As global supply chains grow more complex, the use of AI in traceability and Supply Chain management is becoming vital for improving visibility, automating decisions, and securing data integrity. True success, however, depends on two key elements: access to accurate, secure AI-ready data and the ability to process that data within its proper business context.
In this article, we tap into how AI agents, powered by the Model Context Protocol (MCP), are reshaping end-to-end traceability by enabling trusted, scalable intelligence across complex supply chains.
The Challenge of Secure AI Adaptation
Traceability solutions, such as Kezzler, are typically delivered as managed SaaS platforms, requiring advanced, fine-grained access controls. This complexity involves a combination of Role-Based Access Control (RBAC) and group-level permissions, ensuring that data is strictly separated across tenants, brands, and even individual users.
This security architecture introduces a key challenge for traditional AI adoption: the risk of data leakage when training or using a large language model (LLM) across datasets. Unlocking the value of AI without compromising governance requires a secure method of combining AI capabilities with traceability event data.
From RAG to the Model Context Protocol (MCP)
Early AI implementation often relied on Retrieval-Augmented Generation (RAG). While RAG systems successfully combine general LLM knowledge with secure, specific traceability data, they quickly encounter limitations:
- Context Window Constraints: They are often restricted by the model’s context window size.
- Single Dataset Limitation: They can usually only operate effectively on one dataset at a time.
A much stronger alternative is the Model Context Protocol (MCP). MCP defines how AI agents interact with tools and data, whether hosted locally or remotely. Instead of being constrained by a single context window or dataset, AI agents can dynamically gather, combine, and reason across multiple sources of information while maintaining strict access controls, accessing data and APIs with the same restrictions as the end user.
This architecture is instrumental for complex supply chains, allowing AI to generate deeper insights safely. In practice, the MCP capability can be seamlessly integrated into a company’s existing servers, acting as an enhancement to the current server infrastructure, rather than a replacement. This ensures a flexible solution that avoids the high cost and complexity associated with deploying entirely new systems.
Unlocking Business Value with MCP
Our natural starting point for MCP integration was Kezzler’s analytics backend. Since our traceability event data is structured using JSON Schema, which is the very standard MCP uses to define data properties, implementation was highly streamlined.
By leveraging MCP, organizations can transform static dashboards into interactive AI-driven analytics. Instead of viewing fixed reports, users can directly query their data through AI agents such as Gemini or Claude, asking dynamic, context-aware questions such as:
“Show me all shipments of frozen vegetables that experienced a temperature anomaly in the last quarter.”
Throughout these interactions, sensitive product data never leaves the secure cloud environment. The AI layer functions as a controlled interface, preserving full data integrity and security.
Extending AI into Daily Operations
Beyond analytics, AI agents can be integrated directly into daily workflows. Kezzler’s applications for manual track and trace operations, such as pallet reception or box inspection, are built around curated sets of “Activities” tailored to specific use cases and access rights.
Because these activities are tightly controlled, they are ideal as MCP tools for AI agents. This allows AI to safely assist or execute tasks within operational workflows without exposing sensitive APIs or raw data. This approach ensures full security and compliance controls are maintained throughout the supply chain process.
The Future of Secure Traceability
The adoption of the Model Context Protocol (MCP) represents a critical step toward secure, scalable AI in traceability. It enables future innovations such as rich mobile AI experiences in voice and video mode, where agents can interpret visual or voice inputs triggered by a QR code or Data Matrix relevant to a task.
By standardizing secure data interaction, MCP provides the foundation for the next generation of trustworthy, AI-powered supply chain visibility.
Ultimately, MCP integration reinforces Kezzler’s core promise: security, efficiency, and cost effectiveness. Ensuring sensitive product data remains securely within the company’s cloud environment while streamlining data access and action across the entire Supply Chain.