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... 

IT/OT Convergence: Still Vague, Still Critical

For years, IT/OT convergence has been a recurring theme in digital transformation conversations. It’s almost become a cliché. Everyone agrees it’s important, but few can define it clearly, and every company seems to have its own “flavor.”

That vagueness is both a challenge and an opportunity. IT/OT convergence is not just about technology stacks, data pipelines, or network architectures. It is about organizations, people, and how digital capabilities become part of the fabric of operations. And in the context of continuous transformation, this conversation remains more relevant than ever.

Why IT/OT Convergence Matters in Continuous Transformation

Digital transformation is not a one-time project—it’s a continuous process of adapting, learning, and embedding new technologies into how we operate. In that context, IT/OT convergence is essential.

Why? Because transformation cannot happen in silos. The systems that plan and account (IT) and the systems that control and execute (OT) must work together seamlessly. If they remain separate—organizationally, technologically, or culturally—you end up with fragmentation that slows down transformation instead of enabling it.

Can We Define It? And Why Does That Help?

One of the reasons IT/OT convergence feels vague is because it is often reduced to a technical exercise—connecting networks, integrating databases, or sharing dashboards. But that misses the bigger picture. To make it actionable, we need a broader and more ambitious definition.

At its core, IT/OT convergence is about making IT and OT inseparable. Not aligned, not just integrated, but merged into one digital foundation for the business.

That means:

• Integration of technology across the entire operation—from planning and engineering, to execution on the shop floor, and even to customer-facing processes. IT and OT must form a continuous digital thread that spans the lifecycle of design, production, quality, logistics, and service.
  
  

• Merging organizational roles and responsibilities—so that IT and OT aren’t two camps negotiating interfaces, but one team co-owning outcomes. The boundaries blur until it becomes irrelevant whether a capability was once “IT” or “OT.”
  
  

• Embedding digital practices into operations—so technology isn’t an external tool to be “applied” to operations, but a core element of how the organization works, improves, and creates value.

One of the most dangerous misconceptions in digital transformation is treating technology as an external layer—something added on top of operations. This is what has been going on for decades, born out of necessity of dealing with super complex monolithic systems. It is a model that creates friction, and this friction is detrimental to progress - it will suffocate any digital adoption initiative.   

Defining convergence in this way is helpful because it reframes the conversation: it’s not about how to connect two separate worlds, but how to design an organization where there is only one world. That shift in mindset is what makes IT/OT convergence transformative.

For digital technology to be impactful, it must be embedded into the way work is done at all levels: from the operator on the line, to the planner in the back office, to the leadership team setting strategy. IT/OT convergence makes this embedding possible.

When data, insights, and digital tools flow seamlessly across operations, technology doesn’t feel like an “extra.” It becomes integral to how people work, decide, and improve.

The Composability Pillar of Agile Operations

Finally, IT/OT convergence is inseparable from the principle of composability. To be agile, organizations need technologies that can be composed, reconfigured, and adapted as needs change.

That means convergence cannot be separated—neither organizationally nor by use. If IT and OT are treated as distinct silos, agility suffers. But when convergence is embraced, composable technologies support operational excellence: flexible enough to adapt, strong enough to sustain, and aligned enough to deliver value across the enterprise.

How Do We Know When It’s Complete? And Does That Matter?

Here’s the truth: IT/OT convergence is never “complete.” Like continuous transformation itself, it’s an ongoing journey. Technologies evolve, organizational structures shift, and new business challenges arise.

The goal is not to check a box that says, converged. The goal is to continually deepen the integration between IT and OT so that technology becomes invisible—it simply is the way you run operations.

So whether or not it’s ever “done” is less important than whether it’s continuously evolving to support value creation.

Moving Beyond the Buzzword

So, is IT/OT convergence vague? Absolutely. But it’s vague not because the idea lacks merit—it’s vague because it was born out of conflict.

IT and OT have so far been separate domains, each with its own responsibilities, budgets, and power structures. IT managed enterprise systems, data security, and corporate standards. OT managed the machines, processes, and operational continuity. Bringing the two together is not just a technical exercise—it’s a challenge to established authority.

That’s why IT/OT convergence often feels like a “hot potato.” Nobody wants to own it fully because it requires organizations to do things that are uncomfortable:

• Merging organizations that were once distinct.

• Relinquishing power as decision-making becomes more distributed.

• Diminishing rigid responsibilities as democratized technologies empower more people to contribute to digital solutions.

At its heart, convergence means releasing control—accepting that digital technologies are no longer the sole domain of one function, but a shared capability that belongs to everyone.

And that’s hard. It’s hard for people who have built careers around defending their territory. It’s hard for organizations that have optimized themselves around silos. It’s hard because it requires a cultural transformation just as much as a technological one.

But here’s the truth: without this convergence transformation halts. You cannot achieve continuous transformation if half the organization is innovating in isolation while the other half is protecting legacy boundaries. The result is friction, fragmentation, and failure to capture the value that digital technologies promise.

This is why IT/OT convergence—however uncomfortable, however vague—remains critical. It is the cultural and organizational foundation on which digital transformation rests.

In the end, convergence is not about IT and OT learning to work better together. It’s about creating a new whole where the distinction ceases to matter. That is the mindset shift. And until organizations embrace it, “transformation” will remain more slogan than reality.

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?