As AIoT adoption accelerates, many teams are taking a seemingly simple approach: pointing AI agents or LLMs directly at AIoT platform APIs like Cumulocity.
The result? Without domain context, agents misinterpret telemetry, return unreliable, or even hallucinated results; all while driving unnecessary compute cost.
This video shows a different approach.
By introducing a Semantic Layer and Model Context Protocol (MCP), AI agents are grounded in physical truth. This ensures queries are interpreted correctly and results reflect real-world operations.
Why this matters
For solution architects and integration teams, this addresses a growing pain point: how to safely connect enterprise AI orchestrators (like copilots or agent frameworks) to physical systems.
Rather than building brittle, custom translation layers (or risking unreliable outputs) Cumulocity provides a ready-made foundation. It acts as a specialised industrial task agent, translating natural language queries into accurate, context-aware interactions with your asset data.
What you’ll gain from watching
By the end of the video, viewers will:
- Understand why ungrounded AI agents struggle with IoT/OT data
- See how a Semantic Layer provides the missing context for accurate AI reasoning
- Learn how MCP enables safe, structured interaction between AI agents and physical systems
- Discover how this approach avoids hallucinations while reducing development effort and cost
Ultimately, this demo shows that the future of AIoT isn’t just about connecting AI to data—it’s about grounding intelligence in the reality of your operations.