Portable governed artifact

Shared Capability Foundations v0.1

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Versionv0.1
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AI Capability Playbook · shared vocabulary · v0.1

Shared Capability Foundations v0.1

Reusable terms, control concepts, and architectural primitives for moving AI work from demos and prototypes toward governed capability.

Shared vocabularySource authorityNon-inferenceSustainment
AI Capability Playbook Playbook Home Playbook Map Shared Foundations AI Capability Discipline MLL WESS Enterprise Architecture Review Assistant Prompt Packs
Shared core
Why shared foundations matter

Shared Capability Foundations gives the playbook a common operating vocabulary. It keeps doctrine, build-readiness work, reference implementations, and prompt packs from using different terms for the same governance and capability concepts.

Use it before a review, build decision, prompt-pack rendering, or implementation pattern discussion where terms such as source authority, evidence state, non-inference, eval, review, telemetry, or sustainment need to mean the same thing.

Reader path
From prototype to capability

The foundations explain what must be present before a useful AI artifact becomes governed capability. They do not approve a tool, data path, workflow, supplier, or production use case.

Shared Capability Foundations v0.1

Status: shared operating core candidate. Field guidance, not final policy.

1. Core thesis

A capability is not created by model access, a prompt, an agent, a demo, a Jira epic, or a clever assistant. A capability exists only when intent, authority, evidence, workflow, ownership, governance, telemetry, support, sustainment, and measurable value are credible enough to survive real work.

IntentWhat outcome should the capability create?
AuthorityWhich sources and owners can support claims and decisions?
WorkflowWhere does the capability fit into real work?
EvidenceHow will claims, evals, and review be tested?
SustainmentWho owns support, telemetry, drift, and retirement?

2. Capability formation equation

Governed Capability =
  Clear Intent
+ Approved Data Path
+ Source Authority
+ Workflow Fit
+ Schema Contracts
+ Harnesses
+ Rubrics
+ Valid Evals
+ Human Review
+ Tool Permissions
+ Observability
+ Governance Routing
+ Capacity and TCO Discipline
+ Sustainment Ownership
+ Measured Business Outcome
+ Stop or Retirement Criteria

Missing a major term does not make the idea useless. It means the idea is still discovery, sandbox, feasibility check, or bounded proof. Do not call it operational until the missing terms are resolved or explicitly accepted as limits.

Business outcome and value-line model

Value must be classified before it is judged. Start by naming the primary value class, then ask whether it clears the current acceptance threshold.

Value classMeaning
Direct savingsHard OPEX or CAPEX reduction visible to the accountable budget owner.
Indirect savingsSavings realized by another team, process, workstream, or downstream function.
Avoided costSpend, labor, tooling, audit, remediation, incident, or rework cost avoided.
Risk reductionReduced compliance, operational, cyber, quality, delivery, or resilience exposure.
Capacity releaseHuman time freed for higher-value work or reduced backlog pressure.
Quality or rework reductionFewer defects, escalations, corrections, review loops, failed handoffs, or repeat work.
Cross-workstream valueBenefit that accrues outside the proposing or funding team.

Outcome Acceptance Line means the minimum value class and evidence threshold a decision owner accepts for the current business climate, funding lane, or review context.

A proposal can create real value and still fall below the acceptance line if the wrong owner benefits, the evidence is weak, or the current mandate requires direct savings.

Use owner terms only where they clarify accountability: decision owner approves the work, budget owner owns the impacted budget, benefiting owner receives the measurable benefit, and evidence owner proves the value claim.

All value classes matter, but they do not matter equally in every business climate. When management is under an operating-expense reduction mandate, direct savings from the accountable operational budget may be the only value class that materially changes the decision. Indirect savings, avoided cost, risk reduction, capacity release, quality improvement, and cross-workstream value may still be real, but they may not clear the line without sponsorship, exception, or a different priority signal.

