Observing The Industry Traversing the Digital Divide — Its Finaly here!

Earlier this week at Nvidia’s GTC conference, I had a moment of reflection that, for me, brought a lot of threads together. The energy around AI was undeniable—but more importantly, it wasn’t just hype or futuristic vision. It was grounded in real capability, real deployment patterns, and a clear signal of where the industry is heading.

I shared some of my immediate thoughts in a LinkedIn post during the event, but stepping back, what stood out most was this: the conversation has fundamentally shifted. AI is no longer being discussed as an isolated capability or an experimental technology. It is being positioned as a core building block of how systems will be designed, how operations will run, and how value will be created.

For someone like me—who has been writing for years about composability, democratization, and the need for a new operational architecture—this felt less like a surprise and more like a confirmation. The pieces I’ve been describing are starting to come together in a very visible way.

And it reinforced something I’ve been saying for a long time: manufacturing is on the verge of a fundamental shift. Not another incremental improvement cycle, not another wave of disconnected digital initiatives, but a real transformation in how operations are run, improved, and scaled.

For years, that message felt like a warning. A call to prepare. Today, it feels more like an observation.

Because what I saw at GTC—and what I’ve continued to see in conversations across the industry—is that companies have reached the divide and looking at crossed it. The conversations have changed. The posture of leadership has changed. And most importantly, the level of commitment has changed.

I referred to this earlier in my 2025 trends webinar as a watershed moment, and what we are seeing now is exactly that playing out in real time. I would strongly encourage you to watch that discussion, as it frames much of what is now unfolding across the industry:

What’s important is not just that change is happening—but how it is happening!

Vibe Coding and the Realization of Democratization

One of the clearest signals of this shift is how solutions are now being created. I’ve spent a lot of time over the years writing about democratization in manufacturing—the idea that the people closest to the work should be empowered to improve it, and that technology should enable that rather than constrain it. What is emerging now with AI, and what some are starting to call “vibe coding,” is the most complete realization of that idea that I’ve seen in my career.

What makes this different from previous waves of low-code or no-code is not just accessibility, but the collapse of effort between intent and execution. The ability to describe a problem, iterate on a solution, and see something functional emerge in minutes fundamentally changes the dynamic of how operations evolve. It brings solution creation directly into the operational context, where engineers, operators, and subject matter experts can shape systems in a much more immediate and iterative way. We are now seeing a world where:

• A process engineer can describe a problem and generate a working application
• An operator can help shape a workflow in real time
• A team can iterate on solutions at a pace that was previously unimaginable

This is not incremental improvement. This is a step change in how value is created and something I have consistently pointed to in my writing on composability and frontline operations platforms.

The shift from centrally developed, rigid applications to adaptable, user-driven solutions that reflect the reality of the shop floor.

But what is becoming clear now is that AI is not just enabling this shift—it is accelerating it to a point where it is unavoidable and, I feel, it's removing the mindset barrier. The discussions about technical capabilities, or features and functions are quickly fading away, including the odd ask about monolithic systems and OOTB configurations. They are shifting to be about how quickly solutions it can be built and how effectively it can be applied. That changes expectations at every level of the organization, particularly at the executive level, where the potential for rapid productivity gains becomes much more tangible.

At the same time, this level of democratization introduces a new kind of responsibility. When the ability to create is broadly distributed, the risk of creating the wrong thing—or creating the right thing in the wrong way—also increases. This is where the narrative needs to mature beyond excitement about capability and into a deeper understanding of what it takes to operate in this new model.

Why Platforms Are Now Critical to Operational Integrity

As AI transforms the ability to create solutions, it is tempting to assume that bringing those solutions into operations will follow the same path. This is where manufacturing fundamentally pushes back. The same forces that make “vibe coding” so powerful—the speed, the accessibility, the freedom to create—also introduce a level of variability that operations simply cannot absorb without consequence. In a production environment, the introduction of new technology, solutions, logic, automation, or decision-making is not an isolated act. It becomes part of a tightly coupled system where even small inconsistencies can propagate quickly.

In these environments, the consequences of error are immediate and often irreversible. A mistake cannot be rolled back with a software update, and failures in safety, quality, or compliance can have serious and lasting impact. This reality fundamentally reshapes what trust means for AI. Trust is not about believing that a model is intelligent or statistically accurate, but about whether a system behaves predictably under changing conditions, supports human judgment, and fails safely when uncertainty arises. In operations, trust is earned through repeated, consistent performance in the flow of everyday work.

While AI can generate applications, workflows, and even autonomous behaviors with remarkable speed, manufacturing requires that every one of those elements operates within clearly understood and controlled boundaries. One misstep—whether it’s an incorrect parameter, an unexpected interaction, or an opaque decision—can create cascading effects. Quality can be compromised, performance can degrade, and most critically, safety can be put at risk. In my experience, nothing halts adoption faster in a manufacturing organization than a single visible failure that undermines confidence in the system.

You cannot afford uncontrolled experimentation in a live production environment. This is why I’ve consistently emphasized the importance of a platform-based approach—not as a technology preference, but as an operational necessity. A true operational platform provides:

• Governance over what is created and deployed
• Context so that solutions are aligned with real processes
• Control to ensure consistency, traceability, and compliance
• Resilience so that failures are contained and managed
• Connectivity so that decision and action are based on a holistic understanding
• Content that is industry specific and ready to increase quality and resilience

Accountability in this environment is unavoidable. When AI influences how equipment is configured, how deviations are handled, or whether a product is released, responsibility does not shift to the algorithm. Humans remain accountable for outcomes, which makes human-in-the-loop not just a design preference, but a requirement. If an AI system makes a mistake, and they certainly do, trust erodes quickly—and once that trust is lost, it is very difficult to regain. This is even more pronounced in regulated industries, where expectations around data integrity, traceability, and explainability are explicit, and systems must be understandable not only to technologists, but to operators, engineers, quality professionals, and regulators.

