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! 

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?

Don't Be the Last Dinosaur: Your Plant's Digital Evolution Starts Now!

Plant Managers, manufacturing leaders - look around you. The manufacturing world is not just changing; it's undergoing a seismic shift. While you're grappling with daily firefighting, your competitors – the agile, the digitally native, and those strategically investing – are busy building tomorrow's factories. They are not compromising; they are not settling for just automation, they are digitizing every process, they have rich digital data, they are ready to deploy intelligent, agentic systems that learn, adapt, and optimize production with unprecedented speed and accuracy.

The choice before you is stark: Innovate and adopt, or become obsolete.

If you continue to manage your plants with fragmented data, manual processes, and reactive decision-making, you are literally leaving money on the table. You're bleeding efficiency, compromising quality, and surrendering market share. The 'traditional' way of manufacturing is becoming a relic.

Here are the critical decisions and actions you must undertake RIGHT NOW to avoid being left in the dust:

1. Stop analyzing and planning- Start digitizing today: Your operations are awash in data, but is it usable? Is it real-time? Is it connected? The foundation of every advanced manufacturing system—from IIoT to agentic AI—is clean, contextualized data. 

ACTION: Invest immediately in robust digital data infrastructure. This means connecting your machines, sensors, and systems (OT and IT convergence is non-negotiable). If your data is trapped in silos, you are blind, and your plant will suffocate under its own inefficiency. This is simply critical - not having digital data is a waste, like other wastes in manufacturing!

2. Embrace the Power of Composability: Shift your thinking beyond overarching automation or top-down systems to digitize plant processes. The "lights-out factory" is not only outdated, it never truly materialized. Digital transformation isn't about replacing humans but augmenting their capabilities. Composability focuses on empowering your workforce, leading to significant productivity gains. Deploy pilot projects where AI agents can take over mundane, repetitive, or complex analytical tasks. Think predictive maintenance that schedules itself, quality control that detects micro-deviations before they become defects or failures, and production dispatching that dynamically adjusts to supply chain disruptions. 

ACTION: Identify one critical bottleneck in your plant that could be addressed by a digital solution - yes “Kaizen”, start bottom up and iterate. Adopt technology, find a partner and just start. There are some impactful digital technology platforms (I recommend starting with Tulip of course) and launch a focused pilot. Show your team and leadership the tangible benefits.

3. Future-Proof Your Workforce – Invest in Upskilling, Not Just Training: Your people are your greatest asset. They need to evolve from procedure followers to orchestrators, data interpreters, and system managers. This isn't just about technical skills; it's about fostering a culture of continuous learning and adaptability. And with democratization that digital technologies offer this quickly can create a ground swell, a movement within your organization. 

ACTION: Develop a strategic plan for workforce transformation. Enhance your operational excellence with augmented lean principles and identify critical future roles and necessary skill sets. Empower your team to embrace these new technologies and strike down any skepticism or technology fears.

4. Demand Agility from Your Systems – Ditch the Rigidity: The era of monolithic, inflexible manufacturing systems is over. Your plant needs to be agile, able to pivot production lines, incorporate new products, and respond to market shifts with unprecedented speed. This means moving towards composable, interoperable platforms that can readily empower your operations with the new digital capabilities including critically AI. 

ACTION: When evaluating new solutions (like MES, LIMS, CMMS or other systems), prioritize open composable architectures with cloud-native capabilities, IIoT platforms and and in herent AI capabilities that are not an afterthought or “bolt on”. Demand systems that are built for change, not for static operations. These systems have to enable you to transform, they have to provide you a way to implement according to the 5 pillars of composability. Make agile, emergent control a reality with agentic AI.

5. For Regulated Industries: Make GxP a Competitive Advantage, Not a Burden: If you're in Pharma, Biotech, or Med Device, the GxP implications of these technologies are paramount. But don't let compliance be an excuse for stagnation. Modern digital validation approaches and Pharma 4.0 guidances mean you can innovate with compliance. Transformative digital platforms have inherent built in compliance through detailed digital data, audit trails, and transparency to control mechanisms. Democratization means technology is simpler to understand with that comes transparency and self documentation.

