Author: Vlad Larichev Version: 0.1 – August 2025 License: CC-BY 4.0
In complex industrial systems, AI is often siloed, inconsistent, or blind to the reasoning behind decisions. The Digital Reasoning Thread introduces a new architecture that connects context, assumptions, logic, and outcomes across tools and processes.
This paper outlines the concept, a reference architecture, and early applications across engineering, manufacturing, and asset operations.
Most industrial systems record what happened – but not why it happened.
The industrial value chain can be viewed as a continuous flow of materials over time, shaped by both manufacturing processes and human decisions. As these materials and decisions move from engineering to production to service, they pass through many organizational and technical layers.
Each layer—PLM, MES, ERP, CMMS, simulation tools, document systems—operates in its own domain, with its own language, owners, syntax, and logic for representing reality.
As a result:
In this work, I introduce the concept of the Digital Reasoning Thread – a persistent, cross-domain logic layer that connects assumptions, constraints, evidence, decisions, and outcomes into a consistent flow. This thread is designed to be understood and utilized by both humans and AI agents, enabling more transparent, explainable, and traceable industrial systems.
The concept of the Digital Thread first gained traction in the aerospace and defense sectors and later spread to automotive, energy, and other complex industries. Its original ambition was to create end-to-end traceability – linking requirements, system models, design artifacts, manufacturing steps, and field service events in a continuous chain. The goal: maintain a unified view of product evolution across its lifecycle.
This was made possible through advances in PLM (Product Lifecycle Management) and MBSE (Model-Based Systems Engineering), which provided structure and governance. Over time, vendors and industry standards helped connect tools like CAD, BOM systems, MES, ERP, and CMMS into a more coherent ecosystem. The digital thread allowed teams to trace configurations, track versions, and maintain the lineage of digital product artifacts. It became a natural foundation for digital twin implementations, giving context and history to what the twin observes in real time.
Today, many industrial firms operate some form of digital thread. These threads support:
But despite these gains, key limitations remain:
With the rise of AI in industrial settings, this gap becomes more visible and more critical.
AI models, agents, and automation systems begin to act – but why they act a certain way is often unclear or hard to audit. This creates friction in adoption, safety concerns, and regulatory challenges.
Initiatives like knowledge graphs and Unified Namespace (UNS) architectures have improved connectivity and semantic layering. But they rarely make reasoning explicit.
The logic behind actions remains implicit – hidden in tool-specific scripts, engineering notebooks, or human memory.
This is the gap the Digital Reasoning Thread aims to close.
Digital Reasoning Thread is a persistent layer that carries context and rationale across data, tools, and decisions. It turns reasoning into a asset that can be traced, audited, and reused across the value chain.
The DRT is a persistent, toolchain-spanning logic layer that stores decisions, justifications, assumptions, and context – allowing agents and humans to operate more consistently across complex toolchains.
During the key facts, aprameters and transactins are stored across the data bases, there is no logic for storing and organiszation of the reasonign processes and needed context engineering.
Goal of DRT is connect perception, knowledge, reasoning, simulation, execution, and feedback.
Perception collects signals from sensors, logs, images, and documents. Knowledge organizes schemas, ontologies, and catalogs. Reasoning captures goals, assumptions, constraints, plans, and traces. Simulation explores what-if scenarios and digital twins. Execution applies plans in MES, PLCs, robots, and enterprise systems. Feedback compares intent and outcome and updates the context. Governance spans all layers with identity, policy, lineage, and safety rails.
🛠 [Diagram placeholder – Coming soon in /assets/architecture.png]
The Digital Reasoning Thread is an open concept, actively evolving through research, experimentation, and industry feedback. We’re in the early stages of shaping its architecture, vocabulary, and applications across domains.
If you’re interested in contributing, validating use cases, or collaborating on implementation patterns:
Your input can help shape a reasoning layer that makes industrial systems more explainable, auditable, and adaptive.
The thread works with existing IT and OT. It integrates with PLM and ALM systems like NX and Polarion, MES and MOM, ERP, CMMS, data lakes, and UNS or MQTT backbones. It aligns to standards such as ISA-95, OPC UA, Asset Administration Shell, and MBSE practices. The goal is not to replace core platforms but to connect their decisions and keep the rationale portable and auditable.