This is precisely why a platform approach is not optional—it is foundational. A manufacturing-focused platform creates the controlled, governed environment where AI can actually operate within the strict realities of production. It is what ensures that solutions are not only created quickly, but behave predictably, meet quality standards, respect safety constraints, and remain compliant over time. Without that structure, the same capabilities that make AI so powerful will introduce unacceptable risk. In manufacturing, you cannot compromise on errors, defects, or safety—and you don’t get multiple chances to get it right. A purpose-built platform is what makes it possible to harness the benefits of AI and “vibe coding” without violating the core requirements of the operation. With a platform, you enable what I often describe as controlled democratization—the ability to innovate broadly, but within a structure that protects the integrity of the operation. Without it, scale is not just difficult—it’s dangerous.

Why Domain Expertise Still Defines Success

The final and perhaps most critical element in all of this is the role of domain expertise—something that is increasingly being underestimated in the current enthusiasm around AI. There is a flawed narrative that AI can compensate for gaps in knowledge or experience, that it can generate solutions independent of deep understanding. But as I have explored in other posts, particularly when experimenting with AI as a creative partner, the technology is only as effective as the context and intent that guide it. In manufacturing, this distinction is not subtle—it is fundamental.

With the incredible democratization AI brings to creating solutions accelerates, this constraint does not disappear—it shifts. It becomes even more important and critical to define the right problem and to judge whether a solution will actually work within the realities of the operation. Manufacturing processes are complex, tightly interconnected, constraints by physical realities, driven by well defined methods, and governed regulatory requirements. Understanding how cause and effect play out in that environment is not something that can be inferred generically; it is built through experience, engineering discipline, and operational knowledge. AI can amplify that expertise, but it cannot replace it—and without it, the risk of creating solutions that fail in practice increases significantly.

In the hands of those with deep expertise, AI accelerates learning, experimentation, and scale. This becomes even more critical as we move toward more autonomous systems, where agents are expected to act within operations. Their effectiveness depends not just on data, but on the depth of understanding embedded in how they are designed—grounded in the experience of those who know how the system behaves, especially when things don’t go as planned.

The Take-Away

What we are seeing right now is the convergence of three defining forces: 

1. The democratization of solution creation through AI.
2. The need for structured platforms to govern and control that creation.
3. The enduring importance of domain expertise to ensure it all works in the reality of manufacturing operations. 

This convergence is not theoretical—it is actively reshaping how companies think about, design, and run their operations.

Crossing the digital divide was never just about connecting systems or digitizing processes. It was about enabling a fundamentally different way of operating—one where the creation, deployment, and continuous improvement of solutions are embedded directly into the fabric of the operation. What we are now beginning to see is what that actually looks like in practice, and it is both powerful and unforgiving.

As with any significant shift in manufacturing, success will not come from simply adopting the latest technology. It will come from understanding how to integrate these capabilities into the operational reality—balancing speed with control, innovation with discipline, and democratization with accountability. The companies that get this right will not just move faster—they will operate differently, and ultimately, outperform.

Video Illustration: The AI Knowledge Revolution

An Alternative Visual

This is my alternative visual narrative that explores how AI and specifically Agentic AI are fundamentally disrupting traditional manufacturing hierarchies. The video illustrates the "compression" (or collapsing) of the classic Data-Information-Knowledge-Wisdom (DIKW) pyramid, showing how AI now acts as an intelligent intermediary that instantly transforms unstructured "human language"—like deviation comments and work instructions—into actionable operational wisdom

Key themes include:

• Collapsing Complexity: Moving past the rigid, million-dollar data models of the 1990s to a system that understands context like a human.
• Knowledge Flow: Driving multi-site transformation through "Outbound" digital playbooks and "Inbound" frontline innovations.
• Augmented Lean: Democratizing expertise across the entire network so every site becomes both a consumer and a producer of wisdom.

I also talked about it in an interview for Tulip's "Human in the Loop" series.

Behind this is a body of work and a lot of written content that I will publish in the future. As I have written before I am experimenting with different formats to convey the message about composability. 

Stay tuned more content will be coming out in the future!

Composability, Governance, and the Future of Agentic AI in Manufacturing

We’ve all heard the stories: “Somebody created a solution in just a few hours with no-code or vibe coding…”.

It’s exciting, right? Engineers solving problems in record time, building digital tools with nothing more than intuition, creativity, and a bit of AI support. This is the promise of democratization—Agentic AI empowering everyone to innovate and improve.

And in many contexts, that speed is a superpower. But in manufacturing, the story is more complicated. Operations are inherently complex: machines, materials, people, processes, schedules, quality checks, and compliance requirements all interact in a dynamic web that is nonlinear, a complex adaptive system. In such environments, even small changes can cascade unpredictably, amplifying into disruptions far greater than their cause—an inherent feature of systems where interdependencies drive emergent outcomes.

This is where the risk lies. If an unguided agent makes the wrong decision in such an environment, things can go wrong very quickly—and often in ways that are difficult to anticipate or trace. The result might be downtime, compromised product quality, production delays and at worst safety issues. But the bigger danger is cultural: when something fails spectacularly, it doesn’t just cause operational damage—it can scare the organization away from using the technology at all.

Instead of unlocking incredible productivity, one misstep can trigger a mindset of “we don’t dare do this again…”. That’s not just a lost opportunity; it’s a setback that can stall digital transformation for years.

Digital Maturity Gaps

With the incredible pace of innovation of digital technologies and specifically Agentic AI, we have to acknowledge a hard truth: most manufacturers are still not fully ready to capitalize on this technology. The foundations are often shaky, and if we introduce solutions built by and with agents into this environment without addressing the gaps, the risks multiply.

Some of the most common issues include:

• Fragmented data that prevents end-to-end visibility.
• Disconnected IT and OT systems that block seamless collaboration.
• Cultural skepticism and lack of digital maturity that slow adoption and erode trust.

These are not new problems. I discuss them in detail in this blog.  Our traditional models both in technical architecture and deployment of operational solutions for manufacturing were never designed for the agile, composable, and connected environments we’re striving to build today. In addition we can't ignore that in any given manufacturing facility there are machines and systems of varying ages and legacy status.

Consider a Scenario

An enthusiastic engineer uses vibe coding to create an agent that dispatches materials to keep machines and operators as busy as possible. At first, everything looks good, machines are running at full capacity, operators always have work, and parts move quickly into assembly.

