Democratization Is Not Anarchy. Learn to Deal with IT!

In nearly every conversation I have with manufacturing executives about their digital transformation programs, the topic of democratization surfaces the same way. As a problem. "How do we make sure people aren't building things outside of our control?", "How do we enforce compliance with IT governance?" "How do we prevent shadow IT from getting into production?" "What's our policy on AI-generated code?" The conversation turns to containment before it ever reaches potential. And in my experience, that is exactly the wrong place to start.

I've written about democratization as one of the 5 Pillars of Composability for years. It generates more internal friction than any other pillar — not because it is the most dangerous, but because it is the most misunderstood. Organizations treat it like a problem to be controlled. What they should be treating it like is the engine that has been quietly driving their best productivity gains for the past three decades — whether they recognized it or not.

The evidence has been right in front of us the entire time. We just keep refusing to see it.

The Proof Has Been Hiding in Plain Sight

Look at Microsoft Excel, there is no piece of enterprise technology that has driven more distributed manufacturing productivity gains than a spreadsheet application that anyone can use. Long before no-code platforms existed, Excel was what people reached for when the digital solution they needed simply didn't exist — or when the monolithic system in place couldn't provide it. From simple hour-by-hour production trackers to complex inventory management models, engineers and operators built what they needed with what they had. The gap was always there, because ERP, MES, WMS, QMS, EAM — every monolithic system of record was designed for structured, standardized workflows. The contextual, the ad-hoc, the highly specific operational problem always fell through the cracks. Excel caught them. It democratized data manipulation — it placed analytical and tracking capability in the hands of every engineer, quality manager, and production planner on the floor. IT organizations hated it. Shadow IT. Ungoverned. Risky. And yet — it worked. The productivity it unlocked was enormous, precisely because the people building the tools were the people who understood the problems.

The same story played out with Human-Machine Interfaces on the plant floor. When SCADA and DCS vendors began providing configuration environments that operators and process engineers could use directly, the pace of process improvement accelerated. The automation engineers closest to the process could suddenly instrument, visualize, and adjust without waiting months for a programmer to interpret their requirements, write a specification, queue the work, and deploy a change. Democratization of configuration capability drove productivity. Again.

The pattern is consistent: 

When you give operationally knowledgeable people the tools to solve their own problems, the rate at which problems get solved increases dramatically. This is not a hypothesis — it is a thirty-year track record.

This is precisely the type of democratization that no-code and low-code platforms have extended into frontline operations over the past decade. The shift from "IT builds, operations uses" to "operations builds what they need" compresses the lag between identifying a problem and solving it from weeks to hours. As I've described through the lens of digital maturity, organizations that make this shift don't just get more efficient — they develop a fundamentally different operational capability. They become adaptive

AI Is Closing the Last Gap — and That Changes Everything

No-code lowered the barrier to building digital solutions, but it still required learning a new environment, a new paradigm, a new way of thinking about logic and workflow. AI is closing that gap entirely. You can now describe what you need in plain language and have a working solution generated in front of you. The barrier to content creation for is approaching zero.

Think about what that means for the examples we just discussed. The engineer who used to spend days building a complex spreadsheet model to track WIP and yield can now describe the logic conversationally and have the model built for them. The process engineer who needed a SCADA supplier's configuration team to update an HMI display can now specify the change in plain language and iterate in real time. AI isn't just another wave of democratization, it super charges it. It skips no-code as the primary mechanism by which non-programmers create digital solutions.

And here is the implication that most organizations are missing: as the barrier to creation approaches zero, the value of the platform it runs on increases dramatically. Anyone can generate a solution. Not everyone is generating solutions that are version-controlled, validated, connected to the right data sources, maintainable by someone other than the person who built them, and operating within a compliant environment. The purpose-built platform — composable, human-centric, compliance-ready — becomes more critical, not less, as AI democratizes creation. Ungoverned AI generation without a platform foundation is not democratization. It is the digital equivalent of everyone writing their own procedures on sticky notes.

