Industrial Agentic AI Readiness & Evidence Agent

10 readiness dimensionsPublished methodology

R0-R6 authority modelPublished boundary model

Eight governed industrial skillsDocumented across the existing Industrial IQ engines

Human Approval MatrixPublished decision-owner model

Agent Risk RegisterPublished public control register

Seven synthetic MRO scenariosControlled deterministic demonstration

Read-only Evidence AgentR0-R2 controlled demonstration

Evidence Agent pilotPre-pilot controls required

Operational transaction submissionNot supported

Operational actionOutside current product scope

Industrial AI CoE role

Agentic AI readiness is the agency-control layer inside the AI2COE operating model.

AI2COE treats Agentic AI as a governed readiness question, not a ninth product engine. The page helps teams decide whether agency is justified only after source data, tool access, identity, authority, review, security, evaluation, and escalation boundaries are inspectable.

CoE frameworkIndustrial AI Centre of Excellence Framework

Platform evidenceEight Industrial IQ diagnostic engines

Trust boundaryRead-only evidence, no ERP write-back, and human review before action.

Direct answer

Industrial Agentic AI Readiness is evidence for deciding whether agency is justified.

Industrial Agentic AI Readiness is the ability of an industrial organization to prove that a proposed agent has a valid use case, trustworthy operational data, sufficient industrial context, approved tools, a unique identity, bounded authority, named human approvals, measurable evaluation criteria, and a safe deployment model before it is allowed to recommend or execute operational action.

In AI2COE language, evidence-first Industrial Agentic AI means a proposed agent must be traceable, bounded, reviewable, and safer than simpler alternatives before it is promoted beyond read-only evaluation.

Current AI2COE boundary

AI2COE offers an equal-weight readiness methodology and a deterministic synthetic read-only demonstration. It does not claim a production operational agent, live MCP/A2A/OPC UA integration, autonomous remediation, or approved customer outcome proof.

Agentic necessity test

Use the least complex operating model that can solve the problem.

Approach	Use when	Control boundary
Analytics	A stable question can be answered from prepared data	No dynamic tool planning
Deterministic workflow / RPA	Steps and exceptions are known in advance	Rules own the sequence
Copilot	A human remains the primary planner and operator	Advice or drafting only
Read-only evidence agent	The goal is bounded but evidence requires multi-step context and tool selection	R0-R2 with human review
Approval-gated workflow agent	A future workflow requires transaction drafts and named approvals	R3-R4; not implemented by AI2COE

Ten readiness dimensions

Assess the complete operating system around the proposed agent.

Use-case fit

Does the workflow require variable multi-step planning rather than analytics, rules, RPA, or a copilot?

Data readiness

Are required fields, lineage, freshness, ownership, retention, and access understood?

Industrial context readiness

Can materials, stock states, sites, suppliers, assets, BOMs, work orders, and criticality be related?

Tool and interoperability readiness

Are approved tools, schemas, errors, timeouts, permissions, and transaction boundaries defined?

Identity and authorization readiness

Does the proposed agent have a unique identity, scoped credentials, least privilege, and auditable authorization?

Decision-rights readiness

Are retrieve, analyse, recommend, draft, submit, and execute rights explicitly separated?

Human-oversight readiness

Are owners, approval thresholds, escalation, override, rejection, deferral, and review service levels defined?

Security and safety readiness

Are injection, data disclosure, tool misuse, cross-tenant exposure, replay, poisoning, and runaway-loop risks controlled?

Evaluation and observability readiness

Can the team measure tool choice, sequence, evidence coverage, abstention, policy compliance, latency, and repeatability?

Value and deployment readiness

Are the baseline, target outcome, owners, pilot population, review capacity, support model, and exit plan defined?

Interactive controlled demo

Run the Industrial Agentic AI Readiness Diagnostic.

Choose the current planning state for each dimension. Inputs are not persisted and must not contain private operational data.

Bounded use-case goalDescribe the decision, not a command to a production system.

Use-case fitDoes the workflow require variable multi-step planning rather than analytics, rules, RPA, or a copilot?

Data readinessAre required fields, lineage, freshness, ownership, retention, and access understood?

Industrial context readinessCan materials, stock states, sites, suppliers, assets, BOMs, work orders, and criticality be related?

Tool and interoperability readinessAre approved tools, schemas, errors, timeouts, permissions, and transaction boundaries defined?

Identity and authorization readinessDoes the proposed agent have a unique identity, scoped credentials, least privilege, and auditable authorization?

Decision-rights readinessAre retrieve, analyse, recommend, draft, submit, and execute rights explicitly separated?

Human-oversight readinessAre owners, approval thresholds, escalation, override, rejection, deferral, and review service levels defined?

Security and safety readinessAre injection, data disclosure, tool misuse, cross-tenant exposure, replay, poisoning, and runaway-loop risks controlled?

