TraceLink introduced Agentic Supply Chain Control Tower, an operational intelligence platform that combines analytics, reasoning, active monitoring, and observability to power the Agentic Supply Chain Operating Model.
The Agentic Supply Chain Control Tower transforms business activity into the trusted operational understanding needed for faster decisions, coordinated execution, and continuous adaptation across the enterprise and partner network.
Built on the Agentic Supply Chain Operating System and powered by a large Agentic Business Network—linking more than 315,000 authenticated entities and supporting hundreds of billions of annual supply chain exchanges—the Agentic Supply Chain Control Tower enables organizations to coordinate work across people, enterprise systems, trading partners, and governed OPUS Agents with greater speed, resilience, and confidence.
"The role of the supply chain control tower is fundamentally changing," said Shabbir Dahod, President and CEO of TraceLink. "Traditional control towers helped organizations monitor operations. As AI becomes an active participant in supply chain work, organizations need operational intelligence that provides trusted business context, enables reasoning, and coordinates work across people, enterprise systems, trading partners, and governed OPUS Agents. The Agentic Supply Chain Operating Model requires an Agentic Control Tower to power the transformation."
The Agentic Supply Chain Control Tower brings together foundational OPUS Platform capabilities that transform operational data into trusted business understanding, enabling people and governed OPUS Agents to collaborate on intelligent decisions across the end-to-end supply network.
- Scalable Analytics: Built on a redesigned analytics architecture that supports enterprise-scale workloads, OPUS Reports and Dashboards (ORD) moves analytics beyond the scalability and performance limits of traditional reporting environments, enabling organizations to analyze dramatically larger volumes of operational data. Recent enhancements deliver at least 30% faster reporting while extending analytics from thousands of rows to datasets containing millions of rows. Organizations can transform growing volumes of operational data into trusted business understanding that accelerates decision-making, strengthens operational resilience, and improves execution across the end-to-end supply network.
- Agentic Reasoning: OPUS Brain—TraceLink's agentic reasoning engine—uses reasoning artifacts, semantic search, short-term memory, and object metadata to guide OPUS Agents to perform work consistently without hallucinations. Rather than simply presenting information, it enables organizations to make more deterministic decisions, reduce dependence on manual interpretation, and confidently scale AI-assisted work under appropriate governance and human oversight.
- Event-Driven Intelligence: Object Events and Object Action Scripts continuously observe conditions and initiate governed responses as transactions and workflows progress. By sensing meaningful supply chain events as they occur, organizations can reduce response times, improve exception management, and shift from reactive issue management to proactive coordination.
- Semantic Business Context: Semantic models, canonical objects, and enhanced reference data capabilities ensure information is interpreted consistently across systems, trading partners, and processes. This shared understanding creates a common language that increases confidence in automation, AI-driven recommendations, and standardized execution.
- Continuous Observability: OPUS Metrics and Lakehouse capabilities measure performance, system activity, transaction processing, agent utilization, and outcomes to create a continuous feedback loop for improvement. With greater insight into both execution and agent performance, organizations can identify bottlenecks, optimize processes, and continuously improve supply chain performance over time.
The Latest
Performance bottlenecks aren't uncommon when it comes to rolling out new technology, regardless of how capable or game-changing that technology might be. Every generation of new tech has encountered roadblocks that had to be overcome before it was truly able to shine. Virtualization forced organizations to rethink resource allocation, cloud transformation had us shift our focus toward scalability and elasticity, and microservices introduced entirely new challenges around observability and distributed systems. There's something different about AI, however ...
Consider a single order represented across order-management, execution, and settlement systems. Each database, message broker, and application may be online and processing its own records correctly. Yet the workflow has failed if related events arrive on different clocks, rely on inconsistent state, or cannot be reconciled before an operational decision must be made ...
AI now exists in almost every IT workflow. In a recent survey of more than 800 IT service professionals, all respondents indicated the use of AI in some form within their organization. But there's a growing paradox: if dashboards are clearing faster and alerts are resolved at unprecedented speed, why aren't IT service desks reporting lighter workloads? The research found that 71% of IT teams said their actual workload has remained flat or increased since adopting AI. This reality appears to contradict what we’ve been told about AI ...
Two years ago, almost every customer conversation about AI started with the same questions: Which model should we use? What can it do? Is it ready for the enterprise? Today, those discussions have moved on. CIOs are far more interested in how to govern AI, integrate it with existing systems, prepare their workforce and make it part of everyday operations. The challenge is no longer to prove that AI can deliver value. It's instead about how to embed AI into the business in a way that's secure, scalable and delivers measurable outcomes ...
Two things happened to production incidents between 2023 and now, and they did not happen at the same speed. The first is that a class of dependency that barely existed three years ago now accounts for one incident in ten. Incidents disclosed by AI model and AI application providers rose from 1.7% of all disclosed unplanned incidents in 2023 to 10.7% in 2026 year to date, roughly a sixfold rise; that counts only incidents at AI companies themselves, so the true share is higher. The second is that the time to close an incident has not come down ...
When an AI assistant gives an incomplete or incorrect answer, teams often blame the model. They adjust prompts, switch models, increase context windows or test a new retrieval strategy. However the model may not be a problem. In many enterprise AI workflows, the problem begins inside the document-ingestion pipeline ...
If you talk to any security or observability teams right now, they're all fighting the same fire: their tooling was built to ingest X, but their sources are pumping Y and soon to be doing Z. The knee-jerk reaction is always the same: we need more platform. However, this reaction is wrong. Let me explain why, because the solution to this problem is foundational, not financial. Instead of hurling yet more money at the problem, make sure you've done what's needed upstream ...
Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...
AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...
Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...