Context Engineering: The key to AI performance and reliability

AI success is not limited by models. It is limited by context.

In industrial environments, AI agents often fail for predictable reasons: fragmented data, missing operational meaning, unclear relationships, and logic that lives in people’s heads instead of systems. Without structure, AI cannot reason reliably, explain outcomes, or operate safely at scale.

This workshop explores the emerging discipline of context engineering and why it is becoming foundational to dependable AI in manufacturing and industrial operations.

Led by Zach Etier, VP of Architecture at Flow Software, this session breaks down how context transforms raw data into something AI can actually use. Zach brings deep experience applying AI in industrial software programs, and bridging operational technology, information models, and modern AI architectures.

Participants will learn:

- Why AI agents struggle when context is implicit, undocumented, or scattered
- What “context” actually means in industrial systems, beyond prompts and embeddings
- How structured information models outperform ad hoc data pipelines
- The role of Model Context Protocol (MCP) in delivering consistent, reusable context to AI

The workshop includes hands-on demonstrations showing how MCP can be used to assemble richer operational context and improve AI accuracy, explainability, and trust.

This session is designed for engineers, architects, data teams, and manufacturing leaders who want AI systems that behave predictably, scale cleanly, and reflect how their operations truly run.

AI does not need more data, it needs better context.