Trusted context
A shared, governed operating context every decision can rely on — not signal scattered across systems.
AGENTIC OPERATIONS PLATFORM
From Industrial Signal to Governed Agentic Operations.
XMPro is the Agentic Operations Platform. Operational signals become trusted context, governed decisions, bounded actions, and Decision Trace. One production architecture from monitoring to governed autonomy.
How a signal becomes a governed action.
DataStreams orchestrates the operational flow. OCE enriches every decision with context, identity, trust, governance, and evidence. AppDesigner, MAGS, and Front-Running Simulations turn it into safe, traceable action, without replacing your systems of record.
XMPro AO Platform
DataStreams orchestrates the operational flow.
OCE enriches the flow with context, identity, trust, governance, and evidence.
Governance controls when recommendations become actions.
Systems of record remain authoritative. XMPro connects and empowers.
Operational & Enterprise Sources
SCADA / PLC / DCS
Historians
MES / EAM / ERP
Work orders
Documents / procedures
UNS / MQTT / Kafka namespaces
AnyLog (where deployed)
Enterprise data platforms
Your existing systems
edge data provider / reads, queries, subscribes
XMPro DataStreams
Data acquisition, normalization, event processing, orchestration — the ingestion and normalization fan-in layer.
Listener agents
Context providers
Action agents
Analysis agents
Recommendation components
Connectors and adapters
Event processing logic
Orchestrates the operational flow
Operational Use-Case Pipelines
Configured for each AO solution
Monitor
Detect
Analyze
Recommend
Coordinate
Simulate
Act
context · identity · trust · governance · evidence
Operational Context Engine (OCE)
Recommended enrichment layer for governed human and agent decisions. Critical for MAGS agents and higher autonomy, because agents need explicit context humans often carry tacitly.
Context
Identity
Trust
Governance
Evidence
AppDesigner / Human Applications
Dashboards, workflows, decision review.
MAGS Agents
Reasoning, recommendations, bounded action.
Front-Running Simulations (FRS)
Tests candidate actions before execution — with live data and context.
Governed Outputs (delivered to humans and systems)
Human decision support
Agent recommendations
Approved action packages
Work orders / workflow actions
Evidence and audit trail
Simulation-backed recommendations
Primary operational flow
Context enrichment flow (OCE)
Simulation flow (FRS)
OCE is a context and governance service. It does not replace systems of record or operational applications.
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.
Monitor, advise, coordinate, and operate with increasing authority.
XMPro supports the full journey from operating visibility to bounded autonomy. The platform doesn’t force every use case into full autonomy. It lets teams sequence authority by value, readiness, risk, and evidence.
OBSERVE
Monitor & Predict
Connect operational data, detect risk, and understand what is likely to happen next. Real-time data, anomaly detection, and leading indicators of failure across your existing operational systems.
AgentHuman
Agent Role
Detect & Surface
Human Role
Decide & Act
Target Outcome
Operational visibility
Example Capabilities
Real-time operational data
Anomaly detection in context
Leading indicators of risk
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
The operational model: asset hierarchies, process flows, relationships, events, and constraints.
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.
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.
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
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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PRODUCTION-PROVEN
Real Results. Real Operations.
XMPro runs in production across asset-intensive and mission-critical operations, helping industrial enterprises compose intelligent operations that solve real engineering and operational problems at scale.