As I described in A Composable Agentic Framework for Frontline Operations, the emergence of builder agents — AI that helps domain experts design and iterate digital solutions in real time — is the realization of this shift. The question it raises for every manufacturing organization is not "how do we control this?" It is: do we have the platform foundation to make what our teams are about to build actually work?

And the organizational response? The same one we've seen before. Fear. The containment reflex. "What's our policy on AI-generated code in production?" "How do we prevent people from deploying things that haven't been validated?" "Who is accountable when something built with AI goes wrong?"

I am not saying those are wrong questions. I am saying they are being asked before the far more important one: what becomes possible when your process engineers, quality specialists, and production leads can build and iterate the tools they need in hours rather than weeks? That is the question that unlocks value. Governance comes second — as the framework that makes value creation sustainable — not as a replacement for asking whether value is even being pursued.

In both previous waves of democratization, the organizations that responded with blanket restriction fell behind the ones that built governance structures capable of channeling the new capability. As I've been observing as the industry traverses the digital divide, the companies pulling ahead are not the most cautious — they are the ones with a sound strategy and culture that enables effective governance.

Why Democratization Gets Managed Instead of Harnessed

The answer is structural, and it runs deep. Most manufacturing organizations still operate with a mental model inherited from the era of monolithic systems — where digital capability was scarce, expensive, and necessarily centralized. In that model, IT was the gatekeeper because it had to be. Building anything digital required specialized skills, expensive licenses, and careful change management. The architecture was fragile. A mistake in one place could propagate across the whole system. In that context, tight central control was not a choice — it was a necessity.

The decline of monolithic architectures didn't just change the technology. It changed the risk profile. Composable platforms are designed for distributed development — with version control, role-based permissions, validated templates, and isolated workspaces that contain failure to a single application or station. But organizational culture moves slower than technology. The gatekeeping mindset persists long after the scarcity that justified it has disappeared.

So we still see organizations applying the change governance frameworks designed for monolithic systems deployments to no-code app development. We see IT review boards that were built to manage quarterly release cycles now being used to evaluate whether a process engineer can add a field to a workstation app. The tools changed. The governance didn't. And the result is that organizations spend more energy suppressing the creative capacity of their most operationally knowledgeable people than they do enabling it.

There is also a subtler dynamic that I've observed consistently. Organizational skepticism tends to attach itself to democratization specifically because its outputs are distributed and visible — not because they are more dangerous than centralized systems. A poorly architected process buried inside a monolithic MES can affect the entire operation and take months to unwind. A poorly designed app built by a process engineer affects a single workstation and can be corrected in an afternoon. The distributed failure mode is actually less catastrophic. But it's more visible, and visibility triggers the control reflex — even when the underlying risk doesn't warrant it.

Democratization Is Not Anarchy. It Requires Democratic Governance.

Here is the point I want to make directly: democratization is not the absence of rules. It is the distribution of capability within a system of rules.

We have a model for this. It is called democracy. Functioning democratic systems are not anarchies — they are the most sophisticated governance structures humans have built. They have constitutions, laws, institutions, independent accountability mechanisms, and ethical norms. They distribute power not because they have abandoned governance, but because they have built governance structures capable of handling distributed power. The result — when it functions — is a more resilient, adaptive, and innovative system than any centralized alternative has ever achieved.

The comparison to manufacturing governance is direct. What I have consistently called controlled democratization means giving people the capability to solve their own problems within a governance framework designed to channel that creativity productively — not police it into submission. Policing is a dictatorial method. It is also what traditional top-down, monolithic systems use. And it is precisely why those systems generate compliance without generating adaptability.

The most well-governed democracies do not work because they have the most police. They work because they have strong institutions, a culture that understands and values the rules, and mechanisms for accountability when those rules are violated. Manufacturing organizations that want to harness democratization need to build the equivalent: platforms that enforce governance by design rather than by gatekeeping, centers of excellence that guide rather than approve, and a culture that treats accountability and empowerment as the same thing — not opposites. The power of that combination, when you've seen it operating at scale, is not incremental. It is categorical.

