The Platform
How IndustrialClaw works.
The architecture inside the node -- the governance loop, the autonomy framework, the OT connectivity layer, and the knowledge model that turns your plant's operational history into a compounding moat.
New to IndustrialClaw? Start here →What changes in your stack -- and what does not.
IndustrialClaw sits above your existing OT infrastructure. It does not replace it. Each row shows exactly where the boundary is.
| Layer | What it does today | With IndustrialClaw |
|---|---|---|
| PLC / DCS / SCADA | Executes reliably. Supervises the process. Generates alarms. | Unchanged -- IndustrialClaw proposes through supervisory paths, never bypasses control systems. |
| Historian / MES / CMMS | Records what happened. Schedules maintenance. Tracks production. | Source data for agent reasoning. Agents write back to CMMS (work orders), MES (schedule updates). |
| Software Defined Automation (SDA) | IEC 61499 function blocks, OPC-UA, containerised control on standard edge compute. | Full action surface: in SDA environments, agents can propose and execute control logic changes through governed supervisory paths. |
| ChatGPT / Copilot | Responds when queried. Advisory only. No OT awareness. | Replaced for plant use cases -- IndustrialClaw runs persistently, understands OT context, acts within declared boundaries. |
| Open-source agent frameworks | Capable building blocks. No OT governance, no audit trail, unacceptable blast radius. | Replaced -- IndustrialClaw is the enterprise-managed, governed alternative built for safety-critical environments. |
Every agent action follows the same loop.
A deterministic, auditable sequence. Not a black-box inference chain.
Agent reads from connected data sources on trigger or schedule. Historians, SCADA, CMMS, DCS. Builds a structured operational context window.
Agent generates a candidate action: raise a work order, send a briefing, adjust a setpoint, escalate to an operator. Explicit and auditable before execution.
The governance layer evaluates the proposal against deontic rules, permission boundaries, spend caps, and network constraints. Validation runs on projected outcomes -- not reasoning paths.
Only validated actions reach the execution layer. Write capabilities require explicit role-based authorisation. The proposal, validation decision, and execution record are written to the immutable audit trail.
Start where you are. Progress at your pace.
HAS (Human Agency Scale) is the framework for progressive autonomy -- from monitoring to fully autonomous operations, on one platform.
MONITORING
Agents observe and alert. Humans decide and act. Zero agent-initiated actions.
ADVISORY
Agents observe, diagnose, and recommend. Humans approve. Agents explain their reasoning.
ASSISTED ACTION
Agents initiate routine actions (create work orders, send briefings) within approved boundaries. Humans retain override authority.
SUPERVISED AUTONOMY
Agents handle the full exception workflow. Humans monitor and intervene only on exception. Operations run between shifts.
FULL AUTONOMOUS MULTI-AGENT
Agent networks coordinate autonomously across the operation. Humans set objectives and governance parameters. Full audit trail maintained.
Tier 1 oil & gas operator running AI Operated Control Room under safety-critical autonomous operations
Your institutional knowledge becomes the moat.
The Operational Identity Model (OIM) is the knowledge layer that separates IndustrialClaw from generic AI platforms. OIM captures your operational processes, asset relationships, alarm patterns, and institutional decision logic -- and encodes them into agent behaviour that compounds with every deployment.
Generic AI tools are trained on the general internet. IndustrialClaw agents are trained on your plant, your historian, your maintenance history, and your engineering decisions. That knowledge is yours -- not ours. It becomes progressively harder to replicate as the deployment matures.
Processes
Operational procedures, decision sequences, and escalation paths -- encoded as agent behaviour.
Asset relationships
Equipment hierarchies, failure mode dependencies, and maintenance schedules.
Institutional knowledge
The heuristics experienced operators carry. Preserved, not lost at shift handover.
Compounds over time
Every deployment makes the OIM richer. The longer you run, the wider the moat.
Connected to the systems your operation actually runs on.
150+ OT/IT connectors via XMPro DataStreams -- the widest connectivity library in industrial operations.
Connected systems
Why connectivity matters
The intelligence of an agent is bounded by the data it can see and the systems it can act on. IndustrialClaw starts with the widest OT connectivity library available, so agents aren't artificially limited by integration gaps.
Connections are bidirectional: agents read from historians, SCADA, and CMMS -- and write back to work order systems, notification channels, and approved control setpoints within declared permission boundaries.
Skills are infrastructure. Not plugins.
In open-source frameworks, any skill can be added and executed. In IndustrialClaw, skills are governed infrastructure:
- → Pre-vetted for OT safety -- No skill in the library can issue unconstrained writes.
- → Version-pinned -- Operators know exactly which skill version is running at every moment.
- → Hash-verified -- Skills cannot be modified post-deployment without breaking the hash.
- → Role-gated -- Skills require explicit authorisation for the asset class they operate on.
Skill manifest -- example
SKILL: alarm-triage-v2.3.1
STATUS: APPROVED
HASH: a8f2c91d...
PERMISSIONS: READ historian, READ alarms
WRITE: DISABLED
LAST_AUDITED: 2026-01-15
Powered by XMPro
Production-proven at scale.
IndustrialClaw nodes are powered by XMPro MAGS - the orchestration platform running governed autonomous agents at Tier 1 operators across mining, oil & gas, and energy. Each node you deploy at the plant is orchestrated by the same platform backing an AI Operated Control Room at a safety-critical petrochemical operation and enterprise-wide process control monitoring across thousands of control loops.
AI Operated Control Room
Tier 1 oil & gas operator -- safety-critical autonomous operations
15+ days continuous
Autonomous operation in a safety-critical petrochemical process
Thousands of loops
Control loops monitored across a Tier 1 mining producer's full operation
The governance layer doesn't just prevent bad outcomes.
It actively generates measurable commercial value. Every agent transaction is an opportunity to optimise within governed boundaries -- automatically, at scale, with full audit trail.
Governed Spend
Hard spend caps and circuit-breakers govern every agent action. CFOs get complete visibility into AI operational costs -- per agent, per shift, per asset class.
Staged Investment Model
Start at HAS 1 with a single use case. Each stage generates its own auditable proof before the next investment decision. The governance architecture is in place from day one -- autonomy expands as operational trust is earned.
Compounding Knowledge Moat
Every deployment enriches the Operational Identity Model with your plant's specific processes, failure patterns, and decision logic. That knowledge is yours -- and progressively harder to replicate as the deployment matures.
See the platform in your operational context.
We'll map IndustrialClaw to your current stack, your use cases, and your governance requirements.
Talk to us