Context Engineering for Building Reliable Industrial AI Agents: Zach Etier - Flow Software

Podcast Name: AI in Manufacturing Podcast (Industry40.tv)

Episode Title: Context Engineering Techniques for Building Reliable Industrial AI Agents

Guest: Zach Etier, VP of Architecture at Flow Software

Host: Kudzai Manditereza

Episode Summary

This episode explores context engineering — the discipline of curating and managing the information supplied to AI agents — and why it is the key to building reliable industrial AI systems. Zach Etier, VP of Architecture at Flow Software, joins host Kudzai Manditereza to break down why simply pumping more data into an AI agent's context window actually degrades performance through dilution, hallucination, and lost instructions. Zach walks through three core context engineering techniques — persisting context, summarization/compaction, and isolation via sub-agents — and explains how each one maps to real manufacturing use cases like automated shift-handover reports. The conversation also covers the practical differences between skills, MCP servers, and sub-agents, and why deterministic code should handle calculations while agents handle orchestration. Finally, Zach makes the case that knowledge graphs with formal ontologies will become essential data architecture for scaling industrial AI across the enterprise. Whether you are evaluating your first agent pilot or planning multi-site deployment, this episode provides a concrete framework for engineering context that agents can reliably act on.

Key Questions Answered in This Episode

• What is an industrial AI agent, and how does it differ from a chatbot or general-purpose LLM?
• Why does giving an AI agent more context actually reduce its performance?
• What is context engineering, and why is it replacing prompt engineering for agentic AI?
• What are the three core techniques for managing an AI agent's context window in manufacturing?
• How should you decide when to use skills vs. MCP servers vs. sub-agents?
• Why should deterministic code handle calculations instead of letting the AI agent compute them?
• How do knowledge graphs and ontologies enable enterprise-scale industrial AI?