We’ve all heard the stories: “Somebody created a solution in just a few hours with no-code or vibe coding…”.
It’s exciting, right? Engineers solving problems in record time, building digital tools with nothing more than intuition, creativity, and a bit of AI support. This is the promise of democratization—Agentic AI empowering everyone to innovate and improve.
And in many contexts, that speed is a superpower. But in manufacturing, the story is more complicated. Operations are inherently complex: machines, materials, people, processes, schedules, quality checks, and compliance requirements all interact in a dynamic web that is nonlinear, a complex adaptive system. In such environments, even small changes can cascade unpredictably, amplifying into disruptions far greater than their cause—an inherent feature of systems where interdependencies drive emergent outcomes.
This is where the risk lies. If an unguided agent makes the wrong decision in such an environment, things can go wrong very quickly—and often in ways that are difficult to anticipate or trace. The result might be downtime, compromised product quality, production delays and at worst safety issues. But the bigger danger is cultural: when something fails spectacularly, it doesn’t just cause operational damage—it can scare the organization away from using the technology at all.
Instead of unlocking incredible productivity, one misstep can trigger a mindset of “we don’t dare do this again…”. That’s not just a lost opportunity; it’s a setback that can stall digital transformation for years.
Digital Maturity Gaps
With the incredible pace of innovation of digital technologies and specifically Agentic AI, we have to acknowledge a hard truth: most manufacturers are still not fully ready to capitalize on this technology. The foundations are often shaky, and if we introduce solutions built by and with agents into this environment without addressing the gaps, the risks multiply.
Some of the most common issues include:
- Fragmented data that prevents end-to-end visibility.
- Disconnected IT and OT systems that block seamless collaboration.
- Cultural skepticism and lack of digital maturity that slow adoption and erode trust.
These are not new problems. I discuss them in detail in this blog. Our traditional models both in technical architecture and deployment of operational solutions for manufacturing were never designed for the agile, composable, and connected environments we’re striving to build today. In addition we can't ignore that in any given manufacturing facility there are machines and systems of varying ages and legacy status.
Consider a Scenario
An enthusiastic engineer uses vibe coding to create an agent that dispatches materials to keep machines and operators as busy as possible. At first, everything looks good, machines are running at full capacity, operators always have work, and parts move quickly into assembly.
This isn’t hypothetical. It’s the kind of risk that emerges when Agentic AI is unleashed without governance. And unlike traditional tools, agents can act autonomously and at scale, amplifying errors at a speed we may not be able to react to.
Managing Complexity with Composability: Governance, Framework & Platform
Given this reality—where digital maturity is uneven, legacy systems abound, and the dynamic nature of manufacturing operations—the arrival of Agentic AI presents both an enormous opportunity and a serious risk. Agents are coming at us fast, and their power lies in democratization and speed. But those same qualities, in an environment as sensitive as manufacturing, can amplify problems just as quickly as they solve them.
This paradigm shift cannot be left to chance. To harness Agentic AI safely and effectively, we need to approach it in an organized, managed, and focused way. That requires: governance, framework, and platform all working together and grounded in composability.
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Governance gives us the rules, guidance, and guardrails to ensure reliability, safety, quality, and compliance are never compromised. Governance is not just about control; it actively helps organizations overcome barriers like fragmented data, siloed systems, and cultural skepticism by providing structure, discipline, and confidence. Done right, it helps in the transformation of the manufacturing environment from both a technical readiness (data integrity, system integration) and an organizational readiness (culture, mindset, trust).
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Framework provides the structure to make governance actionable. In A Composable Agentic Framework for Frontline Operations, I laid out how agents can be defined by type, given clear goals, and aligned through the artefact model. The framework ensures that agents are not just scattered tools but part of a purposeful, multi-agent system that reflects and supports real operations.
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Platform is the enabling layer. An Agentic manufacturing operations platform purpose-built for frontline operations makes it possible to apply governance and framework seamlessly—across both authoring (building digital solutions) and execution (running the production line). Unlike generic vibe coding environments, such a platform is designed specifically for process engineers and operations teams. It embeds an understanding of how manufacturing works—constraints, variability, compliance, and safety—and provides tools to build solutions quickly while remaining aligned with operational best practices. Most importantly, it enables the creation and deployment of operational agents at scale that are are integral parts of the production system itself.
And underlying all of this is composability. Composability ensures that agents and solutions don’t exist in isolation, but as modular, human-centric, bottom-up elements of a system that adapts as needs evolve. Digital transformation success depends on treating composability not as an IT concept, but as an operational paradigm. In fact, composability was conceived for multi-agent systems (MAS)—and Agentic AI now makes that vision practical.
Final Thoughts
I think that we all agree that Agentic AI despite all the hype is not a fad—it’s here, its real and it’s already reshaping how work gets done. But to harness it safely and effectively, we need to recognize the two distinct scenarios where agents play a role:
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In building and engineering: where agents help create digital content, processes, and solutions. Here, governance ensures what gets built is valid, safe, and aligned with operational goals.
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In live operations: where agents support and even run production activities, interacting with machines, data, and humans in real time. Here, governance ensures reliability, compliance, and resilience in execution.
Both scenarios are powerful—they are needed but both also carry risks if unmanaged. And in manufacturing, where operations are complex and dynamic, small missteps can cascade into detrimental consequences.
That is why governance, framework, and platform are critical. Governance provides the rules and guardrails; the framework gives agents structure, goals, and alignment through the artefact model; and the platform operationalizes it all, giving process engineers and frontline teams the environment to deploy agents as integral parts of the production system, and all of this at scale.
And beneath it all is composability. As I’ve argued before, composability was conceived for multi-agent systems—modular, autonomous, and collaborative components working toward shared goals. Agentic AI now makes that vision practical on the shop floor.
The challenge ahead is not whether manufacturers will adopt Agentic AI, but whether they will do so in a way that balances speed with safety, democratization with discipline, and autonomy with alignment. Done right, it promises resilience, reliability, and the kind of productivity gains that digital transformation has always aspired to deliver.
