Beyond "Prompt and Pray"

Agus Sudjianto

Agus Sudjianto is the Chief Scientist at KnowlytiX. He has spent more than two decades building, governing and validating quantitative models inside major financial institutions. He was Executive Vice President and Head of Model Risk at Wells Fargo, where he served on the Management Committee and led enterprise model risk management. Earlier in his career he held senior quantitative risk roles at Lloyds Banking Group and Bank of America. Since leaving corporate industry, he has continued this work as an advisor, builder and researcher across banking, fintech and AI.

Agus's work sits at the intersection of machine learning, model risk and governed AI systems. He created PiML and MoDeVa, toolkits for interpretable model development and validation, and his more recent work extends that same discipline into agentic AI, graph-grounded retrieval and geometric memory. Across these projects, the through-line is consistent: high-stakes AI should be built with the same rigor expected of high-stakes statistical models.

He is also co-author of Design and Modeling for Computer Experiments, holds several U.S. patents and has long worked across engineering, quantitative finance and applied machine learning. His current research centers on learning as geometry discovery in both predictive machine learning and generative AI.

In this series, Agus brings the perspective of someone who has spent a career asking not only whether a model works, but whether it can be governed, defended and trusted in practice.

WingYan Lau

Wing Yan Lau is the Chief Technology Officer at KnowlytiX. Her work centers on the systems layer that makes GMS usable in practice: document ingestion, knowledge-store construction, query infrastructure, verification pathways and the interfaces that connect governed AI to real enterprise data. She is a co-author of KnowlytiX's research on graph-verified evaluation and structured financial-document retrieval, including work reflected in FinStructBench and in the company's broader knowledge and testing stack.

Wing brings more than two decades of database and data-platform engineering experience to that work. She has contributed to core systems at IBM, SAP and Workday, with technical work spanning query optimization, storage systems and execution infrastructure. That background is visible throughout the KnowlytiX platform, where the challenge is not only to generate answers, but to connect models to structured knowledge in ways that remain exact, inspectable and operationally reliable.

In this series, Wing brings implementation discipline to every layer of the system: how documents become structured stores, how numeric facts remain exact, how graph-backed retrieval is made usable and how governed workflows are turned into code rather than left as intentions in prose. Her contribution is what turns the ideas in the architecture into systems an engineer can actually build, test and run.