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!