But no one realized the agent doesn’t properly recognize WIP limits or downstream capacity. It keeps dispatching jobs even when assembly stations and testers can’t keep up. The result: piles of excess material stack up between stations, overflowing into walkways and creating unsafe conditions for operators.

In a matter of hours, what was meant to boost productivity causes overproduction, flow disruption, and a serious safety hazard. The line is forced to stop because the system created too much of it, in the wrong place, at the wrong time. 

This isn’t hypothetical. It’s the kind of risk that emerges when Agentic AI is unleashed without governance. And unlike traditional tools, agents can act autonomously and at scale, amplifying errors at a speed we may not be able to react to.

Managing Complexity with Composability: Governance, Framework & Platform

Given this reality—where digital maturity is uneven, legacy systems abound, and the dynamic nature of manufacturing operations—the arrival of Agentic AI presents both an enormous opportunity and a serious risk. Agents are coming at us fast, and their power lies in democratization and speed. But those same qualities, in an environment as sensitive as manufacturing, can amplify problems just as quickly as they solve them.

This paradigm shift cannot be left to chance. To harness Agentic AI safely and effectively, we need to approach it in an organized, managed, and focused way. That requires: governance, framework, and platform all working together and grounded in composability.

• Governance gives us the rules, guidance, and guardrails to ensure reliability, safety, quality, and compliance are never compromised. Governance is not just about control; it actively helps organizations overcome barriers like fragmented data, siloed systems, and cultural skepticism by providing structure, discipline, and confidence. Done right, it helps in the transformation of the manufacturing environment from both a technical readiness (data integrity, system integration) and an organizational readiness (culture, mindset, trust).

• Framework provides the structure to make governance actionable. In A Composable Agentic Framework for Frontline Operations, I laid out how agents can be defined by type, given clear goals, and aligned through the artefact model. The framework ensures that agents are not just scattered tools but part of a purposeful, multi-agent system that reflects and supports real operations.

• Platform is the enabling layer. An Agentic manufacturing operations platform purpose-built for frontline operations makes it possible to apply governance and framework seamlessly—across both authoring (building digital solutions) and execution (running the production line). Unlike generic vibe coding environments, such a platform is designed specifically for process engineers and operations teams. It embeds an understanding of how manufacturing works—constraints, variability, compliance, and safety—and provides tools to build solutions quickly while remaining aligned with operational best practices. Most importantly, it enables the creation and deployment of operational agents at scale that are are integral parts of the production system itself.

And underlying all of this is composability. Composability ensures that agents and solutions don’t exist in isolation, but as modular, human-centric, bottom-up elements of a system that adapts as needs evolve. Digital transformation success depends on treating composability not as an IT concept, but as an operational paradigm. In fact, composability was conceived for multi-agent systems (MAS)—and Agentic AI now makes that vision practical.

Final Thoughts

I think that we all agree that Agentic AI despite all the hype is not a fad—it’s here, its real and it’s already reshaping how work gets done. But to harness it safely and effectively, we need to recognize the two distinct scenarios where agents play a role:

1. In building and engineering: where agents help create digital content, processes, and solutions. Here, governance ensures what gets built is valid, safe, and aligned with operational goals.

2. In live operations: where agents support and even run production activities, interacting with machines, data, and humans in real time. Here, governance ensures reliability, compliance, and resilience in execution.

Both scenarios are powerful—they are needed but both also carry risks if unmanaged. And in manufacturing, where operations are complex and dynamic, small missteps can cascade into detrimental consequences.

That is why governance, framework, and platform are critical. Governance provides the rules and guardrails; the framework gives agents structure, goals, and alignment through the artefact model; and the platform operationalizes it all, giving process engineers and frontline teams the environment to deploy agents as integral parts of the production system, and all of this at scale.

And beneath it all is composability. As I’ve argued before, composability was conceived for multi-agent systems—modular, autonomous, and collaborative components working toward shared goals. Agentic AI now makes that vision practical on the shop floor.

The challenge ahead is not whether manufacturers will adopt Agentic AI, but whether they will do so in a way that balances speed with safety, democratization with discipline, and autonomy with alignment. Done right, it promises resilience, reliability, and the kind of productivity gains that digital transformation has always aspired to deliver.

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A Composable Agentic Framework for Frontline Operations

Over the last year, “agentic AI” has shifted from emerging concept to practical conversation. Everyone is now talking about agents, and with today’s tools, building one has become every person’s business. But that raises a much bigger question: what agents should we build, and how do we organize them into something meaningful?

This is a topic that is not new and I have reflected on it through a number of lenses, e.g. Why IIoT is Transformative - its not the technology! and The Genius of the Toyota Production System Explained. This post however offers my first reflections on what a Composable Agentic Framework could look like for operations in general and manufacturing specifically. It’s an attempt to give industry an initial perspective, a concept and maybe some guidance for applying agentic AI to frontline operations in ways that improve both productivity and adaptability. Notice I say "initial", there is clearly much more to this topic that needs to be explored, discussed and debated - in addition the technology is nascent and we can expect much more sophistication and depth as it evolves.

Why Agents? Why Now?

For decades my peers and I, as manufacturing thinkers, have dreamed of holonic systems and fractal factories—production environments that adapt in real time, scale seamlessly, and continuously self-optimize. But until recently, that vision stayed in the realm of theory. The technology just wasn’t ready.

Now it is. Today’s digital platforms, IIoT connectivity, cloud infrastructure, and AI capabilities make it possible to realize this vision in practice. Instead of rigid, monolithic systems, we can now compose operations out of, and with, autonomous, collaborative agents that interact dynamically.

And this matters because composability is built on the idea that continuous, incremental improvements add up to transformation. What’s new is that agentic AI can accelerate these improvements beyond what we can imagine. Large language models and agentic frameworks have already proven their ability to supercharge productivity in domains like software development, research, and customer interaction. The challenge—and opportunity—before us now is to understand and define how to bring that same acceleration into composability and frontline operations.

Agents make this possible. Each agent is discrete, goal-oriented, and autonomous, yet designed to collaborate with other agents. When composed together, they create systems that flex, adapt, and continuously optimize.

