## 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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