AI technology is powerful but imperfect. Its complexity and imperfections have led to rapidly developing AI engineering (AIE) practices that provide more than off-the-shelf AI technology for building AI applications.
This paper explains current AI technology and AI Engineering in terms of sensemaking, reasoning, and explainability.
- Sensemaking is collecting, organizing, summarizing, and extracting information. Sensemaking leverages the utility of LLMs for finding patterns in data combined with the oversight and experience of people in the loop.
- Reasoning is about modeling how the world works, planning, and taking actions. It is needed for scheduling, design, manufacturing, construction, logistics and other complex activities. Reasoning leverages computation to sort through options and constraints to find optimal combinations.
State-of-the-art AIE practice employs design patterns for creating AI systems (“agentic systems”) that apply diverse knowledge and constraints to generate and cross-check their actions and conclusions.
This paper explains the research breakthroughs, the state of the art, and the whitespace for advancing AIE. It was invited as a follow-on paper to an earlier 2019 paper on XAI (Gunning et al., 2019) that received a Frontiers of Science award at the 2025 ICBS conference in Beijing.
Paper
Stefik, M., Gunning, D., Choi, J., Miller, T., Stumpf, S., Yang, G.-Z. 2025. How AI Engineering Addresses Sensemaking, Reasoning, and Explainability. https://www.dropbox.com/scl/fi/sgqrp0axf1chtijr10899/2025-10-28-How-AI-Engineering-Addresses-Reasoning-and-Explainability.pdf?rlkey=l8ztq319jdjhdq0cw8mia831n&dl=0