The data is fragmented.
Operational signal sits across historians, SCADA, DCS, CMMS, ERP, engineering systems, documents, events, and local spreadsheets. Agents can’t reason across what they can’t reach.
AGENTIC OPERATIONS · INSIGHT TO EXECUTION
The foundation for governed industrial execution.
Industrial AI is moving from insight to action. XMPro is the operating layer where operational data, Datastreams, Agentic Harness, AI Assistants, AI Advisors, and Cognitive Decision Teams come together. Teams move from monitoring to advice, coordination, and controlled autonomy.
AI agents cannot act on operational knowledge they cannot reach.
Most operational environments were not designed for AI agents. Three problems show up before any agent can act safely.
The meaning is often hidden.
Context lives in site-specific language, expert memory, naming conventions, and operational practice. Agents need that context made explicit before they can act safely.
The accountability model is not ready.
When an agent recommends or performs an action, the business needs to know what it observed, why it acted, what policy governed it, who approved it, and what happened next — captured against every decision.
Many execution gaps do not require full autonomy on day one. They require a governed place for AI reasoning to happen — where context is curated, tools are controlled, outputs are validated, and downstream actions are routed through visible rules.
That is the role of XMPro Agentic Harness →
The industrial operating layer for AI, applications, and agents.
Where others stop at data or analytics, XMPro covers the full path from operational signal to governed action — and grows with where you are on the autonomy curve.
01 Enterprise AI Platforms
02 Industrial Data Platforms
03 XMPro AO Platform
Built around
Enterprise objects, data products, AI-enabled workflows
Contextualised asset data and analytics
Governed industrial action — across the full path from data to outcome
Strong at
Knowledge work, document tasks, business workflows
Asset insight, anomaly detection, condition monitoring
Connect, contextualise, decide, coordinate, execute, learn
Gap in industrial ops
Safety-critical control, real-time OT context
Stops at insight, not governed action
Purpose-built for safety-critical OT and governed action
Where it fits
Adjacent to the operation
Reports on the operation
The operating layer for governed agentic operations
From deterministic orchestration to governed autonomy.
XMPro supports the full operating continuum on a single foundation. Customers start where they are, grow into deeper agentic patterns when the operation is ready — no migration between vendors, tools, or architectures.
DETERMINISTIC AGENT AGENCY → POLICY-CONTROLLED AUTONOMY
L1 DETERMINISTIC
Datastreams
Deterministic orchestration for industrial data, events, rules, and workflows. Existing product, foundational layer.
Explore Datastreams → L2 GOVERNED REASONING
Agentic Harness
The first agentic layer. LLM reasoning inside visible Datastream flows where context assembly, tool access, output validation, routing, error handling, and observability remain governed.
Explore Agentic Harness → L3 CONTEXT & EXPLANATION
AI Assistants
User-facing context access, summarization, explanation, and interaction. Powered by XMPro MAGS.
Explore AI Assistants → L4 RECOMMENDATIONS
AI Advisors
Decision-aware recommendations, diagnostics, trade-offs, and next-best actions. Powered by XMPro MAGS.
Explore AI Advisors → L5 COORDINATED DECISIONS
Cognitive Decision Teams
Specialist agents coordinating complex operational decisions across constraints, systems, teams, and policies.
Explore Cognitive Decision Teams → L6 POLICY-CONTROLLED
Governed Autonomous Operation
Selected decision-action loops executing within policy-controlled boundaries, with evidence captured for every step.
Explore Governed Autonomous Operation →Governed AI reasoning before full cognitive autonomy.
Agentic Harness is for industrial use cases that need LLM reasoning but do not yet need persistent memory, autonomous planning, objective functions, or multi-agent coordination.
It gives customers a lower-friction on-ramp to agentic operations — keeping the industrial runtime, connectors, controls, and observability inside XMPro.
EXAMPLES HARNESS USE CASES
- Summarize work-order history
- Classify an oil sample against failure modes
- Explain why a recommendation fired
- Enrich an alarm with operational context
- Translate operator notes into structured records
- Validate generated outputs before they reach downstream workflows
Agents act on operational knowledge they can actually reach.
