Trusted context
A shared, governed operating context every decision can rely on — not signal scattered across systems.
THE AGENTIC OPERATIONS PLATFORM
From industrial data to governed agentic operations.
The XMPro Agentic Operations Platform turns operational signals into trusted context, governed decision loops, bounded actions, and Decision Trace. It gives industrial enterprises the decision architecture needed to move from visibility to advice, coordination, and governed autonomy.
The model may infer. The operation still needs decision architecture.
When AI moves closer to operational decisions, it exposes gaps that were previously managed by experience, workarounds, meetings, and manual judgement. Industrial operations need:
Clear ownership
Explicit ownership of the decision, the action taken, and the outcome that follows.
Governed workflows
Reasoning and action that happen inside visible, controlled workflows — not a black box.
Review paths
Human review wherever the operating risk requires a person in the loop.
Approval boundaries
Explicit limits on what can act, and when, before anything reaches production.
Evidence
A reviewable record of what was observed, why it acted, and what happened next.
THE BOTTLENECK IS DECISION FLOW
Make the path from signal to action visible, governed, and repeatable.
The Agentic Operations Platform connects the parts of the operating decision that are usually fragmented: live data, asset and process context, applications, advisors, approvals, XMPro FRS front-running simulations, execution pathways, and evidence.
The complete journey from operational visibility to governed autonomy.
These aren’t separate point tools. They’re the operating layers of one production decision architecture, designed so authority can increase only where context, workflow governance, decision governance, risk controls, and evidence support it.
- 01
Operational Systems
Historians, SCADA, DCS, sensors, edge devices, alarms, work systems, and engineering systems — the operation as it runs today.
- 02
Data Stream Designer
Observe, normalise, enrich, and prepare operational signals into governed, real-time flows.
- 03
Operational Context Engine
Turn operational data into trusted semantic context and an open, governed ontology every decision can rely on.
- 04
AI Workflow Harness
Govern model reasoning inside visible workflows: context assembly, tool access, validation, routing, and observability.
- 05
XMPro MAGS
Power AI Assistants, AI Advisors, and Cognitive Decision Teams through governed Cognitive Decision Loops.
- 06
AppDesigner · XMPro FRS · Action Agents
Deliver role-based applications, front-run scenarios against validated domain models, and route approvals, bounded action, and escalation.
- 07
Decision Trace
Capture an auditable record of every recommendation and action — queryable, reviewable, replayable.
Trusted context comes before governed action.
Data Stream Designer observes, normalises, and enriches operational signals. The Operational Context Engine turns them into trusted semantic context and a governed ontology. MAGS reasons inside that context. Without trusted context, autonomy becomes opinionated automation.
Data Stream Designer
Observe, normalise, enrich, and prepare operational signals into governed, real-time flows.
Operational Context Engine
Turn those signals into trusted semantic context and a governed ontology every decision can rely on.
XMPro MAGS
Reason inside that trusted context through governed Cognitive Decision Loops, not raw ungoverned data.
OPERATIONAL DATA
Live signals from historians, SCADA, DCS, sensors, edge devices, and OT systems.
ASSET & PROCESS CONTEXT
Asset hierarchies, process flows, relationships, events, constraints — the operational model.
ENTERPRISE CONTEXT
Suppliers, teams, contracts, compliance, and the systems of record around the operation.
EXPERT KNOWLEDGE
Procedures, identity mappings, site-specific language, and the institutional memory agents need to act safely.
Assistants, Advisors, and Cognitive Decision Teams reason inside visible workflows.
MAGS uses trusted context to govern model reasoning inside visible workflows — individual Assistants, advisory patterns, and coordinated Cognitive Decision Teams across operations, reliability, planning, safety, and production objectives. The goal is not model novelty. The goal is better decisions under operational constraints, on one foundation of increasing authority.
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
AI Workflow Harness
The governed workflow layer for AI reasoning. Model use inside visible Datastream flows where context assembly, tool access, output validation, routing, error handling, and observability remain governed.
Explore AI Workflow 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 →Autonomy must earn authority.
Industrial operations cannot accept free-form autonomy. Authority is constrained by operating boundaries and bonded to evidence, review, front-running simulation, approval discipline, and escalation. XMPro helps autonomy earn more authority over time, one governed decision at a time.
BONDED TO
- Evidence
- Review
- Simulation
- Approval
- Escalation
DECISION TRACE 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.
Start with a priority decision path.
The best starting point is a decision path where better context, faster coordination, governed authority, and evidence create operating advantage.
START WITH
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 ExpertThe 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
Trusted context, governed decisions, bounded action, and evidence
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
01 DIFFERENTIATOR
Built around governed action, not data products.
The platform is designed for what happens next in the operation: signal, context, decision, bounded action, and evidence. It does not start 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 Decision Trace record: queryable, auditable, replayable.
ANALYST RECOGNITION
Named in the analyst reports shaping the agentic AI category.
Gartner named XMPro a Technology Innovator in Agentic AI (one of only five companies named globally), plus Sample Vendor in two categories of the inaugural Hype Cycle for Agentic AI 2026.
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