ACTION: Engage with experts who understand both cutting-edge digital transformation and the nuances of GxP. Be proactive in defining your digital validation strategies for IIoT and AI, leveraging initiatives like Validation 4.0. This ensures your innovation is robust, secure, and compliant.

The clock is ticking. The question isn't if your plant will undergo this digital transformation, but when and who will lead it. If you hesitate, you risk becoming a case study in industrial obsolescence. Seize this moment, or watch your competition leave you in their digital dust. This is the mindset you need to adopt to thrive in this new era. What's your immediate next step?

How Holonic Dreams are Becoming Manufacturing Realities

Let me tell you, there are few things more thrilling than seeing a concept you poured your heart into decades ago slowly coming to life in ways you barely dared to dream. For me, that feeling hits hard with the incredible capabilities of AI in general, and specifically, Generative AI . These aren't just buzzwords; they're fundamentally reshaping how we think about digital manufacturing (or whatever the latest term is). While Generative AI is phenomenal for creating content, designing new products, or even simulating complex processes, its true power in manufacturing often lies in its ability to empower something even more profound - Multi-Agent systems. This is the realization of Holonic concepts in a composable manner to enable agile manufacturing with Agentic AI .

When I see these advancements I am just mesmerized and my mind goes back to the 1990s. That’s when my journey into this future really began, deeply immersed in the world of Holonic Manufacturing Systems (HMS) and the emerging field of multi-agent systems.

Back then, I was a young researcher PhD working a methodology and architecture for Holonic manufacturing systems in collaboration with other like minded researchers as part of global consortium. It wasn’t just an academic exercise; it was a burning ambition to make manufacturing truly agile and resilient. I envisioned a factory floor that wasn't a rigid, top-down hierarchy, but a vibrant, decentralized network of intelligent, collaborative entities – what we called "holons."

The Holonic Vision: A Glimpse into the Future I Believed In

Imagine a shop floor where every machine, every production cell, every product, wasn't just a passive component but an intelligent "holon" – a self-contained, self-regulating unit. They would have their own smarts, making decisions, talking to each other, and collectively adapting to whatever curveball the market threw at them. Koestler's concept of a "holon" – simultaneously a whole and a part – perfectly captured this idea of distributed intelligence.

The benefits? Oh, they were clear and seemed a world away but yet an eerily anticipatory need of the current political and economic circumstances.

• Agility beyond belief : Reconfiguring production lines in a flash, launching new products on a dime, responding to customer demands with unprecedented speed.
• Built-in resilience : If one holon stumbled, the others would dynamically pick up the slack, re-routing operations to keep things flowing. Downtime issue would be a distant memory.
• Seamless scalability : Adding new machines or processes would be like plugging in a new module, effortlessly integrating into the intelligent network.
• Optimization from within : Local decisions by these smart holons would ripple up to optimize the entire system, far surpassing anything a central, rigid control system could ever hope to achieve.

This wasn't just theory; it was a blueprint for a manufacturing revolution.

We are Still Waiting for a Digital Manufacturing's Breakthrough

The holonic concept was the perfect architectural dream, but the engine to power it, multi-agent systems, was still in its infancy. My research in the 90's focused on how to design these agent systems, how to give them that holonic spirit, but the reality was that our ambition ran ahead of the available technology. We were hitting walls. The computational power needed to run complex agent logic on shop-floor controllers was simply astronomical for the time. Getting a multitude of agents to communicate reliably and securely across a factory? That was a networking nightmare. And then the AI capabilities were nascent, a whisper of potential, compared to the what we wield in our hands today."

Those were exhilarating times for pure research, pushing the theoretical limits of what manufacturing could be. But bringing it to large-scale industrial reality? That was a bridge too far. Until now ...