3. Canonical reusable primitives

CapabilityA governed operating ability with intent, evidence, workflow, ownership, controls, telemetry, sustainment, and measurable outcome.
SandboxLow-risk personal or local exploration with no sensitive data, recurring team use, operational reliance, or meaningful shared capacity.
Feasibility checkA short time-boxed investigation to answer one specific unknown before more work is justified.
PreflightA lightweight decision checkpoint before meaningful capacity, contractor effort, Jira epic, service-stack work, recurring use, or governance-sensitive activity begins.
Source authorityThe ranked status of evidence sources, including canonical, governed reference, submission evidence, derived analysis, historical, stale, and prohibited.
Evidence stateThe status of a claim relative to available evidence: supported, not evidenced, conflicting evidence, requires confirmation, requires escalation, or not applicable.
Non-inferenceA rule preventing a model, assistant, or workflow from silently deciding approval, classification, source authority, funding, operational readiness, or other governed facts.
Human overrideA captured reviewer action that accepts, edits, rejects, or overrides assistant output with rationale and evidence or judgment basis.
Stop conditionA defined trigger to stop, park, quarantine, remediate, narrow, rebuild, or retire a proof or operationalized capability.
Runtime-portable capability designPreserves durable capability assets outside any single model, chat, IDE, agent, SaaS tool, or orchestration runtime.
Owned workspace, replaceable runtimeThe governed workspace is the durable brain. The runtime is replaceable. Chat history, tool sessions, IDE state, and model memory are not source authority.
Agent Experience / AXThe degree to which an AI agent can safely understand, navigate, validate, and modify a governed repo or workflow without guessing.
Procedure skillA reusable governed procedure with defined purpose, trigger, scope, inputs, outputs, evidence, risk, owner, fixture coverage, stop conditions, review cadence, and retirement condition.
External pattern intakeA verification path for outside transcripts, demos, practitioner claims, vendor claims, and productivity anecdotes before any pattern becomes reusable doctrine.
Harness feedback loopRoutes accepted edits, overrides, rejects, and escalations back to the right issue, control, schema, fixture, instruction, reviewer calibration, or no-change decision.
Queue-based agent work modelA work-state model for agent-addressable tasks that separates queue movement from approval. Queue state does not approve work.

4. Readiness and lane model

Universal readinessAI Capability DisciplineMLL WESSEnterprise Architecture Review Assistant
IdeaLevel 0: IdeaSandboxConcept or intake shaping.
Prompt or local prototypeLevel 1: Prompt artifactSandboxManual exploration or draft prompt.
Reusable harnessLevel 2: Reusable harnessTime-boxed feasibility checkManual harness rehearsal.
Structured workflowLevel 3: Structured workflowPreflight candidateEvidence loop design.
Eval-backed assistantLevel 4: Eval-backed assistantPreflight-approved proofControlled implementation proof.
Governed pilotLevel 5: Governed pilotPreflight-approved proof or early operationalization candidateSmall reviewer pilot with approved data path.
Operational capabilityLevel 6: Operational capabilityOperationalized capabilitySupported review assistant.
Scaled enterprise capabilityLevel 7: Scaled enterprise capabilityHigh-control operationalized capabilityEnterprise integrated review capability.

Queue-based agent work states

Queue-based agent work can use common states when the states help owners route work without turning the queue into an approval system.

Queue stateMeaning
intakeWork has been captured but not yet screened for scope, source authority, risk, or owner fit.
triageThe item is being sorted for owner, priority, dependency, source class, and execution surface.
feasibilityThe item is being checked for enough evidence, constraints, and execution path to justify build work.
buildA bounded implementation or artifact update is in progress under the approved scope.
reviewThe output is ready for human, owner, governance, or validation review.
blockedWork cannot proceed until a missing owner decision, evidence item, dependency, permission, or constraint is resolved.
remediationA reviewed output needs correction before it can close or move forward.
retirementThe work, pattern, harness, or procedure is being closed, narrowed, superseded, or removed from active use.

Queue state is not approval. Approval still requires the proper owner, evidence, and governance path.

5. Source authority and evidence states

Source classCan support findings?Required handling
CanonicalYesCite source, owner, and version.
Governed referenceYes, with contextCite source and explain limitation.
Submission evidenceYes for what was submittedMark as submission evidence, not policy.
Structured source of truthYes for current stateUse for current ownership, status, lifecycle, and workflow facts.
Derived analysisNo by itselfMust cite underlying evidence.
Historical or staleConditional or no for current decisionsUse only with date, version, applicability, and supersession checks.
ProhibitedNoDo not use as evidence.
Evidence stateMeaningRequired behavior
supportedClaim is backed by allowed evidence.Cite or reference evidence.
not_evidencedRequired evidence is missing.Do not infer. Request evidence or block promotion.
conflicting_evidenceSources disagree.Route to owner or human confirmation.
requires_confirmationA qualified owner must confirm.Keep unresolved until confirmed.
requires_escalationGovernance or management path must decide.Route before build, promotion, or operational use.
not_applicableControl does not apply based on documented facts.State basis.

6. Non-inference and routing

Assistants, agents, reviewers using model output, and repo automation must not infer approval status, funding approval, capacity approval, source authority, data classification, privacy posture, GxP or quality impact, architecture clearance, technology approval status, production readiness, operational readiness, exception approval, Jira readiness, value realization, or support-load reduction.

