Professor of Software Engineering at the Pontifical Catholic University of Rio de Janeiro
Rapid advancements in AI paradigms—Machine Learning, Reinforcement Learning, Symbolic AI, Foundation Models, and Multi-Agent Systems—have expanded AI’s capabilities while introducing new software engineering challenges. Each paradigm has inherent limitations: Machine Learning excels at pattern recognition but lacks explicit reasoning; Reinforcement Learning optimizes rewards but is unpredictable; Symbolic AI provides structure but struggles with flexibility; Foundation Models generalize knowledge but raise interpretability concerns; and Multi-Agent Systems enable distributed intelligence but introduce emergent behaviors.
Just as the human brain integrates perception, learning, reasoning, and decision-making, AI systems can combine multiple paradigms to achieve adaptable intelligence. While AI Engineering has made progress in building robust Machine Learning systems, our understanding of Intelligence Engineering—the integration of multiple AI paradigms—remains limited. Furthermore, software engineering aspects such as requirements and architecture are still poorly understood across different AI paradigms and their combinations. Without deeper insights and systematic methodologies, we still lack the necessary means to build trustworthy, scalable, and maintainable intelligent systems that effectively integrate these paradigms.
This talk presents a research roadmap for AI Engineering, highlighting the urgent need for evidence-based intelligence engineering and the development of systematic specification approaches, architectural patterns, and tactics for multi-paradigm AI integration. It also underscores the critical role of empirical software engineering in navigating the hype responsibly and reinforcing AI Engineering as an evidence-driven discipline.
Marcos Kalinowski is a Professor of Software Engineering at PUC-Rio, Brazil. His research focuses on AI Engineering, Experimental Software Engineering, and Human Aspects of Software Engineering. Before transitioning to academia, he accumulated over a decade of industry experience. He co-leads the ExACTa PUC-Rio lab, where he supervises PhD research and leads R&D projects with industry partners to develop AI engineering solutions and real-world AI systems. His lab's work has resulted in award-winning AI deployments and multiple US patents. He has authored several books and over 200 research publications, earning 19 distinguished paper awards, including ACM Distinguished Paper Awards at ICSE and CAIN. He holds a research productivity distinction from the Brazilian Research Council (CNPq) and an honorary 'Scientist of the State' distinction from Rio de Janeiro's state research agency (FAPERJ). He serves the community as associate editor of the Journal of Systems and Software and as part of the organizing and program committees of several international conferences. He is an active member of ACM, IEEE, ISERN, and the Brazilian Computer Society.