Today's Reality: AI and Digital Infrastructure can Unleashing the Holonic Dream

Fast forward to today, and the technological landscape has dramatically evolved. The convergence of several critical advancements has not only rendered the holonic vision achievable but has propelled it into operational realms previously unimaginable:

1. Advanced AI and Machine Learning Capabilities : Modern Artificial Intelligence and Machine Learning algorithms now provide the sophisticated analytical and cognitive capabilities for individual software agents. These algorithms enable agents to learn from large-scale industrial datasets, execute robust predictive analytics with high precision, and achieve adaptive process optimization in real-time. This represents a fundamental shift from deterministic, rule-based systems to dynamic, self-optimizing intelligence.
2. The Industrial Internet of Things (IIoT) as the Network of Agents, Powered by Agentic AI : This is where the core holonic vision finds its full realization. The Industrial Internet of Things (IIoT) is not merely a collection of connected devices; it forms the very network of agents. The IIoT nodes themselves are designed as autonomous, intelligent agents . Each smart device, each sensor, each piece of equipment can be made to act as a data acquisition point and, crucially, as a decentralized intelligent entity. The emergence of Agentic AI imbues these IIoT nodes with advanced capabilities for complex task decomposition, strategic planning, execution, and critical self-reflection. This synergy of IIoT as the foundational agent network and Agentic AI providing the cognitive layer directly mirrors the autonomous and cooperative principles fundamental to our original holonic concepts, enabling seamless interoperable collaboration across the manufacturing ecosystem. 

Futuristic Use Cases Becoming Reality: Empowering the Digital Twin for Tomorrow's Factory

For all these visions to truly become reality, we needed more than just powerful tech; we needed a concept that can effectively by applied to the realities of a physical operation. Here we can use a Digital Twin approach to represent the reality of the operation and build a foundational model with a set of constructs that aligned perfectly with the holonic principles I’d spent years researching.

This is also where Agentic AI enters the stage, becoming the intelligence that breathes life into this digital replica. My research from the 90s proposed a consistent model for these foundational constructs, embodying them as intelligent Product, Order, and Resource agents (or holons). This was the blueprint for how the real-world manufacturing elements could become autonomous, cooperative entities within the digital twin.

Let's step into this future for a moment, and I’ll paint a picture of what a manufacturing operation truly looks like when its digital twin is powered by these agentic principles, or in other words a next generation paradigm shifting composable manufacturing system . In this world, a work order isn't just data on a screen; it instantly awakens an intelligent Order Agent within the digital twin - or plainly the manufacturing system. This agent immediately gets to work, dynamically negotiating with Resource Agents —the digital representations of machines, tooling, even the specific human expertise required—to secure optimal production slots and materials in the virtual space, which then collaboratively drives actions in the physical factory assisting operators in orchestrating the operation.

As raw materials enter the facility, each component, or even the nascent product itself, manifests as a Product Agent within this digital twin. This agent carries its own unique digital identity and manufacturing instructions, literally guiding operators to route its counterpart through the physical production line. It's constantly communicating its status and needs within the digital twin, ensuring it receives the precise processing at each stage in the real world. If a specific machine (a Resource Agent ) suddenly reports a slight anomaly – say, a bearing starting to warm up – its built-in intelligence within the digital twin instantly flags it. Instead of waiting for a catastrophic failure, the line's collective intelligence, orchestrated by operators using the various software agents operating within this digital twin environment, might subtly re-route the physical Product Agent to an alternative, readily available machine. Or, better yet, the affected Resource Agent might even initiate a precise, self-healing routine or schedule a just-in-time, predictive maintenance intervention, ensuring that the issue is resolved before it impacts production, all while the Order Agent helps ensure deadlines are still met.

Quality control isn't a post-production check; it's baked into every micro-decision. Product Agents and Resource Agents , leveraging their digital twin data, are continuously monitoring parameters, spotting the tiniest deviation, and triggering immediate corrective actions to ensure "right first time". This seamless, autonomous orchestration – where products find their way, orders fulfill themselves, and machines manage their own well-being, all empowered through the precise, real-time fidelity of their digital twins – transforms the factory into a living, breathing, self-optimizing organism. It’s a level of agility, efficiency, and resilience that felt like pure science fiction in the 90s, but is now can become our tangible reality.