In earlier posts, I’ve argued that digital transformation is not about IT and OT learning to coexist, but about creating a new whole where the distinction ceases to matter. That’s the essence of composability. Agents are the next step: a way to make that vision practical, modular, and scalable.

An Agent Framework for Composable Digital Solutions

The starting point is simple: in new digital operations platforms like Tulip, apps already behave like agents.

• They have a clear goal (guide an operator, track a unit, log a machine event).
• They operate autonomously within their context.
• They collaborate with other apps and systems through shared data, triggers, and transitions.

Add AI into the mix, and these apps become agentic apps—supercharged digital teammates. And when multiple apps are composed together, they form a multi-agent system that mirrors the complexity of real operations, in other words they become digital twins in the true sense of the concept.

But in order for this "mirroring" to become a digital twin we need to define some rules and this is where the framework comes in. To move from scattered apps and automations to composable digital solutions, we need a structured way to think about agents.

Add AI into the mix, and apps evolve into agentic apps—supercharged digital teammates. Composed together, they form multi-agent systems that can mirror the complexity of real operations—becoming digital twins in the truest sense. But achieving that requires more than simply building agents; it requires structure and guidance. Without it, the risk is a proliferation of scattered apps and agents with no cohesive purpose—at best delivering little value, and at worst creating more complexity, reduced productivity, and even unsafe outcomes. The goal of this suggest framework is to provide the rules and design principles that ensure agents align toward a shared purpose. This reflects the essence of holonic structures: their power comes not just from autonomy, but from working together toward a common goal. That shared purpose is what makes them optimal, and what turns a collection of agents into a true composable digital solution that delivers measurable benefits.

In my earlier blog post “To Data Model or Not to Data Model”, I described the Artefact Model as a key element in composability: a scalable, flexible, interpretable representation of the operational system. The Artefact Model gives us the common context in which agents can interact—products, orders, machines, deviations, and operators all represented digitally and consistently.

A Perspective on Agent Types

Before diving into the types themselves, it’s important to recognize that agents serve two distinct scenarios in manufacturing:

Authoring / Building – Here, agents augment the creation process. They help engineers, developers, and even citizen builders design solutions faster and smarter. Think of them as co-pilots that propose app templates, generate artefact structures, suggest best practices, and automate repetitive setup tasks. These agents accelerate innovation and democratize solution-building.

Operations – Once deployed, agents act within day-to-day execution. They monitor machines, guide operators, coordinate workflows, manage deviations, and connect enterprise systems. These operational agents are the ones “living” in production, continuously working toward defined goals while collaborating with other agents and humans.

The framework presented here is focused specifically on the operations scenario. That being said there are commonalities and some agent types apply to both scenarios, but the context differs: in building, agents amplify human creativity and speed; in operations, agents amplify execution and adaptability. 

Agentic AI in operations enables a powerful ecosystem where "teams of experts" (agents) work together, demonstrating "collective intelligence". Crucially, operational agents, empowered by AI, transform what were once innate objects like machines and materials into active, intelligent members of the dynamic work environment. We are giving them the ability to be agents—autonomous and collaborative participants within the dynamic manufacturing operation network.

With that lets take a look at a shared taxonomy for the different types of agents in a composable agent framework:

Physical Agents: These agents are defined by their direct representation of physical manufacturing objects within the digital twin. They continuously mirror the real-world status, attributes, and behaviors of their tangible counterparts, enabling real-time monitoring, analysis, and control. Here are some examples:

• Product Agent: Represents a specific product or unit throughout its manufacturing journey. Its goal is to track the product's individual status, quality parameters, and genealogy, providing a comprehensive digital record for each item produced.
• Machine Agent: Serves as the digital twin of a specific piece of equipment or machinery on the shop floor. Its purpose is to monitor machine health, performance metrics (e.g., OEE, availability, performance, quality ), and predict potential failures, enabling proactive maintenance and optimized utilization.
• Tote Agent: Represents a tote or device that carries and conveys product or material on the shop floor. This agent's role is to track the movement of the material or products it carrier in the operations and facilitates traceability and location with ease.

Operational Agents: These agents are defined by their focus on tangible operational entities and processes used in manufacturing management. They manage the flow of work, information, and events, ensuring that manufacturing processes adhere to plans and respond effectively to deviations. Here are some examples:

• Order Agent: Represents a specific production or work order. Its goal is to oversee the end-to-end execution of that order, tracking progress against the schedule, managing material consumption, and ensuring all required steps are completed.
• Deviation Agent: Activated when a process or quality deviation occurs. Its purpose is to identify, classify, and manage the deviation, potentially initiating corrective actions, alerts, or escalation workflows to relevant personnel or systems.
• Schedule Agent: Responsible for dynamically managing and optimizing production schedules. This agent works to ensure resources are efficiently allocated and production targets are met, adapting to real-time changes in machine status, material availability, or order priorities.

System Agents: These agents are defined by their role in facilitating integration and intelligent interaction with broader enterprise-level systems and data repositories. They ensure data consistency, enable seamless workflow orchestration across different platforms, and provide access to critical business context. Here are some examples:

• ERP Agent: Manages the flow of information between Tulip and the Enterprise Resource Planning system. Its function includes receiving work orders, reporting production updates, and managing material consumption and inventory levels in the ERP.
• UNS Agent: Represents the integration with a Unified Namespace. This agent enables seamless, real-time data exchange across the entire operational landscape, ensuring that all systems have access to consistent and up-to-date information.
• Data Lake Agent: Responsible for managing the ingestion of operational data from Tulip into a central data lake and enabling access to this data for advanced analytics and further AI model training. It ensures that the rich data captured by Tulip's composable applications is leveraged for broader insights.
• Device Agent: Corresponds to a specific connected device, such as a scale, barcode scanner, or sensor. This agent's role is to facilitate seamless data exchange between the physical device and the Tulip platform, ensuring accurate data collection and enabling device-triggered actions.

