Scaling Agentic AI Workflows in Manufacturing with Causal AI: Bernhard Kratzwald - Co Founder & CTO, EthonAI
## Episode: Building and Scaling Agentic AI Workflows in Manufacturing
**Podcast Name:** AI in Manufacturing Podcast
**Episode Title:** How to Build and Scale Agentic AI Workflows in Manufacturing
**Guest:** Bernard Kraswald, Co-Founder & CTO at Ethon AI
**Host:** Kudzai Manditereza
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## Episode Summary
This episode explores how manufacturers can build and scale agentic AI workflows to achieve operational excellence across factories. Bernard Kraswald, Co-Founder and CTO at Ethon AI, explains why traditional continuous improvement methods have reached their limits and how purpose-built industrial AI—grounded in process knowledge graphs and causal reasoning—unlocks the next wave of manufacturing optimization. Key insights include why deep data contextualization through knowledge graphs is essential for agentic AI (not just basic tag hierarchies), how causal AI differs from correlation-based analytics by making root cause findings actionable, and why a layered architecture of data infrastructure, specialized model layer, and application layer prevents hallucinated recommendations in safety-critical environments. Bernard also shares real-world results, including a globally scaled deployment at Siemens that generated over $10 million in documented savings. Whether you're evaluating industrial AI platforms or architecting your data stack for agentic workflows, this episode provides a practical roadmap from data ingestion to autonomous process control.
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## Key Questions Answered in This Episode
- What is a process knowledge graph, and why is it essential for agentic AI in manufacturing?
- How does causal AI differ from correlation-based analytics in industrial settings?
- What architecture layers are needed to run agentic AI workflows reliably in manufacturing?
- Why can't general-purpose LLMs like ChatGPT or Claude replace purpose-built industrial AI models?
- How do you build a knowledge graph iteratively without delaying ROI?
- What does a typical deployment timeline look like for industrial AI platforms?
- How should manufacturers handle security and governance when connecting OT systems to cloud-based AI?
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## Episode Highlights with Timestamps
**[2:27]** – **Bernard's Background & Ethon AI Origin Story** — How a PhD in computer science and collaboration with Fortune 500 manufacturers like Siemens led to founding Ethon AI, now approaching 100 employees with offices in Zurich and New York.
**[4:24]** – **Why Traditional Methods Have Maxed Out** — Bernard explains the "20 cents of every dollar goes to waste" principle and why classic automation and data science have hit diminishing returns, requiring agentic workflows and foundation models for the next improvement frontier.
**[7:49]** – **What Deep Contextualization Really Means** — A detailed walkthrough of why basic UNS tag hierarchies aren't sufficient for agentic AI, using the example of tracing a batch rework problem across tanks, recipes, time series, and operator interventions.
**[12:45]** – **Process Knowledge Graph Explained** — Bernard defines ontologies and knowledge graph triples, showing how semantic meaning enables questions like "which five machines cost the most downtime today" versus simple tag queries.
**[16:02]** – **Build the Graph First or Build the Application First?** — The chicken-and-egg debate on knowledge graph strategy, and why Ethon chose to build the graph behind ROI-delivering applications rather than creating a monolithic model upfront.
**[18:16]** – **Causal AI vs. Correlation Analytics** — The ice cream and shark attacks analogy applied to manufacturing: how causal models turn seasonal production correlations into actionable insights about cooling water temperature adjustments.
**[21:28]** – **The Full Agentic AI Architecture Stack** — Bernard outlines three layers: data infrastructure (connectivity + knowledge graph), model layer (purpose-built causal and inspection models), and application layer (agentic workflows or human interfaces).
**[24:54]** – **Why General-Purpose LLMs Aren't Enough for Manufacturing** — Safety-critical environments require models that understand spec limits, user manuals, and process constraints—not just pattern-matched text generation.
**[29:33]** – **Ethon AI Platform Walkthrough** — A modular enterprise platform that measures what's happening, understands why, suggests improvement actions, and enables autonomous process control through dynamic SOPs and centerline dashboards.