Evaluation and observability readinessCan the team measure tool choice, sequence, evidence coverage, abstention, policy compliance, latency, and repeatability?

Value and deployment readinessAre the baseline, target outcome, owners, pilot population, review capacity, support model, and exit plan defined?

Equal weights are a versioned methodology starting point and are not presented as scientifically validated.

Decision rights

Authority increases only after evidence and named approval.

Class	Capability	Current boundary
R0	Retrieve approved information	Allowed in the controlled demo
R1	Analyse and generate evidence	Allowed in the controlled demo
R2	Recommend a human-reviewed action	Allowed in the controlled demo
R3	Draft a transaction or work package	Future; approval design required
R4	Submit after named approval	Future; not implemented
R5	Execute a bounded reversible action	Not near-term
R6	Closed-loop physical control	Out of scope

Human Approval Matrix

Every consequential interpretation has a named decision owner.

Decision	Owner	Required evidence	Safe fallback
Accept a review candidate	Named domain owner	Source references, limitations, confidence, and exception context	Request more data or defer
Assign remediation analysis	Process owner	Accepted finding and accountable assignee	Reject or return to review
Use a financial exposure band	Finance owner	Approved valuation and currency assumptions	Keep as unquantified hypothesis
Advance to a controlled pilot	Executive sponsor, security, data owner	Isolation, identity, policy, evaluation, incident, and exit controls	Remediate foundations first
Execute an operational change	Outside current AI2COE scope	Not available in the controlled demo	Use customer-controlled systems and approvals

Industrial context graph

Connect operating entities before asking an agent to reason across them.

MaterialManufacturer / MPNInventory positionSite / storage stateSupplier / POAsset / BOMWork order / criticalityEvidence / owner / policy

The methodology starts from exported data. OPC UA is assessed as a future semantic-readiness option; this page does not claim a live OPC UA connection or digital-twin feed.

Eight governed skills

The solution coordinates existing engines; it is not a ninth engine.

Skill contract	Industrial IQ engine	Authority	Responsibility
partscleanse.resolve_material_identity.v1	PartsCleanse AI	R1	Material identity and duplicate-family evidence
inventorymind.analyse_stock_posture.v1	InventoryMind AI	R1	Stock posture, reservation, quality-state, and availability evidence
procuremind.review_purchase_path.v1	ProcureMind AI	R1	Purchase history, supplier aliases, emergency flags, and price context
financemind.qualify_exposure.v1	FinanceMind AI	R2	Assumption-labelled financial exposure for owner review
assetmind.map_asset_context.v1	AssetMind AI	R1	Asset, equipment, functional-location, and BOM context
reliabilitymind.review_work_context.v1	ReliabilityMind AI	R1	Work-order, maintenance, and criticality context
readymind.validate_source_fit.v1	ReadyMind AI	R1	Data sufficiency, use-case fit, and readiness limitations
governancemind.apply_evidence_policy.v1	GovernanceMind AI	R2	Evidence class, confidence, policy, owner, and review routing

Read-only Industrial Evidence Agent

Test seven synthetic MRO evidence scenarios.

The deterministic demonstration chooses only the scenario-required versioned skills, preserves source references and legitimate exceptions, enforces an execution budget, abstains when context is insufficient, creates a tamper-evident sample event chain, and routes the result to human review.

Agent Trajectory Viewer

Trace the sample agent path before trusting the answer.

The public viewer exposes the bounded goal, plan summary, selected skill contracts, evidence class, confidence, exception, owner, and policy result. It does not expose private reasoning or permit live tool execution.

Sample sessionDEMO-REPEAT-PURCHASE-CANDIDATE-001

Authority ceilingR0-R2

External calls0 model / 0 network

Source writes0

Plan summary

#	Step	Control
1	Validate the bounded goal and synthetic scenario contract	Policy-bound sample trajectory
2	Classify source fields as untrusted data and disable persistent memory	Policy-bound sample trajectory
3	Build only the industrial context required by this scenario	Policy-bound sample trajectory
4	Run 8 scenario-required allowlisted read-only skills	Policy-bound sample trajectory
5	Validate source references, evidence classes, reason codes, and legitimate exceptions	Policy-bound sample trajectory
6	Apply the R0-R2 policy, abstain where required, and route the result for human review	Policy-bound sample trajectory