Staff or Companion Agents. These are a general type of agent that augment the human's ability to find information, research topics, suggest improvements, and perform tasks. They are used in a variety of scenarios and serve as utilities in both the operational environment as well as the engineering or builder environments. Here are some examples:

• Quality Research Agent: Quickly finds documentation, suggests troubleshooting steps to an operator, or summarizes a quality history for a supervisor.
• App Builder Agent: Generates app templates, proposes table structures in the Artefact Model, or scaffolds connectors based on device specs—accelerating citizen developers and engineers.

What unites these agent types in this framework is the three core properties: it has a goal, it operates autonomously within a bounded scope, and it is collaborative—able to exchange data, signals, and intent with other agents and humans.

Crucially, these agent types do more than align with the Artefact Model — they enhance it. By consistent real time representation of artefacts (products, resources, orders, deviations), agents enrich the shared digital twin with actionable state, decisions, and provenance. That enriched Artefact Model becomes the lingua franca that lets agents interoperate reliably, enables composition, and prevents the classic failure mode: a landscape of scattered apps and ad-hoc bots with no unifying purpose.

In short: agents must be designed to work as an integral part of  the Artefact Model so that autonomy and collaboration add up to a cohesive, safe, and value-driving digital twin.

Beyond Incrementalism: The Future of Multi-Agent Collaboration Frameworks

Much of what we know about manufacturing improvements has historically been driven by incrementalism—step-by-step gains in efficiency, quality, or throughput. This mindset is not wrong; in fact, it is the foundation of continuous improvement and the heart of lean thinking. But incrementalism alone can only take us so far. To thrive in today’s volatile and complex operating environments, we need systems that don’t just get gradually better but can adapt dynamically to new conditions.

This is where multi-agent collaboration becomes transformative. Agents, by design, are autonomous but collaborative, and when they interact at scale, they exhibit something greater than the sum of their parts: collective intelligence.

The result is emergence and self-organization: a system-level intelligence and adaptability that was not explicitly programmed into any single agent. Emergent behavior is what allows multi-agent systems to flex and reconfigure in response to disruptions, market changes, or unexpected events. This is not just automation—it’s a new layer of operational intelligence applied directly to the frontline.

But to realize this potential, we must also reconsider the frameworks that structure our digital manufacturing systems. Such a composable, agentic world—where apps act as agents and operations are orchestrated by multi-agent systems—doesn’t fit neatly into traditional manufacturing systems standards and hierarchies definitions (see my earlier post, OK, Let’s Talk ISA-95).

That doesn’t mean throwing standards away, but it does mean rethinking or adapting them to this new reality. If emergence is the key to adaptability, then our models and standards need to evolve to describe systems that are dynamic, distributed, and composable rather than hierarchical and rigid.

In short: incrementalism is still essential, but it is no longer sufficient. Collective intelligence, powered by agents and guided by frameworks like the Artefact Model, is what will enable manufacturing to achieve adaptability at scale—and truly fulfill the promise of digital transformation.

Final Reflections

Building, authoring, creating agents is within everyone’s reach. But without structure and guidance, we risk ending up with a fragmented landscape of apps and bots—scattered efforts that deliver little value, or worse, add complexity, reduce productivity, and even create unsafe outcomes.

My hope is that this initial framework presented here provides that needed guidance. It should help us define what kinds of agents to build, how to compose them into systems, and how to ensure they align with a unifying purpose. It grounds agentic design in Composability and its Artefact Model, ensuring that agents not only adhere to but actively enhance the shared digital twin. This alignment is what keeps autonomy and collaboration from drifting apart and turns them into something greater: collective intelligence with emergent adaptability.

This is why composability and agent frameworks matter. They give us the structure to channel autonomy toward common goals. They offer to guide us on the path to increased productivity with adaptability. And they point to the need for new thinking in our standards and models—beyond the rigid boundaries of monolithic approaches, toward a more dynamic and composable reality.

In the end, the promise of agentic AI in operations is a the newest step in the digital transformation journey: Continuous Transformation - reinvention at scale of manufacturing operations.

And this is just the beginning. There is so much more to explore, define, and refine. I invite you—industry peers, practitioners, and thinkers—to engage in this discussion and debate. Let’s shape together what a composable agentic framework should look like in practice. After all, it takes a village... 

Why Are We Still Talking About MES–ERP Integration?

Every few months, I still come across discussions about how to integrate MES and ERP. And every time, I find myself asking: why are we still talking about this?

It’s a bit like asking whether a boat floats. The answer is obvious—yes, it does. The real question is where is it going and why are we on it?

Integration Isn’t the Problem

Let’s be clear: integration between MES and ERP is not new, nor is it unsolved. For decades, manufacturers have been connecting these systems to exchange the information that keeps their operations running. I challenge you—have you ever heard of an MES system that couldn’t integrate to ERP?

The technology is there. APIs, middleware, standardized data models, cloud-native platforms—the tools have only gotten better. Integration is no longer the hard part.

As I wrote in an earlier post "About Accountants and Production", ERP and MES have always been about different things. ERP is designed for financial management (order-to-cash) - transactions, costs, compliance, reporting. MES is built for the shop floor—real-time visibility, control, and execution. Each system has its domain. Integration ensures they don’t talk past each other.

But the value doesn’t come from whether or not you can connect the two. It comes from what you do with that connection.

From Technical to Value-Driven

When integration conversations remain technical—what middleware to use, which API calls to expose—we miss the bigger picture.

The true conversation should be:

• What processes, operations and decisions do we want to improve?
• What outcomes are we aiming to achieve?
• What value will the integration unlock for the business?

For example, integrating to have a streamlines and effective work order execution from ERP to MES is not valuable because the two systems are connected. It’s valuable because it eliminates manual re-entry, reduces errors, speeds up production scheduling, and ensures financial systems reflect operational reality in near real time.

Integration is the means. Value is the end.

Enter the Age of Digital and AI

We’re well into the era of digital, transformation is ongoing and constant, and AI in manufacturing is becoming a reality. Advanced analytics, machine learning, digital twins, and agentic AI are reshaping how operations are managed and humans work. Against that backdrop, spending time debating MES–ERP integration feels outdated.

The real opportunity is to ask: how do these systems, together, create the digital backbone that enables AI to bring operational insights that deliver business value?