The Question Is Not Whether. It's How.

Your teams will use AI to build things. The only variable is whether they do it inside your governance framework or around it. Blanket restrictions don't prevent use — they push it underground, where it operates without documentation, without platform guardrails, and without accountability. That is the actual risk. And it is a risk created entirely by the policing reflex.

Every previous wave of democratization taught the same lesson. The organizations that tried to stop it accumulated missed improvements, unsolved problems, and eventually lost the people who were motivated enough to try. The organizations that built governance structures to channel it gained ground that compounded over time.

Continuous transformation is only achievable if democratization is operating at full potential. You cannot get there by treating your most operationally knowledgeable people as risks to be managed. The difference between democratization and anarchy has never been the absence of rules — it has always been the quality of governance.

Build governance worthy of the capability your teams are ready to use. Don't be the last to start.

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!

Agentic AI in Action: What I Learned Experimenting with Operational and Builder Agents

Over the last several months, I’ve been deepening my exploration of Agentic AI within Tulip, applying the concepts I laid out in my Agentic Framework and testing them in real operational scenarios. What started as curiosity has quickly become something else entirely: a recognition that we are opening a fundamentally new chapter in how manufacturing systems are built, operated, and scaled.

As I’ve experimented with both operational agents—those that support frontline teams in real time—and builder agents—those that help design, generate, and improve digital solutions—I am realizing how deep and wide the impact is going to be. The more I explore, the more use cases reveal themselves, and the more explosive the potential becomes. Its much more than I initially thought, and I have been thinking in terms of multi-agent systems for manufacturing since the 90's! Agentic AI (multi-agent systems powered by generative AI) are a much bigger step change than I would have imagined to how we think about creating and running manufacturing solutions.

Let's start with a brief recap. Operational agents extend the capability of the production system realizing digital twin capabilities in ways that introduce reasoning, interpretation, and contextual understanding directly into the work being done on the floor. Builder agents open the door to a multithreaded, parallel engineering process that fundamentally changes the speed and depth at which solutions can be created. It feels less like a “copilot” assisting a developer and more like a coordinated team of SMEs designing solutions - Augmented Lean at hyperspeed!

This combination—augmenting frontline execution while accelerating the design and iteration of digital systems—points to a future where humans orchestrate agent ecosystems rather than manually building every piece of a solution themselves. This brings me to the motivation for writing this post that became clear to me in a recent customer conversation about DCS integration in support of a digital solution for pharmaceutical manufacturing of clinical drugs.

Reimagining Composable Integration with DCS and ISA-88 Through Agentic AI

The question I was asked recently wasn’t the classic “How do you integrate an MES with a DCS?”—that problem has been addressed in many different ways in the traditional architectures. The real question was far more interesting: How do you integrate a Composable MES built on a Frontline Operations Platform with a DCS or other ISA-88 based batch system?

In a traditional MES world this integration immediately triggers a familiar debate about how to partition the recipe across systems, define boundaries of responsibility, and reconcile master data, recipe models and equipment hierarchies. And that debate is almost always constrained—if not outright dominated—by the rigidity of monolithic MES platforms. The architecture drives the discussion more than the operational needs do.

But in a composable environment, the constraints that shaped those historical debates simply don’t apply. Let's look at what happens when you apply a composable, agentic model.

1. Composable Apps Remove the Traditional Constraints

In a composable architecture, apps are not bound to a predetermined master data model or recipe structure. This means that there is no need for recipe model partitioning, no need to replicate equipment hierarchies, no predefined S88 recipe model to map into. 

