Mining engineers transforming an approved autonomous operating model into production-ready engineering packages covering AI agents, data, integrations, authority, cybersecurity, testing, and commissioning.
心を動かす会話

Engineering the Agentic IROC

Turning an approved autonomous operating model into buildable, testable, cybersecure engineering packages for agents, data, integrations, authority, and commissioning.
会議のスケジューリング
A concept cannot be commissioned.
WHY THIS "AGENTIC IROC ENGINEERING" CHALLENGE?
Agentic programs can stall between feasibility and implementation when decisions, interfaces, authority limits, fallback logic, testing, and acceptance criteria remain implicit. Detailed engineering makes those elements explicit before build begins.
主なメリット
Autonomy becomes real when every boundary is engineered

Detailed design converts operating ambition into specifications that vendors, integrators, OT teams, and operations can build, test, and govern.

チェックアイコン
Make the system procureable

Produce packages that teams can scope, price, and deliver.

チェックアイコン
Remove ambiguity from data

Define source, quality, latency, semantics, ownership, and failure behavior.

チェックアイコン
Engineer authority decision by decision

Specify confidence, vetoes, escalation, and fallback.

チェックアイコン
Test the operating model before release

Group deployment units around critical decisions and domains.

NTTデータのサポート体制は？

01
Specify the decision itself

Define objective, trigger, owner, inputs, alternatives, constraints, authority, fallback, and acceptance criteria.

Mining operations specialist defining a critical operational decision with clear objectives, triggers, ownership, inputs, alternatives, constraints, authority limits, fallback actions, and acceptance criteria in an active mine environment.
02
Engineer the data and integration layer

Design event streams, semantic models, causal context, contracts, APIs, OT gateways, lineage, latency, and resilience.

Mining engineering specialists collaborating in the field to define how operational data from mine equipment, processing systems, sensors, and enterprise platforms can be integrated into a reliable foundation for autonomous decision-making.
03
Define how agents work together

Specify responsibilities, reasoning patterns, tools, message contracts, traceability, conflict resolution, and orchestrator behavior.

Mining engineering specialists collaborating around laptops and operational plans to define how specialized AI agents coordinate decisions across mine operations, processing, maintenance, energy, supply chain, and sustainability.
04
Engineer for industrial risk

Combine cybersecurity, AI functional safety, simulation, adversarial scenarios, human factors, and staged authority release.

Top-down view of mining engineering and operations specialists collaboratively assessing industrial risks around a mine plan, considering cybersecurity, AI safety, simulation, adversarial scenarios, human factors, and controlled autonomy.
05
Make delivery executable

We don’t leave teams with an architecture diagram. We produce work packages, interface documents, test plans, commissioning sequences, cost baselines, and schedule baselines.

Mining engineering team working together in the field to translate an autonomous operating design into detailed plans, documentation, testing activities, commissioning steps, cost estimates, and delivery schedules.
Mining engineering and operations professionals reviewing implementation plans and operational evidence together at an active mine site, representing an autonomous operating model that has been fully specified, bounded, tested, and prepared for delivery.
実証済みの効果
Specified. Bounded. Testable. Ready to build.
重要な結果
Engineering turns an autonomous operating concept into a release-ready delivery system.
チェックアイコン
Connected requirements to evidence

Decisions trace to interfaces, authority, and acceptance tests.

チェックアイコン
Bounded agent authority

Explicit limits govern confidence, vetoes, escalation, and fallback.

チェックアイコン
Made interfaces testable

Semantics, latency, lineage, and failure behavior are defined.

チェックアイコン
Prepared commissioning for release

Build, test, cutover, rollback, training, and handover become executable packages.

Move beyond architecture into something teams can build, test, and commission.

Turn autonomous operating intent into production-ready engineering.

話しましょう
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天然資源
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天然資源
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天然資源
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Turning drone and thermal imagery into early warning for irrigation failures, water accumulation, runoff, and ground instability.
Mining operations team deploying Physical AI through controlled implementation waves, progressing from supervised use cases to domain-level autonomy and cross-domain orchestration while monitoring safety, performance, and operational continuity.
天然資源
自律運転
Implementing the Agentic IROC
Deploying Physical AI in controlled waves so the operation builds trust, demonstrates value, and advances toward cross-domain orchestration without compromising safety or continuity.
Integrated mining operations center using Physical AI to connect mine, plant, maintenance, logistics, energy, water, and safety operations while specialized AI agents coordinate recommendations under human supervision.
天然資源
自律運転
From Recommenders to an Agentic IROC
Transforming the integrated operations center into a Physical AI system that understands the end-to-end operation, coordinates specialized agents, and turns insight into governed action.
Mining safety professionals reviewing camera evidence while an operational vehicle and ore train interact at an active mine rail crossing monitored by computer vision.
天然資源
Rail Crossing Intelligence: Before the Train Arrives
Using computer vision to detect unsafe vehicle behavior and expose time-critical interactions between road and rail.
Motion analytics monitoring a suspended industrial load during a lifting operation at a mining site, tracking speed, oscillation, amplitude, and trajectory while an operator supervises the operation.
天然資源
Predictive Lifting Safety
Using motion analytics to detect abnormal speed and oscillation in suspended loads before lifting risk escalates.
Automated visual inspection system analysing copper cathodes on a production line while an industrial operator monitors quality data in a modern mining processing facility.
天然資源
AI-Assisted Copper Cathode Inspection
Embedding real-time visual quality control into cathode production to detect defects, reduce errors, and strengthen traceability.
Computer vision analyzing ore fragments in real time as they move along a mining conveyor belt, while engineers review particle-size distribution and identify oversized material before it reaches downstream grinding equipment.
天然資源
Ore Size Intelligence in Motion
Automating real-time granulometry on conveyor belts to reduce jams and stabilize material entering the grinding process.
Mining operations team using real-time computer vision to monitor dust and haul-road conditions across multiple camera feeds and coordinate targeted water-truck deployment.
天然資源
Vision-Optimized Haul Roads
Turning mine cameras into real-time intelligence for dust, surface condition, and more targeted water-truck deployment.
Drone and thermal imaging monitoring a mining leach pad from above, detecting abnormal water accumulation, irrigation issues, runoff, and potential ground instability while an operator reviews visual evidence.
天然資源
Leach Pad Integrity, Seen from Above
Turning drone and thermal imagery into early warning for irrigation failures, water accumulation, runoff, and ground instability.
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