Main principles

What is a Digital Reasoning Thread?

The Digital Reasoning Thread (DRT) is a persistent, context-aware, auditable representation of the reasoning that drives industrial decisions across systems, agents, and humans. It turns reasoning into an asset that can be traced, audited, and reused — not just “what happened,” but why it happened.

In practice: it's the structured chain of machine reasoning that connects steps end-to-end. The difference between “five AI models that each did something” and “one coherent line of industrial thought.”

The problem

Most industrial systems record what happened — not why. As data and decisions move across PLM, MES, ERP, digital twins, and AI models:

• Assumptions are lost at handovers
• Analysis is repeated across teams
• Audits become slow and incomplete
• Trust in AI outputs stays low

Reasoning today is fragmented: isolated model calls, logic buried in applications, or in people's heads. DRT is the connective layer that carries context and rationale across that chain.

Four pillars of a DRT

• Context propagation. Every step inherits full upstream context — not only data, but rationale, confidence, and rejected alternatives. That travels with the thread.
• Decision lineage. An immutable chain of reasoning provenance for regulation, safety, and root-cause analysis — a record of why each conclusion was drawn.
• Composability. Reasoning is decomposed into modular, chainable units (like microservices for logic). A vibration-analysis thread can feed a remaining-useful-life thread, then procurement — each testable, versioned, and reusable.
• Persistence. The thread lives across time: it can be paused, resumed, branched, and merged. A thread started at inspection can be extended with later data and closed with the repair outcome.

The Kubernetes analogy

Kubernetes didn't replace containers — it made them operable at scale. Similarly, the DRT is the Kubernetes of industrial reasoning: it doesn't replace your models or agents; it makes them viable where “the AI said so” is never enough.

How DRT connects the stack

DRT aims to connect perception, knowledge, reasoning, simulation, execution, and feedback. In that flow:

• Perception — signals from sensors, logs, images, documents
• Knowledge — schemas, ontologies, catalogs
• Reasoning — goals, assumptions, constraints, plans, traces
• Simulation — what-if scenarios, digital twins
• Execution — MES, PLCs, robots, enterprise systems
• Feedback — intent vs outcome, context updates
• Governance — identity, policy, lineage, safety (spanning all layers)

Anatomy of a thread

A DRT is not just a prompt chain or a DAG of API calls. It includes:

• Reasoning nodes — units of inference (LLM, physics model, rules, or human), with inputs, outputs, confidence, and explanation trace
• Thread context — a cumulative object flowing through the thread, carrying rationale and narrowing uncertainty
• Branching and convergence — when diagnostics consider multiple failure modes, the thread branches; when evidence rules some out, it converges
• Temporal anchors — each node tied to the data state at a point in time, so you can replay, do what-if, and audit retroactively
• Governance hooks — who authorized, what policy, safety constraints, human-in-the-loop — built into the structure, not bolted on

What this makes possible

• Explainable autonomy — Full thread from sensor → anomaly → failure mode → risk → recommendation, with every step traceable.
• Reasoning reuse — A thread validated for one asset can be templated and deployed across a fleet; improvements propagate.
• Cross-system reasoning — Decisions that span OT, supply chain, finance, and safety without losing context or lineage.
• Continuous learning — When the outcome is known (repair worked, prediction correct), it can flow back through the thread to calibrate nodes.

Example use cases

• Engineering handover — Constraints from PLM/tools → compliance check, risk simulation, parameter windows → MES work instructions and safe ranges. Outcome: traceable handover, faster ramp-up.
• Predictive maintenance + scheduling — Time-series (e.g. UNS), maintenance logs, shift plan → failure-window prediction, downtime optimization → work order and pick list. Outcome: less unplanned downtime.
• Supply chain exceptions — Supplier ETA change, inventory, priorities → option evaluation, penalty/service-level simulation → replan and customer updates. Outcome: consistent decisions and audit-ready explanations.