This flexibility removes the most painful barrier in traditional MES ↔ DCS integration: the structural reconciliation of recipes and equipment models. The DCS can continue using its ISA-88 representations. Tulip apps can represent the process in the most intuitive and useful way. And the integration simply becomes the mapping of meaning and intent between the two worlds. You design the representation of the process that makes sense for your operation—not the one dictated by the systems.

Composable solutions also shift the perspective entirely by taking a human-centric, activity-based approach organized around the physical reality of the shop floor. I fully recognize that, in the traditional monolithic MES world, standard models like ISA-88 were considered essential—they provided structure, discipline, and a shared language for process-centric systems. But composability represents a fundamentally new paradigm. 

To democratize operational systems and bring them closer to frontline work, we must prioritize operator-first design rather than forcing every SME to become a master of S88 modeling. ISA-88 remains invaluable for process control, but the surrounding operational systems must be simplified and democratized so they can work hand in hand with the distributed nature of modern manufacturing. Composable platforms do exactly that: they allow process engineers, chemical engineers, and frontline teams to collaborate without being constrained by rigid, expert-only models.

This alone would dramatically simplify integration. But the real breakthrough comes with agents.

2. Builder Agents Enable Multithreaded, Generative Solutioning

Builder agents transform integration work from a linear, manual design activity into a parallel, iterative, and generative process. They don’t just help you “build faster”—they fundamentally change how solutions are conceived and engineered.

I experimented with builder agents that can ingest a full ISA-88 recipe structure and conduct deep introspection on it: understanding the procedural models, identifying phase logic, parsing parameter definitions, and extracting the relationships between equipment, units, and operations. It then suggested mappings, app contexts, and design patterns—not only based on expert interpretation of the ISA-88 standard, but also from what they’ve learned across existing apps, historical integrations, real-world performance of similar solutions and critically expert knowledge of composable design principles. In other words, these agent combines domain expertise with empirical insight, offering design options that reflect both best practices and operational realities.

This alone already feels like having a team of process engineers and MES architects working in hyperspeed. But the true power emerges when operational agents begin contributing dynamic intelligence into that design loop.

Operational agents provide real-time feedback about process variability, material availability, logistics implications, quality status, or unexpected delays. They can accommodate non-optimal or evolving recipes by dynamically dispatching materials, reallocating resources, or bringing the right expertise into the process at the right time. This dramatically increases operational resilience and reduces risk—because the system adapts rather than stalls when confronted with real-world complexity.

And then there’s compliance...

Specialized builder agents trained on GxP principles can support on-the-fly risk assessments, propose mitigation strategies, and generate validation documentation as part of the design cycle. Operational validation agents can take this further, enabling true continuous validation—monitoring execution conditions, evaluating deviations against risk models, and providing traceable explanations for decisions. Compliance becomes embedded, in fact native, in the system rather than layered on top.

When you step back and think about the implications, the potential is almost infinite. The combination of builder and operational agents elevates agility and compliance to levels we’ve never imagined in traditional MES architectures and design approaches. It enables systems that are not only faster to build, but continuously improving, self-aware, and aligned with both operational needs and regulatory expectations.

This is the beginning of a new era in how manufacturing solutions are designed, executed, and validated. It feels like a generative design process running at hyperspeed. Not a single assistant helping you code tasks faster — but a team of AI experts collaborating to create a complete solution.

And this unlocks something we have never had before in manufacturing software: the ability to rapidly iterate and explore multiple viable integration architectures before committing to one. This is enormously valuable in an ISA-88 context, where recipes, equipment logic, and operational variability rarely align perfectly.

Seeing the Explosion of Use Cases

If you let the builder and operational agents begin to work together, the number of possibilities just explodes - its the first step towards a Multi-Agent System (MAS). These agents don’t simply execute tasks—they learn, reason, and collaborate in ways that constantly reinforce and expand what’s possible. Suddenly, problems that used to take months of engineering effort can be tackled in days—or even hours.

