AI agents look magical in demos and messy in production. This talk distills how we operationalize AI agents for day-to-day analytics. We cover schema discovery for stable interfaces, structured outputs for evaluation, human-in-the-loop gates for quality, and intentional context hooks for reproducibility. In 10 minutes I will show the shapes that work, the failure modes that recur, and the smallest set of practices that make agents dependable for data science.
Bullfrog AI
Website: https://bullfrogai.com
The Bullfrog AI mission is to de-risk drug development, streamline discovery, and ultimately bring life-changing therapies to patients faster. By combining deep domain expertise with cutting-edge AI technology, BullFrog AI empowers pharmaceutical and biotech innovators to navigate complexity, reduce uncertainty, and improve outcomes in the pursuit of next-generation treatments.
BullFrog AI is transforming the future of drug discovery and development by harnessing the power of artificial intelligence and machine learning to address some of the industry’s most critical challenges, to include clinical trial optimization, target ID, and data analysis. At the core of BullFrog AI’s innovation is bfPREPTM and bfLEAP™, the company’s proprietary causal AI platform designed to analyze and interpret complex biological and clinical data with precision. Unlike traditional machine learning methods that often reveal correlations without context, bfLEAP™ uncovers causal relationships, enabling researchers to identify meaningful biological insights that drive more effective therapeutic strategies.
Through strategic collaborations with leading research institutions and biopharma organizations, BullFrog AI integrates multi-omic, preclinical, and clinical datasets to accelerate drug development pipelines. By transforming unstructured, fragmented data into harmonized, analysis-ready datasets, BullFrog AI enables scientists to uncover novel drug targets, refine trial design, and enhance patient stratification. This capability matters because more reliable insights at earlier stages directly reduce costly late-stage trial failures, shorten development timelines, and increase the likelihood of bringing effective therapies to patients who need them most.