Selected skill trajectory

#	Skill	Engine	Authority	Role
1	`partscleanse.resolve_material_identity.v1`	PartsCleanse AI	R1	Material identity and duplicate-family evidence
2	`inventorymind.analyse_stock_posture.v1`	InventoryMind AI	R1	Stock posture, reservation, quality-state, and availability evidence
3	`procuremind.review_purchase_path.v1`	ProcureMind AI	R1	Purchase history, supplier aliases, emergency flags, and price context
4	`financemind.qualify_exposure.v1`	FinanceMind AI	R2	Assumption-labelled financial exposure for owner review
5	`assetmind.map_asset_context.v1`	AssetMind AI	R1	Asset, equipment, functional-location, and BOM context
6	`reliabilitymind.review_work_context.v1`	ReliabilityMind AI	R1	Work-order, maintenance, and criticality context
7	`readymind.validate_source_fit.v1`	ReadyMind AI	R1	Data sufficiency, use-case fit, and readiness limitations
8	`governancemind.apply_evidence_policy.v1`	GovernanceMind AI	R2	Evidence class, confidence, policy, owner, and review routing

Finding review path

ID	Reason	Evidence	Confidence	Owner
F-001	MATERIAL_IDENTITY_OVERLAP	Derived	Medium	Material master owner
F-002	CROSS_SITE_STOCK_VISIBLE	Observed	High	Inventory owner
F-003	STOCK_NOT_ACCESSIBLE	Observed	High	Maintenance and quality owners
F-004	REPEAT_BUY_SIGNAL	Derived	Medium	Procurement owner
F-005	VALUATION_CONTEXT_MISSING	Hypothesis	Low	Finance owner

This is a deterministic synthetic trace. It is not a production trace, tenant assessment, or customer outcome.

Agent Passport

The sample agent has a named identity, policy, and authority ceiling.

The passport makes the current control envelope visible before any pilot discussion. It is a public sample identity for synthetic evidence review, not a tenant-specific agent registration.

Agent name	MRO False-Stockout Evidence Agent
Agent ID	ai2coe.evidence-agent.synthetic-readonly.v1
Version	1.0.0
Purpose	Synthetic read-only industrial evidence review
Authority ceiling	R2 - retrieve, analyse, and recommend for human review
Approved data scope	Published synthetic fixtures only
Allowed authority	R0, R1, R2
Allowed skills	8 versioned read-only skill contracts
Denied actions	transaction submission, source-system mutation, live OT control, cross-tenant access
Human review	Required before action
External model calls	0
Source-system writes	0
Policy checksum	3532d0ae4e9f358d...

Sample readiness report

A decision pack, not a production approval.

A generated result includes use-case fit, ten readiness signals, autonomy recommendation, decision rights, human approval requirements, agent security risks, an evaluation plan, a first-agent recommendation, and a remediation backlog.

Evidence classEstimated for self-declared diagnostic inputs

Default outcomeFoundation remediation, read-only prototype, controlled pilot, or do not use an agent

Owner decisionProceed, constrain, remediate, investigate, or defer

Agent Risk Register

Threats, controls, and residual gaps remain visible.

Risk	Current control	Remaining requirement
Direct or indirect prompt injection	Separate instructions from data; quarantine prompt-like source content	External models and retrieved content require further red-team testing
Excessive agency or unauthorized tools	Fixed skill allowlist, schema validation, R2 ceiling, deny by default	External tool gateway and credentials are not implemented
Cross-tenant disclosure	No tenant or customer data in the synthetic demo	Tenant isolation must pass before any customer pilot
Unsafe HTML or spreadsheet output	Escaped HTML and neutralized formula prefixes	File export paths require continued regression tests
Missing context or false confidence	Source references, evidence classes, exceptions, limitations, and abstention	Domain-owner validation remains mandatory
Runaway planning, latency, or cost	Deterministic fixed plan and no external model calls	Metering and loop limits are required for a future model-backed pilot

Agent evaluation plan

Evaluate the complete trajectory, not only the final answer.

Dimension	Review evidence	Minimum gate
Use-case classification	Fit result and non-agent alternative	Can recommend analytics, workflow, RPA, copilot, read-only agent, or no agent
Tool trajectory	Selected skill IDs, versions, sequence, and schema status	Every tool is allowlisted and R2 or lower
Evidence quality	Source-reference coverage, reason, confidence, limitation, and exception	Every material conclusion has references
Safety	Injection, formula, authority, output, and tenant tests	No source content changes policy or permissions
Abstention	Insufficient-data and missing-context scenarios	Escalate rather than fabricate a conclusion
Review usefulness	Accept, reject, defer, assign, and request-more-data paths	Named human owner retains the decision
Operations	Repeatability, latency, cost, version, and incident records	Baseline first; no public performance percentage yet

Quantitative targets will be defined only after a reproducible baseline exists. No unvalidated evaluation percentage is published.

18-industry Agentic AI map

One bounded industrial decision pattern per target industry.

Each guide connects an Agentic AI use case to trusted source data, operating context, human owners, and failure scenarios. The purpose is query ownership and readiness education, not claims of deployed industry agents.

Oil & Gas

Turnaround and critical-spares evidence review

Data: material master, site stock, reservations, asset BOMs, work orders, shutdown scope, and criticality.