ERP knows the plan. MES knows what actually happened. AI thrives when it can see both and spot patterns across them—optimizing schedules, predicting disruptions, and suggesting interventions. That’s the conversation worth having.

Time to Move On

So let’s put this to rest: MES and ERP can integrate. They do integrate. The technical questions have answers.

The real debate—the one that matters in the age of digital and AI—is about value. How do we design our digital architectures, processes, and cultures so that integration serves as the foundation for smarter, faster, and more agile manufacturing? Shift the focus from can we integrate? to what value will the integration deliver?

Experiencing The Realities of Disruptive Technology

I still remember my first encounter with CAD software in the early 1990s. I had just learned to draft using  drafting table, T-squares, and mechanical pencils. And then here comes CAD, I was excited—and admittedly a little naive—about the possibilities. 

During my university years, I was required to take a compulsory drafting course, even though CAD had already become an industry standard. I spent time drafting by hand, carefully measuring every line and angle, despite knowing that digital tools had long since taken over professional workflows. After the learning how to draft my expectation of CAD was that it would be a tool that would instantly produce my designs for me, yes I had inflated expectations of a magical tool that would do all the work for me. I would think of an idea and like magic, the computer would take care of everything.

Of course, reality quickly set in. Instead of effortlessly generating perfect blueprints, I had to learn an entirely new way of thinking. CAD didn’t replace the design process; it transformed it. I still needed to have the ideas, understand the engineering principles, and apply them. Drawing the first line, starting with defining points then connecting them, was not a trivial task. CAD was a powerful tool, but it required skill, experience, and thoughtful direction to produce anything meaningful. It didn’t do the work for me—I had to learn how to use it effectively.

Fast forward to today, and I see a strikingly similar pattern emerging with the rise of AI—especially Generative AI (GenAI). There are a similar confused and inflated expectation that GenAI will simply generate perfect content, solve complex problems, and even replace human creativity. But just like CAD in the ‘90s, GenAI is not a magic wand. It’s a tool—one that still relies on human intelligence to be effective.

The misconception that AI will replace human ingenuity overlooks a crucial reality: AI doesn’t think, ideate, or problem-solve on its own. It relies on human direction. Just as I had to learn how to structure my designs for CAD, today’s professionals must learn how to craft effective prompts, refine outputs, and iterate toward meaningful results. Prompt engineering isn’t just about asking the right questions; it’s about framing problems, guiding AI to useful insights, and applying critical thinking to refine its output.

Take this blog post as an example. You can’t simply tell a GPT AI tool to “write a perfect blog post this specific comparison” and expect a meaningful result. I had to define the scope, structure the request, and continuously iterate to get content that aligns with what I, emphasis on "I", wanted to convey. These are my ideas and my experiences - I had to include these in the prompt. AI amplifies human creativity—it doesn’t replace it. The key here is that ChatGPT did make me more productive, I wrote this post in minutes versus a few hours, including the graphic.

Just as CAD revolutionized drafting and engineering without eliminating the need for skilled designers, GenAI is reshaping many industries making content creation, coding, and problem-solving—but it still requires expertise and direction. The future isn’t about replacing human intelligence but augmenting it—"humans in the loop." Those who understand how to harness AI and its applications will be the ones who truly unlock its potential, especially in manufacturing operations where precision, problem-solving, and adaptability are crucial. Technology evolves, but one thing remains constant: the value of human ingenuity. Whether it’s designing with CAD or leveraging GenAI, success comes not from the tool itself, but from how we apply it with expertise and intent.

Digital Maturity Embracing the Paradigm Shift with Composability

In my last post, The 5 Pillars of Composability, I broke down how composable systems have to be bottom-up, agile, democratized, human centric and compliant to enable a resilient digital  manufacturing environment. However, these pillars don't standalone and you may have noticed that the graphic drew the pillars within a structure, i.e. a house. Yeah a bit cliche but its a simple way to drive the point - the foundations is connectivity and data integrity while the roof is digital maturity. Without connectivity to reliable data and a high level of digital maturity, the benefits of composability can be diminished. 

• Data integrity ensures that the digital solutions operate on accurate, consistent, and trustworthy data, preventing breakdowns in decision-making or system performance. High-quality, accurate data is essential for making informed, evidence-based decisions
• Digital maturity enables organizations to effectively adopt composable architectures, ensuring they have the technical capabilities, culture, and processes in place to take full advantage of modular solutions. 

Together, data integrity and digital maturity complete the story of composability by ensuring that organizations can both build and sustain these flexible, adaptive systems in a reliable and future-proof manner. In this post I want to dive deeper into these concepts as they are foundational concept that propels us forward in the digital paradigm shift to reshape manufacturing operations.

Digital Maturity, Connectivity & Data Integrity complete the composability model

What is Digital Maturity?

Digital maturity represents an organization’s capacity to leverage digital tools and processes effectively based on their strategy with the objectives of significant increases in productivity. It's not a simple matter of capabilities related to adoption or implementation new technologies but rather about integrating them strategically to align with long-term goals. As companies mature digitally, they move beyond basic digital adoption to foster seamless connectivity across systems, data transparency, an empowered workforce and with that comes order of magnitude productivity improvements - the ultimate goal for transformation.

A digitally mature organization is one where digital tools support real-time decision-making, democratized technology access, and predictive insights - aligning perfectly with the benefits of composable principles. This is also what the Pharma 4.0 operational model prescribes, that manufacturers need to do more than automate - they need to integrate everything from operations to compliance in a way that’s seamless, agile, and deeply data-driven.

What is Connectivity & Data Integrity?

Its not news that data must be accurate, accessible, and trustworthy across all systems for true digital maturity. It must be connected, collected, contextualized and stored to ensure that data collected from production lines, suppliers, and product design all feed into a single, reliable source, creating actionable insights and reducing costly errors. Yet surprisingly it still is very much a challenge in many solutions that I encounter. Mostly in legacy situation, implementation of monolithic system, but also if not considered appropriately in newer digital technologies.

