The Agentic Supply Chain Operating System

TraceLink AI is purpose built for supply chain context and work

The Agentic Supply Chain Operating System is purpose built for the way supply chain work actually happens. Four elements work together: a Supply Network Digital Twin, Multienterprise Business Processes, Supply Chain Intelligence, and Governed Agents.

The Network Supply Network Digital Twin A live representation of the partners, products, locations, and business transactions across the supply network, built on the Integrate-Once™ Agentic Business Network.

The Work Multienterprise Business Processes Orders, shipments, inventory, quality, and compliance work that forms, adapts, and completes across enterprise boundaries rather than inside one system.

The Reasoning Supply Chain Intelligence Agentic Control Towers apply knowledge, analytics, and supply chain meta-reasoning to operational context so people understand what is happening and what should happen next.

The Workforce Governed Agents OPUS Agents are created as governed users, assigned to human managers, and controlled through OPUS Agent Profiles, roles, permissions, objectives, Tasks, Decisions, and Rules.

The digital foundation for governed multienterprise AI

Powered by the OPUS Platform, the Agentic Supply Chain Operating System continuously unifies the information required to perform operational work across multiple enterprises. It is built from the bottom up, starting with the companies that make up your supply network.

Agentic Business Processes

Humans

Supply Chain Planner

Procurement Manager

Customer Service Lead

People set the objectives, apply judgment, and stay accountable for the decisions agents bring them.

Business Processes

Demand & Business Planning

Source & Supply Management

Production & Inventory Management

Planning, sourcing, production, and service processes run continuously rather than in periodic cycles.

Agents

Demand Planning Agent

Supplier Management Agent

Logistics Orchestration Agent

Each OPUS Agent is created as a governed user, assigned to a manager, and measured against business objectives.

Agentic Control Tower

Agentic Reasoning

OPUS Agent Profiles (IOTDR)

OPUS Meta-Reasoning

Orchestration of LLMs / SLMs

Agent profiles, meta-reasoning, and model orchestration interpret what is happening against your objectives, policies, and rules.

Agentic Intelligence

Agentic Experiences

Supply Network Analytics

Reports and Dashboards

Network-wide analytics, dashboards, and agentic experiences make operational conditions legible to the people accountable for them.

Agentic Execution

Object Events and Actions

Roles and Permissions

Business Outcomes

Events, actions, roles, and permissions determine what agents may do, and every action is tied to a business outcome.

Integrate-Once™ Agentic Business Network

Multienterprise Solutions

Business Transactions

Collaborative Processes

Track-and-Trace Compliance

Business transactions, collaborative processes, and track-and-trace compliance run as shared solutions between partners, so both sides of every exchange work from the same record.

Supply Network Digital Twin

Supply Network Administration

OPUS Lakehouse

Integrate-Once™ Integration Profiles

Partner links and system data become one live model of how your network actually operates.

Integrate-Once™ Network Platform

OPUS Metadata Model

Solution Development Environment

Security, Privacy & Regulatory Compliance

One link is enough for every partner and every solution above it, with security, privacy, and compliance handled at the platform level.

End-To-End Supply Chain

Every segment of the chain links to the network once and interoperates with all of the others, whatever systems it runs internally.

Brand Owners Own the product and its market commitments, and orchestrate demand, supply, and compliance across every partner.

Contract Manufacturers Produce and package to plan, and share production status, capacity, and quality data back.

Suppliers Commit and confirm materials and components against forecast, so constraints surface before they become shortages.

Logistics Providers Move goods between nodes and report shipment, condition, and exception status as it happens.

Distributors and Wholesalers Hold and allocate inventory, and exchange orders, shipment notices, and receipts with both sides.

Retailers and Dispensers Fulfill demand at the last mile and return consumption and service data to the network.

Systems of Record

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See the Human-Agent Workforce

Ask AI about the foundation

Agentic Business Processes unlock the human-agent workforce

Agentic Business Processes define how human employees and governed OPUS Agents perform work together. OPUS Agents are no-code, enterprise vertical AI agents created as governed users, assigned to human managers, and controlled through roles, permissions, objectives, Tasks, Decisions, Rules, and execution controls.

This is the work model. Operational work moves from episodic, manually coordinated activity to continuously managed work performed across the multienterprise supply network.

Humans Judgment, governance, and accountability People create governed OPUS Agents, set SMART objectives, define operating policies, and continuously refine intent, Tasks, Decisions, and Rules as business conditions evolve.