Engineers and operators do not just pass a CAD file, a ticket, or a schedule. They pass the reasoning that led to it. Agents do not act as black boxes. They act under policy with human-reviewable traces. Digital twins are not static models. They reference the assumptions that were valid at the time of a decision and record how those assumptions evolved with new evidence.
In engineering orchestration, requirements, CAD, CAE, and tests stay linked through rationale. A change in a load case or material triggers an explicit chain that connects design, simulation, and verification. In production optimization, a scheduling change in MES is tied to the constraints and trade-offs that justified it, and to a twin-based what-if that explored alternatives. In service and maintenance, a recommendation to replace a component points to the sensor patterns, historical fixes, and risk model that generated it. In agent operations, autonomous steps are logged with goals, context, tools used, and checks performed so supervisors can accept, correct, or roll back.
Shorter decision loops because teams reuse prior reasoning instead of redoing work. Fewer losses at handovers because assumptions and constraints travel with the data. More reliable audits because the logic behind each step is searchable. Clearer guardrails for agentic systems. Numbers will vary by site and process, but a reasonable starting target is 15 to 30 percent faster decision cycles in selected flows, 20 percent fewer handover defects, and material improvements in traceability scores and mean time to confident decision.
Key metrics. Track decision loop cycle time. Track handover loss rate between tools and teams. Track a traceability score for AI or agent produced artifacts. Track defect escape rate. Track mean time to confident decision, plus MTTD and MTTR for anomalies where the thread is in use.
Design notes. Keep the thread storage simple and append only. Separate human readable summaries from machine readable fields. Do not create a tool zoo. Prefer adapters that attach to existing items in PLM, MES, ERP, and CMMS rather than duplicating them. Use your UNS or MQTT backbone to move event references, not full payloads, where possible. Treat prompts, constraints, and checks in agent workflows as configuration that must be versioned and reviewed like code.
Governance and safety. Every reasoning trace should have an owner, a policy class, and a retention rule. Access should follow least privilege. Sensitive content should be redacted at source, not at the viewer. All agent actions should include preconditions, tool calls, outcomes, and verification steps. Avoid hidden prompts. Make assumptions and known limitations explicit.
Risks and how to avoid them. A thread that is too heavy will not be used. Keep it lean and automate capture where possible. A thread that is not linked into daily tools will be ignored. Embed links and viewers in PLM, MES, ERP, and CMMS. Agents without audit will create trust issues. Require traces for any autonomous action that changes state. Data-only pipelines that ignore rationale will keep duplicating past mistakes. Attach reasoning at the point where decisions are made, not as an afterthought.
Where to start. Pick a single flow where poor handovers cost time and money. Write down the decisions and assumptions that matter. Capture them in a small schema. Connect two systems with a minimal adapter. Measure the before and after. Share the results and iterate.
Digital Reasoning Thread is a practical step toward trustworthy AI in industry. It does not ask you to replace your stack. It asks you to carry your reasoning with you. When teams and agents share not only what they do but why they do it, work gets faster, audits get simpler, and outcomes improve in a way that can be measured.
“It’s not just a data thread. It’s a reasoning thread.”
It complements data platforms and knowledge graphs by adding traceable, explainable, dynamic logic.
What to add:
How it enhances the current thread.
Interoperability stance. Work with existing stacks. Reuse standards like ISA-95, OPC UA, AAS, and SysML. Use UNS or MQTT for event references rather than moving heavy payloads. Do not duplicate master data. Link to it.
🛠 [Diagram placeholder – Coming soon in /assets/architecture.png]
Perception Layer: sensors, logs, documents
Knowledge Layer: structured models, ontologies
Reasoning Layer: traceable logic, chains of thought
Execution Layer: automation, robotics, MES, ERP
Feedback Loop: learning from system behavior
Engineering orchestration: maintaining reasoning across CAE, PLM, and simulation
Manufacturing optimization: linking MES decisions with simulation assumptions
AI agents for documentation: enabling traceable, explainable outputs from autonomous agents
Cycle time reduction in decision loops
Number of traceable decisions
Fewer handover losses between departments/tools
Auditability score of AI-generated results