Connectivity in manufacturing is all about creating a seamless flow of data across systems, devices, and people. Imagine every machine, sensor, and workstation talking to each other and feeding data into a single network that anyone can access in real time. When systems are connected, it’s like moving from an isolated set of puzzle pieces to seeing the whole picture. Connectivity enables manufacturers to understand what’s happening on the production floor instantly, respond to issues faster, and improve coordination across departments. For example, in a highly connected factory, when a machine experiences a slowdown, that data can flow directly to maintenance teams and operators, letting them address the issue right away.

But connectivity is only as useful as the quality of data being shared, which brings us to data integrity. Data integrity is about making sure that information is accurate, reliable, and complete across its entire lifecycle. It’s not just about having data; it’s about having good data you can actually trust. In the Pharma 4.0 model, where data integrity is critical, maintaining high-quality data is a must, especially for meeting strict regulatory standards. This means putting practices in place to ensure that data isn’t duplicated, corrupted, or altered improperly, so everyone—from operators to auditors—can make decisions with confidence.

Together, connectivity and data integrity are the backbone of any digitally mature operation. They enable real-time visibility, reliable decision-making, and the flexibility to adapt to change. Without them, even the best technology can fall flat. So, as manufacturers embrace digital maturity and composability, focusing on solid connectivity and data integrity will be crucial for a smooth, resilient operation.

The journey from Technology Adoption to Strategic Transformation

Many manufacturers today are adopting digital tools, but there's a significant difference between early digitalization and achieving digital maturity. A mature digital approach emphasizes:

1. Strategic Data Utilization: Digital maturity involves a shift from collecting data in isolated pockets to having unified, actionable insights. For manufacturers, this means no longer relying on static, siloed data but leveraging real-time insights that span from the shop floor to the boardroom. Yes, this in a way nothing new and really dates to Industry 3.0 concepts - however with new digital tools this has become and achievable reality.

2. IIoT & Interoperability: Digitally mature systems don’t merely integrate; they interoperate, embodying the composable principle of Bottom Up where IIoT components are autonomous and collaborative. Composable architectures are inherently emergent in both design and control - the manufacturing solution is required to evolve with minimal friction.

3. Human-Centric Technology: In a departure from an automation focus, the current paradigm shift places people at the center of the digital equation. Technology becomes an enabler for employees, from line operators to managers, allowing them to respond dynamically to changes and resolve issues swiftly.

4. Resilient and Adaptive Workflows: A composable manufacturing ecosystem relies on digitally mature workflows that can adapt to disruptions, whether due to supply chain variances or unexpected equipment breakdowns. A digitally mature manufacturer leverages their digital capabilities to enable resilience, be predictive and adaptive.

The digital transformation journey towards order of magnitude productivity improvements

The path to digital maturity requires a tailored, strategic approach that elevates an organization from a technological upgrade to a business transformation—one that enables agility, resilience, and sustainable growth. The first step in this journey is to assess and align digital initiatives with overarching business goals. Defining what a mature digital state means for each organization—whether it's minimizing downtime, improving product traceability, or streamlining supply chain management—is critical. Aligning digital initiatives with operational excellence or lean initiatives by implementing data-driven approaches to cut down production waste and achieve near-real-time optimization are critical. Drive value by prioritizing areas where digital maturity will have the most impact on operational outcomes.

A characteristic of digitally maturity is how well your organization is equipped to handle the ever-evolving challenges and capitalize on new opportunities. Embracing composability allows your organization to not only keep pace with the current demands but to thrive in the future - thrive with the accelerated pace of digital innovation. Digital transformation should be more that mere adoption of new technology - it is embedding it deeply in your operational fabric, enabling sustainable growth and resilience in the face of change.

About Accountants and Production Managers: ERP vs. MES

This is a rewrite of a whitepaper that I published in 2004 based on a long and frustrating MES selection process where the "can I use my ERP as MES" misunderstanding went rampant. I find that the discussion is still very relevant today and the topic gets even more confusing with some of the emerging digital technologies in this space. So this is an attempt to bring more clarity...

About ERP and MES

With today’s increasingly accelerating manufacturing technology innovation, digital transformation is critical for staying competitive. Among the key systems that have traditionally driven manufacturing operations are Manufacturing Execution Systems (MES) and Enterprise Resource Planning (ERP) systems. The myriad of opinions and discussion on these concept in the context of digital transformation and therefore understanding the differences between these two systems is crucial. In addition, as digital technologies continue to advance, the lines between MES and ERP are increasingly blurring, especially with the advent of No-Code democratization and Frontline Operations Platforms. I have found that and effective ways to conceptualize this is through the analogy of a production manager and a company accountant.

The Accountant: ERP’s Role in Manufacturing

In the last decade ERP systems have seen massive proliferation into many businesses, including manufacturing businesses. These businesses have invested heavily in ERP systems and today struggle to realize payback from these investments. In the case of manufacturing businesses, realizing ROI is logically focused on the production floor, an area in which ERPs are traditionally considered weak.

As the ERP market becomes increasingly saturated, vendors are looking for ways to increase revenue and expand their footprint. ERP vendors have turned their attention to shop floor management and manufacturing execution systems (MES). By adding MES functionality, they can increase license revenue. 

Consider the role of an accountant in a manufacturing company. The accountant manages financial records, oversees budgets, handles payroll, and ensures that all financial transactions comply with regulations. Their work involves high-level data analysis, financial forecasting, and strategic decision-making that influences the entire organization. This is akin to the role of an ERP system.

At their core, ERP systems are advanced accounting information management systems, they are enterprise-wide management tools designed to integrate various functions across a business. In manufacturing, ERP systems handle tasks such as procurement, inventory management, finance, human resources, and supply chain operations. They provide planning tools like Material Requirements Planning (MRP) and Manufacturing Resource Planning (MRP II), which help companies predict future resource needs based on historical data and forecasts.

However, despite their comprehensive nature, ERP systems are not designed to manage the real-time, dynamic environment of the production floor. They excel at providing a broad, strategic view but lack the granular control needed to manage the intricacies of manufacturing processes. Just as an accountant isn’t equipped to manage the day-to-day operations on the production floor, an ERP system isn’t designed to handle the real-time demands of production management.

