No verified outcome, no operational success.

No approved knowledge commitment, no durable truth.

Change Log

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

Change:

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

Added:

  • Deployment Suspended readiness level
  • Readiness Principle
  • Model Route Readiness
  • Multi-Agent Role Separation Readiness
  • Tool Execution Verification Readiness
  • External Knowledge Readiness
  • Review Readiness
  • Independent Review Readiness
  • Exchange Zone And Handoff Readiness
  • Dependency Readiness
  • Rescue Routing Readiness
  • Logging And Observability Readiness
  • Cost Visibility Readiness
  • Persistent Agent Readiness
  • Knowledge Commitment Readiness
  • Session Continuity Readiness
  • Critical Readiness Gates
  • Deployment Suspended verdict
  • persistent-agent application
  • Manual Use Rule
  • Future Plugin Or UI Relevance
  • Relationship To SIT Brain
  • Relationship To Data Brain
  • Deployment Readiness Drift Signals
  • Minimum Compliance Standard
  • Strategic Summary
  • Final Rule

Expanded:

  • Purpose
  • Scope
  • Core Definition
  • readiness levels
  • checklist sections
  • deployment verdicts
  • scorecard
  • readiness template
  • workflow-specific applications
  • failure modes
  • deployment gate
  • governance
  • drift protection
  • architectural intent

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

Purpose of update:

To evolve the MWMS AI Agent Deployment Readiness Checklist from a general readiness gate into the complete activation, automation, suspension and restart control for AI Employees, multi-agent workflows, model routes, tools, persistent agents, external knowledge systems and future AIBS client workflows.

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

Change:

Created the MWMS AI Agent Deployment Readiness Checklist as the readiness gate for moving AI Employees, agentic workflows, Brain automations, task workflows, reporting systems and future AIBS client systems from concept into controlled operation.

Change Impact Declaration

This v1.1 update expands the AI Agent Deployment Readiness Checklist from a twenty-section deployment gate into a complete lifecycle readiness control covering model routing, independent review, dependency management, tool verification, Rescue Routing, cost visibility, persistent agents, knowledge commitment, session continuity, deployment suspension and safe restart.

Pages Created

None

Pages Updated

MWMS AI Agent Deployment Readiness Checklist

Pages Deprecated

None

Standalone Pages Not Created

MWMS Model Route Readiness Checklist

MWMS Persistent Agent Readiness Checklist

MWMS Rescue Route Readiness Checklist

MWMS Tool Execution Readiness Checklist

MWMS Deployment Suspension Protocol

Registries Requiring Update

None confirmed by the supplied source.

Canon Version Update Required

No

Change Log Entry Required

Yes

Strategic Absorption Result

MWMS gains a stronger deployment gate that prevents AI Employees and workflows from receiving operational authority until their roles, models, tools, validation, handoffs, failure controls, Rescue Routes, costs, persistent-agent safeguards, outcomes and shutdown mechanisms are proven.

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