**[37:19]** – **Causal AI's Medical Origins Applied to Manufacturing** — How treating a production process like a patient (healthy or sick) allows causal models to extract actionable knowledge from months of operator interventions and process adjustments.
**[48:03]** – **Deployment Timeline and Forward Deployed Engineers** — Ethon's Palantir-inspired deployment model with on-site engineers, achieving first value consistently in under three months.
**[51:17]** – **Case Studies: Siemens and Lindt & Sprüngli** — Globally scaled deployments with $10M+ documented savings at Siemens (published by the World Economic Forum) and significant waste reductions at Lindt & Sprüngli's chocolate production facilities.
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## Key Takeaways
- **Knowledge graphs are non-negotiable for agentic AI:** A unified namespace provides basic tag context, but agentic workflows require deep semantic relationships—connecting batches to recipes, tanks to flow paths, and time series to operator interventions. Without this ontology layer, AI agents cannot perform meaningful root cause investigation.
- **Causal AI makes insights actionable, not just interesting:** Correlation analytics can tell you production runs better in winter, but causal AI identifies that lower feeding water temperature improves cooling behavior, giving operators a specific lever to pull in summer months. This distinction is critical for safety-critical environments where recommendations must be trustworthy.
- **Purpose-built industrial models prevent hallucination in critical decisions:** By placing a specialized causal model layer between the data infrastructure and the agentic application layer, recommendations are grounded in verified causal relationships rather than LLM pattern matching. The agentic layer enriches these findings with SOPs and documentation but cannot fabricate the underlying analysis.
- **Start with ROI-delivering applications, not infrastructure perfection:** Rather than building a complete knowledge graph before deploying AI, Ethon's approach builds the graph incrementally behind applications that deliver measurable value. Users often don't realize they're building a knowledge graph because they're simply modeling their data while getting returns.
- **Change management is as important as the technology:** Operators and process engineers have solved problems for decades without data-driven tools. AI systems must explain their reasoning through causal chains, build trust incrementally, and integrate into existing workflows without adding friction—even one extra second per task multiplied across thousands of repetitions creates significant resistance.
- **Security requires one-way data flow by design:** When connecting legacy OT systems (some 20-30 years old) to cloud AI, the architecture must ensure information flows only from factory to cloud, with no return path that could serve as an attack vector. Edge-deployable modules handle latency-sensitive tasks like optical inspection independently.
- **Cross-factory intelligence is the next major value unlock:** Most manufacturers still analyze individual lines or factories in isolation. Connecting multiple factories to shared knowledge graph concepts enables cross-site learning—identifying why one line outperforms another and transferring those insights globally.
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## Notable Quotes
> "Every dollar you spend on manufacturing, 20 cents go to waste. That has been true 50 years ago, and it will be true probably 50 years in the future, because there's always 20% to get." — Bernard Kraswald, CTO at Ethon AI
> "The insights you get cannot be hallucinated, because they're coming from this underlying model layer—from this causal model. The LLM agentic layer on top cannot fabricate that." — Bernard Kraswald, CTO at Ethon AI
> "You're never done with building your knowledge graph, because there's always more knowledge you can distill out of it." — Bernard Kraswald, CTO at Ethon AI
> "The only mistake you can make today is not doing anything. The best time to start was yesterday, and the second best time to start would be today." — Bernard Kraswald, CTO at Ethon AI
> "Every AI system will make some mistakes. So here is my best, wholehearted suggestion, and this is why I believe it's true—and now you can click and triple down, follow the root cause links, and investigate everything." — Bernard Kraswald, CTO at Ethon AI
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## Key Concepts Explained
**Process Knowledge Graph**
Definition: A semantic data model built on ontologies that assigns meaning to industrial data and defines how different data elements relate to each other—connecting machines, sensors, batches, recipes, and physical flows into a queryable graph structure using subject-predicate-object triples.
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