This leads us to an operational reality where the use cases that may seem futuristic are in fact possible , for example:

• Self-Optimizing Production Lines : Imagine entire lines monitoring themselves, sniffing out bottlenecks, predicting breakdowns, and then autonomously re-routing production or tweaking parameters to keep output optimal. Empowering human operators with currently unimaginable support in orchestrating operations.
• Dynamic Resource Allocation : Agents negotiating for machines, tools, and materials in real-time, ensuring every asset is utilized perfectly, eliminating idle time. Elevating scheduling and dispatching to unheard of levels of effectiveness and accuracy. 
• Predictive Maintenance and Self-Healing Systems : No more waiting for a breakdown. Agents predict failures with incredible accuracy and can even kick off self-repair routines or proactive maintenance, slashing downtime and costs.
• Enhanced Quality Control : Agents tirelessly monitoring processes and product quality, spotting deviations instantly and triggering immediate corrective actions. This is "right first time" manufacturing, every time.
• Boosted Compliance : Automated data collection, precise procedure execution, immutable digital records – agentic systems dramatically reduce human error and guarantee adherence to the toughest regulations.
• Unparalleled Traceability : Every single action, every decision by an agent, meticulously recorded. Audit trails become pristine, investigations swift and clear.
• Driving "Right First Time" : By minimizing variability and providing real-time feedback, these systems help ensure products meet quality specs from the outset, slashing costly rework.
• Accelerated Innovation : With more efficient, reliable processes, companies can pour more resources into R&D, bringing life-saving drugs or mission-critical components to market faster.

The journey from the elegant theories of holonic manufacturing systems to the practical, jaw-dropping capabilities of Agentic AI has been long, but intensely rewarding. What started as pure academic curiosity, exploring the power of decentralized, intelligent control, has now become the very bedrock of digital transformation in manufacturing. We’re no longer just imagining; we are actively building factories that learn, adapt, and optimize themselves , powered by the incredible, collaborative intelligence of software agents, physical machines, and empowered humans. It's a testament to the enduring power of fundamental research, a strategic commitment to true digital transformation, and the relentless, accelerating pace of technological innovation.

But hold on, not so fast. Here’s where my passion often turns to frustration. We have the technology today, and more is coming fast – innovation is accelerating at an exponential rate! Yet, a fundamental problem persists in manufacturing: so many companies still don't grasp that adopting digital technology demands a profound transformation , not just a simple upgrade.

The Agility Forum , a 1990 initiative to transform manufacturing, proclaimed that we need to thrive in an environment where change is the only constant. In today's volatile business landscape – marked by unprecedented geopolitical shifts, rapid market fluctuations, and increasingly fragile global supply chains – this is not longer a theory; it's the raw truth of survival. While our research in the 90s certainly anticipated a future of greater dynamism and the critical need for manufacturing systems to adapt , even we couldn't have fully foreseen the sheer velocity and breadth of the disruptions we face now.

This intense, continuous flux means leveraging that change, embracing agility itself, as your ultimate competitive advantage. The sheer ability to adapt, to pivot swiftly, and to continuously evolve your operations is precisely what will differentiate leaders from those left behind. In this dynamic landscape, digital transformation is not a static one time event, nor is it a project with a defined end; it is a continuous process of adaptation to changing business and technological environments - hence we need to start talking about Continuous Transformation .

The core issue isn’t the lack of innovative tools; it’s the mental, organizational, and cultural shift required to truly embrace them - yes its still really about people . Real digital manufacturing means rethinking everything – your processes, your workflows, even how you do business. It’s a complete reimagining, and that's precisely why the original holonic concepts, now enabled by modern tech, offer the inspiration for a breakthrough path. Embrace this paradigm shift, or risk being outmaneuvered by those who do. The future of manufacturing is intelligent, interconnected, and increasingly autonomous, built directly on the visionary concepts laid down decades ago.

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.