If evidence is absent, use an explicit unresolved state such as `not_evidenced`, `requires_confirmation`, `requires_approval`, `requires_routing`, `blocked`, or `not_applicable`.

Runtime-portable design does not imply runtime or tool equivalence. A governed capability can move across runtimes only when the same boundaries, inputs, outputs, validation, permissions, and evidence contracts are preserved.

These shared primitives do not imply policy approval, tool approval, data approval, production approval, autonomous-governance approval, model approval, vendor approval, security approval, legal approval, GxP approval, or enterprise deployment approval.

Route before build or promotion when work touchesExamples
Security, ISRM, privacy, legal, TQ, or qualityIdentity, secrets, personal data, GxP, validation, retention, audit posture, or regulated-process impact.
Platform, architecture, service owner, or operationsCopilot Studio, Azure, AWS, APIs, enterprise connectors, source-system dependency, endpoint workflows, support process, or monitoring.
Management, capacity, or agent scopeBudget impact, contractor consumption, priority tradeoff, shared agents, scheduled flows, publication to Teams or Copilot, or enterprise audience.

7. Evaluation, review, and sustainment

Control areaMinimum expectation
Fixture disciplineTest golden, incomplete, conflicting, adversarial, ambiguous, restricted, possible GxP, missing-security, non-AI alternative, and regression cases where relevant.
Human reviewReviewer has authority, evidence visibility, time, clear states, captured rationale, traceable decisions, and feedback into prompts, controls, schemas, fixtures, and source authority.
Capacity and TCOEstimate owner time, technical lead time, employee and contractor effort, operations, documentation, governance review, recurring sustainment, platform cost, and opportunity cost.
Sustainment classClassify as disposable, best-effort, team-supported, service-supported, or high-control.
Retirement triggerRetire, narrow, rebuild, or quarantine when source authority cannot be maintained, workflow changes beyond harness design, review burden exceeds value, false-negative risk appears, permissions cannot be governed, users ignore output, or a better platform capability exists.

Runtime-portable assets

Durable capability assets include governed files, schemas, manifests, source maps, catalogs, receipts, decisions, validation fixtures, and authoritative source systems.

The governed workspace is the durable brain. The runtime is replaceable. Chat history, tool sessions, IDE state, and model memory can provide context, but they are not source authority.

Good AX includes clear file paths, manifests, receipts, tests, validators, schemas, stop conditions, issue links, ownership boundaries, and closeout expectations.

Procedure skill design standard

Procedure skills must define purpose, trigger, scope, required inputs, allowed outputs, evidence requirements, context exposure risk, owner, fixture coverage, stop conditions, review cadence, and retirement condition.

External pattern intake and verification

External pattern intake requires normalization, source identity, presenter or source credibility check, claim classification, sponsor and commercial incentive split, credibility scoring, safe reuse labels, unsafe reuse labels, and framework mapping.

External transcripts, demos, practitioner claims, vendor claims, and productivity anecdotes must not become doctrine without verification and owner approval.

Harness feedback loop

Accepted edits, overrides, rejects, and escalations must route to source issue, control issue, schema gap, fixture gap, prompt or instruction gap, reviewer calibration, or no-change decision.

8. Use rules for the playbook family

PackageHow to use the shared foundations
AI Capability DisciplineReference the shared foundations as common doctrine and vocabulary while keeping teaching examples broad.
MLL WESS Development Commitment and Build-Readiness FrameworkUse the shared foundations as the discipline layer, then add WESS-specific decision rights, capacity rules, and preflight triggers.
Enterprise Architecture Review AssistantUse the shared foundations as the architecture discipline layer, then add EA-specific source authority, controls, evidence states, reviewer workflow, and platform path.
Prompt PacksUse the shared foundations to keep generated or rendered explanations aligned with playbook terms and policy boundaries.

9. What not to do

Do not treat readiness scores as approval. Do not promote any package to policy without source-owner validation. Do not let the assistant, agent, Jira story, model, or vendor become the decision authority.

Do not treat runtime portability as approval to use a different model, vendor, tool, data class, security posture, legal posture, GxP posture, production runtime, or enterprise deployment path.

10. Future refinement candidates

The previous public page carried a raw planning table. That backlog-like content has been reframed here as future refinement candidates so it does not interrupt the public reader flow or imply approved roadmap scope.

Useful future refinements include field-tested examples, a compact glossary, stronger evidence-state alignment checks, practical WESS checklists, a teaching module version of AI Capability Discipline, a source-owner validation receipt model, and adoption telemetry. These remain candidates until owner validation and issue-level prioritization confirm scope.