The Production Manager: MES’s Role on the Shop Floor

MESs have evolved to address the inherently complex production management functions. An MES is a specialized system focused on the shop floor, where it manages real-time production activities. It coordinates equipment, workers, materials, and processes to ensure that production is carried out according to plan. Unlike ERP systems, MES operates in real-time, responding instantly to changes and ensuring that production goals are met. It tracks production data minute by minute, making it possible to identify and correct issues as they arise.

Imagine the role of a production manager. This person is in the thick of things, ensuring that production runs smoothly and efficiently. They manage workers, monitor machines, and make real-time decisions to keep everything on track. The production manager is intimately familiar with the production process, knows when to adjust schedules, and reacts quickly to any disruptions. This role exemplifies what an MES does in a manufacturing environment.

While ERP provides a high-level overview of production schedules and resources, MES is concerned with execution ensuring that production is executed as planned. MES is deeply integrated with the physical aspects of manufacturing, enabling it to manage the nuances of the production process that ERP systems cannot.

Differences Between MES and ERP

Aspect	ERP  (Enterprise Resource Planning)	MES  (Manufacturing Execution System)
Scope and Focus	Covers a wide range of business functions across the entire enterprise. Designed for strategic planning and resource management across departments.	Specifically focused on the production floor, with deeper engagement in executing production processes, equipment monitoring, and labor management.
Data and Time Frame	Deals with high-level, aggregated data, often historical or forecast-based and financially biased. Works on a broader timeframe for long-term planning and decision-making.	Operates in real-time, handling detailed, granular data from the shop floor, responding immediately to production needs.
Integration and Flexibility	Integrates various business functions but often lacks the flexibility needed for real-time adjustments on the production floor.	Highly flexible and adaptable to the dynamic environment of manufacturing. Integrates with machinery, sensors, and other shop floor systems.
Decision-Making	Supports strategic, long-term decision-making at the corporate level, focusing on overall financial business performance and resource allocation.	Highly flexible and adaptable to the dynamic environment of manufacturing. Integrates with machinery, sensors, and other shop floor systems.

Blurring the Lines: How Digital Technologies Are Redefining MES and ERP

As digital transformation continues to reshape manufacturing, the era of traditional monolithic MES may be coming to an end. New technologies and platforms are presenting a different way to solve the shop floor management coordination challenge. Based on the foundations of MES these new solution incorporate advanced digital technologies such as No-Code, IIoT (Industrial Internet of Things), machine learning, AI-driven analytics, Generative AI and enhanced user interfaces. They offer a more holistic view of manufacturing operations, providing real-time insights that empower workers on the shop floor to make data-driven decisions.

Unlike traditional monolithic MES, which focused solely on production execution, the new breed of technologies leading with the Frontline Operations Platforms encompass a broader range of activities, including quality control, maintenance, lab operations, inventory management, and workforce training. This transformation is a direct response to the growing need for systems that not only manage production but also integrate seamlessly with other digital tools and platforms to enhance overall operational efficiency. It also aligns with the broader digital paradigm, where the goal is not just to automate existing processes but to create a more connected, intelligent, and responsive manufacturing environment. The integration capabilities of Frontline Operations Platforms enable a seamless flow of information between the shop floor and the enterprise level, blurring the traditional lines between MES and ERP.

Several key trends are driving this convergence:

1. IIoT and Real-Time Data Integration:

The proliferation of IIoT devices on the shop floor allows for the real-time collection and analysis of data. This data can be fed into both MES and ERP systems, enabling more informed decision-making across all levels of the organization. For instance, real-time production data captured by IIoT sensors can be used by the ERP system to adjust supply chain logistics or by the MES to optimize production schedules on the fly.

2. Advanced Analytics and AI:

Machine learning and AI are increasingly being used to analyze the vast amounts of data generated by manufacturing processes. These technologies enable predictive maintenance, demand forecasting, and process optimization, functions that traditionally belonged to either MES or ERP. The use of advanced analytics allows these systems to overlap, as both can now contribute to strategic and operational decision-making.

3. Human Centric Platforms:

The new no-code platforms take a human centric approach that break down the traditional process centric solution. They allow to build solution that can be used across manufacturing modalities and also allow to combine MES and ERP functionalities blurring the lines between the two. The new solutions provide a democratized platform for managing all operational process. Workers on the shop floor, managers, and executives can all access the same platform, though with different levels of detail and control, depending on their role.

4. Cloud Computing and Edge Computing:

The shift towards cloud-based solutions and edge computing is enabling greater integration and scalability of MES and ERP systems. Cloud computing allows for centralized data management, making it easier to integrate MES and ERP data. Edge computing, on the other hand, brings computational power closer to the production site, enabling real-time data processing and decision-making that benefits both MES and ERP functions.

5. Interoperability and Open Standards:

Increasingly, manufacturers are adopting interoperable systems that can communicate with each other through open standards. This trend is making it easier to integrate MES and ERP systems, allowing for a more seamless exchange of data and better collaboration between different departments.

The Future: A Converged System for Manufacturing Excellence

The convergence of MES and ERP functionalities into more integrated platforms represents the future of manufacturing. As these systems continue to evolve, they will offer manufacturers the ability to manage both high-level strategic planning and detailed operational execution through a single, cohesive platform. This convergence will enable a more agile and responsive manufacturing process, better equipped to meet the demands of the modern market. The systems are working together more closely than ever, driven by advancements in digital technology that empower manufacturers to achieve new levels of efficiency, flexibility, and innovation.

In Summary...

In the rapidly changing landscape of manufacturing, understanding the distinct yet increasingly interconnected roles of ERP and MES systems is crucial. As digital technologies continue to advance, these systems are evolving and converging, offering manufacturers a powerful toolset for driving operational excellence. The transformation of MES into Frontline Operations Platforms exemplifies this convergence, blurring the lines between strategic planning and operational execution. By embracing these integrated platforms, manufacturers can unlock new opportunities for efficiency, agility, and competitiveness, setting the stage for a new era of manufacturing excellence in the digital age.

Yet, it is naive and risky to assume that one of these systems can be extended to effectively do the other’s job. Similarly, one would not assign an accountant to be a production manager, or vice versa. Each might be an expert in his own field, yet it takes a completely different set of skills, expertise and knowledge to effectively tackle each task.