Governed OPUS Agents Speed, consistency, precision, and scale Governed through OPUS Agent Profiles created with the IOTDR framework, OPUS Agents execute Tasks and Decisions across Agentic Business Processes using real-time business transactions and process context.

Measurable Outcomes Work validated by business value Operational inputs such as purchase orders, invoices, ASNs, inventory events, and shipment updates produce OPUS Agent Outcomes that validate work completed, decision quality, and business value created.

Explore OPUS Agents

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Agentic Control Towers put knowledge and reasoning where humans and agents work

Agentic Control Towers provide the knowledge, intelligence, reasoning, and operational context required for humans and OPUS Agents to monitor, manage, decide, and perform work across Agentic Business Processes.

They bring together business transactions, partner activity, product movement, process status, inventory signals, compliance events, quality exceptions, operational risks, analytics, and institutional knowledge into a shared intelligence environment. Every business transaction becomes the catalyst for intelligent work.

OPUS Reports and Dashboards Reporting built on trusted operational context rather than exported spreadsheets, so the numbers humans and OPUS Agents work from are the same numbers.

Supply Network Analytics Analytics that span partners and processes, making performance, exceptions, and operational risk visible across the multienterprise network.

From insight to action Reports and dashboards are the starting point for work. Humans and OPUS Agents act on what they surface inside the same governed environment.

Business transaction event Component delay confirmed by a contract manufacturer A 12-day raw material delay is reported against three finished-goods lines.

Agentic reasoning Open orders, safety-stock policy, product genealogy, and regional demand are linked into shared operational context across the network. Governed by OPUS Agent Profile

OPUS Agent recommends Reallocate available inventory to two markets and re-sequence the next production campaign.

Human reviews & approves The supply planner approves. The OPUS Agent executes the approved Tasks and Decisions, and partner orders, forecasts, and commitments update across the network.

See How Reasoning Works

Ask AI About Operational Intelligence

OPUS Meta-Reasoning applies deep supply chain knowledge

The OPUS Brain, OPUS Cognitive Functions, and OPUS Meta-Reasoning combine trusted operational context with company-defined objectives, policies, rules, and decision boundaries. Together they interpret changing conditions, evaluate alternatives, and orchestrate the appropriate combination of large and small language models to produce a governed recommendation.

Step 1 Operational Context Business transactions, process status, partner activity, supply network knowledge, and prior outcomes establish what is happening.

Step 2 Company-Defined Intent OPUS Agent Profiles, created using the IOTDR framework, provide the objectives, decisions, rules, and guardrails relevant to the work.

Step 3 OPUS Meta-Reasoning OPUS evaluates the situation, determines the reasoning required, and identifies the appropriate next action.

Step 4 Orchestration of LLMs and SLMs The OPUS Platform applies the models best suited to the specific reasoning task rather than relying on one general-purpose model.

Governed Recommendation The result is presented with the relevant context for human review or governed agentic work.

One multienterprise foundation: link once, interoperate with everyone

The governed, multienterprise foundation beneath every Agentic Control Tower and Agentic Business Process.

Link once as a uniquely identified network node.
Exchange information with every partner, with no point-to-point integrations.
Create the trusted operational context that fuels agentic work.

How the Model Improves

Ask AI About the Network

Direct Material Suppliers Manufacturers Contract Manufacturers Logistics and Transportation Providers Distributors and Wholesalers Customers

Every business transaction makes the operating model more capable

Traditional operating models improve through periodic transformation initiatives. The Agentic Supply Chain Operating Model embeds continuous improvement directly into operational execution. Every business transaction, decision, recommendation, approval, exception, and outcome contributes to an expanding body of operational memory used to improve the intelligence of the work performed.

Agentic Memory Understand why, not just what Operational memory enables organizations to understand not only what happened, but why it happened and how similar situations should be addressed in the future.

Reinforcement Learning Patterns become predictions As operational knowledge accumulates, reinforcement learning identifies patterns, predicts changing conditions, and generates insights that improve the reasoning performed by both people and governed OPUS Agents.

Human Insight The model refines itself Business leaders, assisted by governed OPUS Agents, evaluate predictions and continuously refine objectives, decision rules, governance policies, and standard operating procedures.

See the Business Value

Redesigning work changes the economics of supply chain operations

Organizations that redesign their operating model establish structural advantages that become increasingly difficult to replicate. Early adopters are expected to:

50–100% Increase in operational productivity

20–40% Scale in operational capacity without proportional workforce growth

30–50% Improvement in customer responsiveness

15–25% Acceleration of revenue opportunities

50–80% Reduction in the operational impact of disruption

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