Industrial environments hide meaning in site-specific language, expert memory, naming conventions, and operational practice. The platform connects four knowledge layers so agents, applications, and recommendations work from the same operating picture.
01 OPERATIONAL DATA
Live signals from historians, SCADA, DCS, sensors, edge devices, and OT systems.
02 ASSET & PROCESS CONTEXT
Asset hierarchies, process flows, relationships, events, constraints — the operational model.
03 ENTERPRISE CONTEXT
Suppliers, teams, contracts, compliance, and the systems of record around the operation.
04 EXPERT KNOWLEDGE
Procedures, identity mappings, site-specific language, and the institutional memory agents need to act safely.
THE OPERATING LOOP
Detect. Decide. Coordinate. Execute.
Every operational decision flows through the same loop — whether a human acts, an agent recommends, or the platform executes within policy. One loop, many modes.
Most platforms stop at Detect. XMPro completes the loop.
Every recommendation. Every action. A record.
Six fields captured for every decision — across Human-Controlled, Human-Approved, and Policy-Controlled modes.
DECISIONGRAPH RECORD SCHEMA · 6 FIELDS
- 01 Observed
- What the platform saw.
- 02 Context
- What context was used at decision time.
- 03 Action
- What was recommended or executed.
- 04 Policy
- What policy applied.
- 05 Approval
- Who or what approved it.
- 06 Outcome
- What outcome followed.
Progressive intelligence → measurable operational value.
Multi-agent coordination drives real financial and operational impact across four levers — uptime, throughput, quality and cost.
Operations run more often + UPTIME
Predict and avoid failures before they trigger downtime. Coordinate maintenance autonomously across teams and systems.
- Predictive avoidance of failure
- Autonomous maintenance coordination
- Optimised schedules in real time
8-WEEK TREND
More output when running + THROUGHPUT
Lift throughput at the edge of your existing capacity — in real time, against operational constraints.
- Autonomous throughput optimisation
- Real-time constraint removal
- Coordinated multi-line orchestration
8-WEEK TREND
Better quality at full potential + QUALITY
AI-augmented quality guidance with operator oversight — yield without compromising safety or compliance.
- AI-augmented quality guidance
- Human-in-loop oversight + compliance
- Closed-loop process correction
8-WEEK TREND
Lower operating cost − COST
Multi-agent resource efficiency reduces waste, rework and idle utilisation across the operational footprint.
- Multi-agent resource efficiency
- Reduced waste and rework
- Energy and consumable optimisation
8-WEEK TREND
Not sure which lever matters most for your environment?
Talk to an ExpertFour differences that show up in the operation.
Enterprise AI platforms and industrial data platforms can both reason over data. XMPro is built around what must happen next in the operation — and what gets captured for evidence after.
01 DIFFERENTIATOR
Built around governed action, not data products.
The platform is designed for what happens next in the operation — detect, decide, coordinate, execute, learn — rather than starting from objects and reports.
02 DIFFERENTIATOR
Industrial-grade context, not chat over documents.
Live operational signal, asset hierarchies, expert knowledge, and policy boundaries are first-class — not bolt-ons.
03 DIFFERENTIATOR
One operating foundation across the autonomy spectrum.
Customers start with Harness and grow into Assistants, Advisors, and Cognitive Decision Teams on the same runtime, connectors, and controls.
04 DIFFERENTIATOR
Evidence-first by default.
Every recommendation and action gets a DecisionGraph record — queryable, auditable, replayable.
VALIDATED
The category, validated by analysts.
Gartner named XMPro a Technology Innovator in Agentic AI — one of only five companies recognised globally — plus Sample Vendor in two categories of the inaugural Hype Cycle for Agentic AI 2026.
37+
36
10+
+186%
The platform components behind agentic operations.
Data Stream Designer, App Designer, Recommendation Manager, Workflow, Subscription Manager — and Agentic Harness. The components that compose every operational application, agent, and decision flow.
