Unified Namespace and AI Agents: Game-Changer for Manufacturing or Just Hype?

Digital transformation in manufacturing is often bogged down by legacy system architectures, slow batch-based integrations, and rigid hierarchies like the traditional automation pyramid. But two concepts are starting to reshape the narrative: the Unified Namespace (UNS) and AI agents.

In Episode 4 of CONNACK, Marc Jäckle, Tech Head of Smart Factory at MaibornWolff, and Kyrill Schmid, Lead AI Engineer at MaibornWolff, explore what happens when these two forces combine. Are they truly transformative for smart factories, or just tech hype squared?

Watch the full talk below, and read on as we unpack the key takeaways from their presentation.

The Current Challenges in Manufacturing Digital Transformation

Many manufacturers still rely on outdated architectures with batch-based, point-to-point system integrations. When new data needs arise, employees often scramble to gather information from siloed systems like ERP, MES, SCADA, and operator terminals.

This results in time-consuming, error-prone processes, often involving spreadsheets, long email threads, and procurement delays. It slows down innovation and creates barriers to solving even simple questions on the shop floor.

Why the Unified Namespace (UNS) Matters

The Unified Namespace offers a modern solution: a centralized, structured, and scalable data layer where all systems—machines, SCADA, MES, ERP, AGVs, and even forklifts—publish their current state and events.

Rather than chasing data across disconnected silos, teams can access real-time and historical data in one place. HiveMQ makes this architecture possible by reliably streaming MQTT messages across the factory floor and beyond, providing the real-time backbone for a UNS.

What Are AI Agents and Why Now?

AI agents, powered by large language models (LLMs), represent a shift from narrow, task-specific machine learning to open-ended, context-aware systems. These agents can plan, reason, and execute tasks using enterprise data.

Unlike classical ML, they don’t need exhaustive data prep upfront, thanks to foundation models that are already pre-trained. However, to be truly useful in manufacturing, these agents need context, and that’s where the UNS comes in.

This brings us to a key idea shared in the talk: context engineering. Supplying meaningful, structured enterprise context to an LLM, such as equipment hierarchies, machine relationships, KPIs, and metadata, is essential to reduce ambiguity and ensure relevance. The UNS acts as a highly effective channel for delivering that context.

Bringing It Together: UNS and AI Agents

By integrating AI agents with a Unified Namespace, manufacturers can enable intelligent automation without needing deep technical intervention for every question. Agents can query live and historical data, explain problems, suggest optimizations, and even recommend actions.

For example, in the presentation, the team walks through a pizza factory scenario. 

Image Source: MaibornWolff Presentation at CONNACK! Episode 4

An AI agent helps investigate a cooling anomaly by retrieving freezer logs, visualizing data, analyzing dependencies, and identifying that a thermostat replacement may have caused the shift. All this happens through a natural language interface, dramatically reducing time to insight.

This illustrates use cases like:

• A “pocket data scientist” for quick data analysis

• A multilingual assistant on global shop floors

• Alarm rationalization to add context to cryptic machine errors

While these agents are powerful, it’s important to note: they do not act autonomously on critical systems. As the speakers emphasized, operators must confirm any suggested changes before execution.

Real-World Considerations: Modeling, Metadata, and Security

The presenters also highlighted important real-world concerns:

• How much of the factory environment should be explicitly modeled through APIs (e.g., via the Model Context Protocol), and how much can be inferred dynamically by LLMs?

• How can retained MQTT messages and metadata provide timely, rich context, so agents can operate with minimal reconfiguration?

• What does enterprise-grade security look like when AI agents are part of the architecture?

HiveMQ, with features like retained messages, quality of service (QoS), and secure MQTT communication, provides the solid, real-time foundation needed to manage these concerns. Add MaibornWolff’s expertise in smart factory implementations and AI engineering, and you get a future-ready blueprint.

HiveMQ and MaibornWolff: Enabling the Future of Smart Factories

HiveMQ enables manufacturers to implement a Unified Namespace that is robust, scalable, and ready for AI integration. MaibornWolff brings in deep consulting experience, custom software expertise, and an innovative approach to applying AI agent frameworks in industrial settings.

Together, the partnership delivers the tools and know-how to make agent-powered manufacturing more than just a thought experiment. It’s grounded in real architectures, tested technologies, and relatable use cases.

Conclusion: Hype or Hope?

As this presentation of CONNACK illustrates, combining UNS with AI agents is more than a futuristic concept. It’s a practical, evolving strategy for manufacturers seeking greater agility, transparency, and smarter decision-making.

While questions remain—especially around governance, modeling, and safety—the synergy between structured, real-time factory data and intelligent, context-aware agents is undeniably promising.