White Papers Agentic Control Tower: Conversion to Autonomous Detection-Judgement-Execution Feedback Loop Beyond Visibility

Key Definitions

Supply Chain Control Tower (SCCT)

A centralized system that integrates and manages data across the entire supply chain—from procurement and manufacturing to logistics and sales—while enabling organizations to detect and manage exceptions at an early stage. It has evolved to enhance Supply Chain Visibility and support faster, more informed decision-making.

Agentic AI

AI powered by Large Language Models (LLMs), such as ChatGPT, Claude, and Gemini, that can understand assigned objectives, autonomously plan the tasks required to achieve them, and execute those tasks. Unlike conventional rule-based software, Agentic AI analyzes diverse data sources and formulates context-specific response strategies autonomously.

Agentic Control Tower

A feedback loop–based supply chain operating infrastructure that collects data across the supply chain in real time, enables AI agents to autonomously detect abnormal situations, formulates response strategies within predefined operational scope, and determines and executes final response actions through collaboration with human operators.

Ontology

A structured knowledge framework that defines relationships, rules, and constraints among concepts within a specific domain. In supply chain operations, ontologies enable LLMs to understand supply chain data and business context, supporting more accurate decision-making throughout the Detection–Judgement–Execution process.

Agentic Control Tower: Key Questions

• Q1.

  Why does the Supply Chain Control Tower require a new paradigm?

  Traditional Supply Chain Control Towers have evolved to enhance supply chain visibility by providing operational data to users. However, issuing early warnings, determining response strategies, and executing operational actions have remained largely dependent on human judgment and execution capabilities. As massive volumes of data are shared in real time, cognitive overload has emerged as a new operational bottleneck.

• Q2.

  How does Agentic AI transform supply chain operations?

  Powered by LLMs, Agentic AI can identify problems across diverse data sources and autonomously formulate context-aware response strategies. This enables Supply Chain Control Towers to evolve beyond data monitoring into an autonomous Detection–Judgement–Execution feedback loop.

• Q3.

  How does an Agentic Control Tower differ from a traditional Control Tower?

  Whereas traditional Control Towers focus primarily on data integration and supply chain visibility, the Agentic Control Tower enables AI agents to autonomously detect supply chain issues, assess situations, and formulate response strategies within predefined operational boundaries. Human operators focus primarily on exceptional situations and final operational coordination.

• Q4.

  Why is unstructured data important?

  Operational disruptions frequently originate from unstructured data sources—including emails, news articles, images, videos, and phone conversations. Agentic AI can interpret and reason over these data sources without requiring dedicated preprocessing technologies, enabling broader and more comprehensive detection of supply chain exceptions than traditional Control Towers.

• Q5.

  Why is ontology necessary?

  General-purpose LLMs may have limited understanding of supply chain–specific knowledge. Ontologies structure the relationships and meanings among supply chain entities—such as orders, contracts, transportation, and costs—allowing Agentic AI to better understand operational context and deliver more reliable decision-making.

• Q6.

  How are global companies adopting Agentic Control Towers?

  project44 has introduced an AI agent–powered supply chain operating platform, while Blue Yonder has launched its AI-driven Cognitive Solutions across the end-to-end supply chain. Net Feasa has demonstrated an Agentic Control Tower platform that deploys AI agents at the container level, showcasing AI-driven autonomous supply chain operations.

• Q7.

  What should organizations prepare?

  Successfully implementing an Agentic Control Tower requires prioritizing field data acquisition and ontology development before deploying AI agents. Organizations should begin with high-frequency, low-risk operational tasks and gradually expand AI adoption. In addition, the primary KPI of the Supply Chain Control Tower should shift from decision quality toward decision-making speed and operational coverage.