AI agents in established system landscapes

How to use MCP to seamlessly integrate AI agents with IT and OT systems, scale projects and deliver real operational value.

Free of charge. Compact. And featuring specific, practical use cases.

From generative AI to productive AI agents in industry

In industry, AI agents are evolving from mere assistance systems into digital colleagues. They can independently gather information, assess contexts, prepare decisions and trigger actions within business systems. As a result, what was once sporadic AI support is becoming an operational lever for efficiency, quality and responsiveness.

Industrial companies already possess extensive data sets in production, maintenance, IT and business management systems. Nevertheless, the benefits of many current AI solutions remain limited. Although generative AI tools are already being widely tested and deployed, their impact is often confined to isolated productivity gains for individual employees. Sustainable added value for the core business usually only arises when AI not only generates content but also understands processes, prepares decisions and can trigger actions in real-world systems.

This is precisely where AI agents come into their own. The biggest hurdle usually lies not in the intelligence of the models themselves, but in their controlled integration into established IT and OT landscapes comprising ERP, MES, CRM, DMS, ticketing systems, production facilities and other specialist applications. This white paper demonstrates how this challenge can be addressed using MCP as a unified, secure and scalable integration platform.

Name*

Surname*

E-Mail*

Telephone*

Company*

Receiver-Email

Website

Your data entered in the contact form will be processed by us for the purpose of contacting you. The legal basis for this is either the initiation or fulfillment of a contract (Art 6 para 1 lit b DSGVO) or our legitimate interest in answering your inquiry (Art 6 para 1 lit f DSGVO). You can find more information in our privacy policy

Community News: Yes, I would like to be informed about news about Orchestra, events, training offers and services of soffico GmbH by e-mail via the soffico Community News. I consent to my data entered in the form being processed and stored for this purpose. Consent is voluntary and can be revoked at any time with effect for the future via the unsubscribe link in the newsletter or by e-mail. You can find more information in our privacy policy

The Model Context Protocol as a basis for integrating AI agents

The Model Context Protocol, or MCP for short, addresses the key integration challenge between AI agents, data sources and enterprise systems. As an open standard, it establishes a uniform, secure and scalable connection through which agents can access information, resources and functions in a controlled manner.

However, the operational value is not created by the protocol alone. What is crucial is an integration foundation that provides structured access to data sources, processes, access rights and system logic within established IT and OT landscapes. This is precisely where Orchestra comes in: as a standardised software solution for secure, scalable data exchange, soffico positions Orchestra as the foundation for making legacy and brownfield systems agent-ready and making data and processes available to AI agents via MCP.

MCP is thus not merely a technical standard, but a strategic foundation for an enterprise-wide agent ecosystem. In this way, companies create the conditions to integrate AI agents into their value chain not in isolation, but systematically and in a controlled manner.

The key challenges involved in introducing AI agents into industry

The added value of AI agents does not stem solely from powerful models. What matters is whether they can be integrated securely, in a controlled manner and at scale into existing processes, data sources and system landscapes. This is precisely where many industrial companies are currently facing the same challenges.

Established IT and OT landscapes
Over the years, complex system landscapes have developed in many industrial companies. Valuable information is stored in ERP, MES, CRM, DMS and other specialist systems, but is often only accessible and usable at considerable expense.

The limited business value of current AI solutions
To date, many AI tools have primarily served to boost individual productivity. However, they often fail to deliver measurable added value in day-to-day operations.

Significant effort required for integration and scaling
Use cases often fail not because of the idea itself, but due to a lack of standards, proprietary interfaces and the difficulty of scaling successful pilot projects across the board.

Security, Governance and Control
Particularly in industrial environments, data access, system actions and automated decisions must remain secure, traceable and controllable.

MCP as the foundation for scalable AI agents

The challenges described show that the benefits of AI agents in industry do not depend solely on the quality of the models. What is far more crucial is whether they can be securely, scalably and controllably integrated with existing IT and OT systems.

This is precisely where MCP comes into its own. As an open standard, it creates a common foundation for connecting AI agents to data sources, applications and processes. This reduces integration efforts, enables use cases to be implemented more quickly and makes it easier to scale successful pilot projects.

For decision-makers in industrial companies, this presents a clear opportunity: Those who establish the prerequisites for MCP-based architectures at an early stage lay the groundwork not only for testing AI agents on a case-by-case basis, but also for integrating them in a controlled manner into operational value creation. MCP is thus not merely a technical enabler, but a key factor for greater efficiency, higher quality, improved responsiveness and faster innovation within mature IT and OT system landscapes.

Request whitepaper