SYSTEM: OPERATIONAL
·
SYS:INDUSTRIALCLAW.AISTATUS:NOMINALOT/IT CONNECTORS: 150+AUTONOMOUS OPERATION: 15+ DAYSINDUSTRIES: MINING · OIL & GAS · ENERGYGOVERNED AUTONOMY: ENFORCEDAUDIT TRAIL: IMMUTABLEBLAST RADIUS: ZERO
Why IndustrialClaw

The governed intelligence node.
At the OT boundary.
Where AI meets the physical process.

IndustrialClaw is not a general AI platform configured for industrial use. It is the node you install at the plant - pre-wired for OT systems, hardened by default, and designed to act safely in the one domain where acting unsafely has consequences measured in lives, asset integrity, and regulatory liability.

Architecture requirements

What governed industrial autonomy requires

Deploying agents in safety-critical industrial environments sets requirements that consumer and enterprise AI architectures are not designed to meet.

Requirement Generic / Open-Source Agents Enterprise AI Agents IndustrialClaw
Objective functions None None Explicitly encoded -- what the agent team is optimising for
Utility functions None None Principled multi-objective trade-off -- auditable real-time decisions
Agent coordination None None Governed team architecture -- ownership, negotiation, escalation
Skill library General-purpose, unvetted General-purpose, enterprise-hardened OT-specific, vetted, version-pinned
Domain knowledge Trained on general web Enterprise documents, IT workflows Historian protocols, OT alarm schemas, industrial ontologies
Permissions Default-allow IT workspace: files, APIs, network routes Default-deny: read-only until explicitly authorised
Audit trail None Application log Immutable log for every action and decision
Security model Consumer-grade Enterprise IT (sandbox, egress approval) OT/IT architecture with network egress filtering
Deployment User manages Enterprise-managed, IT governance Enterprise-managed, OT-governed
Blast radius API credits Digital workspace only Designed to be zero

MAGS is the brain. IndustrialClaw is the arms and legs.

IndustrialClaw is the OT deployment module for XMPro MAGS -- the mandatory gateway between the cognitive layer and the plant floor. Enterprise agent frameworks have no governed path to OT systems. Any action that needs to reach the plant is routed through IndustrialClaw.

What is IndustrialClaw?
The knowledge gap

Industrial autonomy requires three types of knowledge. Most AI systems have one.

Foundation model labs are solving physical knowledge -- physics, world dynamics, process prediction. Industrial operations require two more knowledge types that ML research is not systematically building.

PHYSICAL KNOWLEDGE BEING BUILT

World and process dynamics

What sensors read. How materials behave. Equipment failure modes. Thermodynamics. This is what foundation model labs are building -- V-JEPA 2, World Labs, DeepMind. It is necessary. It is not sufficient.

OPERATIONAL KNOWLEDGE GAP

KPI and production dynamics

Production schedules, quality targets, maintenance windows, energy budgets, throughput constraints. This is operational policy -- it changes when the product, market, or contract changes. No ML lab is systematically building this.

ORGANISATIONAL KNOWLEDGE CRITICAL GAP

Socio-technical dynamics

Escalation protocols, approval workflows, regulatory constraints, the heuristics experienced operators carry. When that operator retires, this knowledge leaves with them unless it is encoded as institutional memory.

IndustrialClaw encodes all three knowledge types into each node. The Operational Identity Model connects physical sensor intelligence to operational production constraints and the organisational context experienced operators carry. Objective functions, utility trade-offs, and agent team coordination are encoded at deployment - not inferred from general training data. Every recommendation is validated against formal constraints before it reaches a control system. Propose - Validate - Execute.

Security built at the architecture level

Prompt Injection

MITIGATED

OT-specific input sanitisation prevents alarm text, historian tags, or inbound messages from being interpreted as agent instructions.

Skill Governance

VERIFIED

Vetted skill library with version pinning and hash verification. Skills treated as privileged infrastructure, not npm packages.

Blast Radius Control

ZERO

Agents operate with minimum-necessary permissions. Read-only by default. Write and action capabilities require explicit role-based authorisation per skill.

Spend Caps

ENFORCED

Hard API budget limits and circuit-breakers prevent runaway agents. Operators set cost ceilings per agent per shift.

Network Isolation

ACTIVE

Agent communications bound to allowlisted OT/IT domains. No open internet egress from the agent layer.

Audit Trail

IMMUTABLE

Every agent action, every tool call, every decision logged to an immutable audit trail for regulatory and incident review.

How we're different

Positioned against the alternatives

vs. NVIDIA NemoClaw

NemoClaw can be part of the same stack. MAGS can activate NemoClaw for enterprise-side tasks - document retrieval, IT system queries, knowledge worker workflows. But NemoClaw has no path to OT systems. Any action that needs to reach the plant floor is routed through IndustrialClaw. That is not a limitation of NemoClaw. It is the architectural guarantee that keeps the plant safe.

vs. Microsoft Copilot

Copilot gives your operators a faster way to write shift reports. IndustrialClaw makes the shift report unnecessary.

vs. Analytics platforms

Analytics tells you what happened. IndustrialClaw coordinates what happens next. Different layers: one is a reporting tool, one is an operations system.

vs. Generic agent frameworks

Those are developer tools. We're an enterprise platform. They give you building blocks. We give you governed autonomous operations out of the box.

vs. Build it yourself

You could. It took 15 years and production deployments at the world's leading mining and energy operators to get the governance layer right. How long do you have?

Ready to deploy agents with a blast radius of zero?

Talk to us

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