Owner path: Maintenance, turnaround, procurement, and finance owners. Current support remains diagnostic and read-only.

Mining

Mobile-fleet maintenance and remote-spares review

Data: fleet asset, part identity, remote-site stock, work orders, lead times, and equipment criticality.

Owner path: Mobile maintenance, supply, and site operations owners. Current support remains diagnostic and read-only.

Manufacturing

Downtime root-cause and material-readiness review

Data: line asset, BOM, failure history, work order, material identity, stock, and supplier history.

Owner path: Plant, reliability, maintenance, and materials owners. Current support remains diagnostic and read-only.

Utilities

Outage-readiness and critical-asset evidence review

Data: asset criticality, outage plan, work package, spares, supplier lead time, and quality state.

Owner path: Outage, asset, procurement, and operations owners. Current support remains diagnostic and read-only.

Data Centers

Critical-facility maintenance-readiness review

Data: facility asset, redundancy tier, work order, spares, service state, and criticality.

Owner path: Critical facilities, reliability, security, and finance owners. Current support remains diagnostic and read-only.

Aviation MRO / Airlines

Aircraft-on-ground parts evidence review

Data: part identity, interchangeability, aircraft applicability, stock location, quality status, and work package.

Owner path: AOG, engineering, stores, procurement, and quality owners. Current support remains diagnostic and read-only.

Commercial Fleet, Trucking & Logistics

Vehicle-maintenance and parts-availability review

Data: vehicle, work order, part, depot stock, supplier, route impact, and lead time.

Owner path: Fleet maintenance, depot, procurement, and operations owners. Current support remains diagnostic and read-only.

Ports, Marine Terminals & Shipping

Crane and terminal-equipment readiness review

Data: equipment, BOM, work order, terminal stock, supplier, operating window, and criticality.

Owner path: Terminal engineering, maintenance, procurement, and operations owners. Current support remains diagnostic and read-only.

Pharmaceutical

Gxp maintenance-evidence review

Data: validated asset, work order, material, quality state, change control, and approval records.

Owner path: Engineering, quality, maintenance, and procurement owners. Current support remains diagnostic and read-only.

Healthcare Systems

Biomedical and facilities maintenance triage

Data: equipment criticality, service state, work order, stock, location, and vendor context.

Owner path: Clinical engineering, facilities, procurement, and safety owners. Current support remains diagnostic and read-only.

Rail, Metro & Transit

Rolling-stock maintenance-readiness review

Data: fleet unit, depot, BOM, work order, spares, service plan, and criticality.

Owner path: Fleet engineering, depot maintenance, materials, and operations owners. Current support remains diagnostic and read-only.

Telecom Network Operators

Field-maintenance and spares evidence review

Data: network asset, site, alarm context, field work order, stock, dispatch, and vendor.

Owner path: Network operations, field service, supply, and security owners. Current support remains diagnostic and read-only.

Aerospace & Defense Maintenance Depots

Sustainment and mission-readiness evidence review

Data: configuration, part identity, equipment, work package, stock, quality status, and approvals.

Owner path: Sustainment, engineering, quality, security, and supply owners. Current support remains diagnostic and read-only.

Food & Beverage

Line-reliability and maintenance evidence review

Data: line asset, hygienic design context, work order, part, stock, supplier, and production window.

Owner path: Plant maintenance, quality, production, and procurement owners. Current support remains diagnostic and read-only.

Warehousing, Distribution Centers & 3PL

Material-handling-equipment uptime review

Data: equipment, site, work order, part, stock, vendor, and throughput window.

Owner path: Facilities, operations, maintenance, and procurement owners. Current support remains diagnostic and read-only.

Construction & Heavy Equipment Fleets

Heavy-equipment maintenance and parts review

Data: equipment, jobsite, work order, part identity, mobile stock, supplier, and criticality.

Owner path: Fleet maintenance, project operations, procurement, and finance owners. Current support remains diagnostic and read-only.

Higher Education & Multi-Campus Facilities

Campus-facilities maintenance triage

Data: building asset, work request, contractor, inventory, location, service priority, and schedule.

Owner path: Facilities, procurement, safety, and campus operations owners. Current support remains diagnostic and read-only.

Hospitality, Resorts & Gaming

Critical-facilities maintenance evidence review

Data: facility asset, guest-service impact, work order, stock, vendor, location, and operating window.

Owner path: Engineering, operations, procurement, and risk owners. Current support remains diagnostic and read-only.

AI2COE Industrial Agentic AI Assurance Toolkit

Evaluate the boundary before evaluating the promise.

The public artifacts package the current methodology, sample report, evaluation pack, human review matrix, risk register, and synthetic baseline. They are designed for review conversations and do not claim production deployment.

Published readiness methodology

Agentic Fit Matrix

Sample methodology template; not customer evidence. Read-only R0-R2 evidence support; no operational write-back or customer-result claim