Some of the notable and exciting use cases I’ve come across include:

• Automatically mapping process logic into app structures.
• Rapidly generating compliant workflows for regulated environments.
• Exploring recipe variants and operational scenarios through simulation.
• Using agents to assist in validation and documentation.
• Dynamically interpreting and adapting recipes at runtime.
• Applying cross‑system reasoning to catch inconsistencies early.
• Coordinating multiple agents to design complete production solutions.

Each one opens a new door—where imagination, not technical limitation, becomes the real constraint.

What stands out to me most is the sheer power of these systems and what they make possible. Seeing a builder agent reason through an ISA-88 recipe, or an operational agent adapt to a real-time process disruption, feels less like traditional programming and more like working alongside a tireless, highly capable collaborator. My role has shifted from hands-on integration to guiding and steering intelligent agents—and that shift fundamentally changes how we think about manufacturing systems. The emerging dialogue between human expertise and machine reasoning opens up an entirely new design space, one where adaptability, resilience, and scale are no longer constrained by human bandwidth.

I’m also starting to document and share some of these experiences through AI‑generated videos, another capability I’m learning to use. They’ve turned out to be a surprisingly powerful way to show what agentic systems can do—and to help others visualize these new forms of collaboration on the shop floor. It’s a learning journey in itself, but it feels like the right extension of this exploration: using AI not only to build better systems but to communicate and learn in entirely new ways.

Seeing all of this unfold up close, it’s clear we’re not just evolving automation—we’re watching Holonic concepts come alive, the manifestation of the new digital manufacturing reality.

Crossing the Digital Divide: Human-Centric Manufacturing in a Multi-Agent World

When you step back and look at what emerges from the combination of builder agents and operational agents, it becomes clear that this is not just another productivity boost or architectural evolution. It is a convergence point — one that aligns remarkably well with how manufacturing operations have always been run by humans.

Manufacturing has never been a purely deterministic, rules-based environment. It is adaptive, situational, and deeply human. Engineers design intent. Operators respond to reality. Supervisors balance constraints. Quality professionals manage risk. For decades, our digital systems have struggled to reflect this reality, forcing people to adapt to rigid models and monolithic workflows rather than supporting the way work actually happens.

Multi-agent systems change that equation by enabling digital systems to finally reflect the way manufacturing actually operates—through parallel problem solving, continuous adaptation, and coordinated decision-making across people, processes, and technology.

Builder agents mirror how engineering teams work: exploring options in parallel, iterating designs, learning from past outcomes, and continuously refining solutions. Operational agents mirror how plants operate: responding to variability, adjusting to constraints, coordinating people and materials, and managing risk in real time. Together, they form a digital system that finally behaves the way manufacturing organizations behave — collaborative, contextual, and resilient.

 

This is profoundly human-centric, because it aligns digital systems with how manufacturing teams actually operate — dynamically, collaboratively, and contextually.

It also brings into sharp focus a theme I’ve been writing and speaking about since the late1990s. For decades, we have tried to digitize manufacturing by automating tasks, enforcing standard models, and embedding rigid logic into systems. That approach delivered value, but it also created the very constraints that now limit agility, scalability, and innovation.

What we are seeing now is the realization of a different paradigm — one where digital systems augment human reasoning instead of replacing it, where composability replaces monoliths, and where intelligence is distributed across agents rather than centralized in static applications. This is the paradigm shift I’ve been pointing to for years, and it is finally reaching a practical, scalable form.

The convergence of composable platforms, agentic AI, and multi-agent collaboration marks a true inflection point. We are no longer just modernizing legacy systems. We are crossing the digital divide — moving from systems that support transactions to systems that participate in operations.

The potential here is vast! Agility, resilience, productivity, and compliance are no longer trade-offs. They become reinforcing outcomes of a system designed around human workflows, continuously learning agents, and real-world context.

This is not the end state — it’s the beginning. But for the first time, the tools, platforms, and paradigms are aligned. And that alignment is what makes this moment different from other transformative eras that came before. And it will not stop - that is why we call it continuous transformation!