Hybrid AI: Combining Deterministic Analytics with LLM Reasoning
The article discusses the challenges faced in developing an agentic AI system for manufacturing operations. It highlights the issues of generating plausible but incorrect analytics due to the limitations of current AI models. A proposed solution involves separating deterministic data analysis from LLM-based reasoning to improve reliability.
- ▪The initial prototype of the AI system produced mostly incorrect results despite appearing promising.
- ▪AI models like ChatGPT and Microsoft Copilot exhibited patterns of generating convincing but fabricated outputs.
- ▪The author emphasizes the need for deterministic execution in foundational data analysis to ensure reliable results.
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Agentic AI Hybrid AI: Combining Deterministic Analytics with LLM Reasoning How AI architecture prevents plausible but wrong analytics Ingo Nowitzky May 22, 2026 19 min read Share Generated by author using ChatGPT Introduction I tried to build an agentic AI network for my company that advises manufacturing plants on how to mature their operations. The system was designed to be data-driven, allowing users to upload assessment data directly through the chat interface. The first working prototype was finished surprisingly quickly, and at first glance the results looked promising. There was only one problem: Most of the results were wrong! Even worse, the AI quickly learned which numerical ranges looked plausible and began generating convincing — but fabricated — outputs.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Towards Data Science.
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