Deploying AI for highly complex, open-ended discovery tasks exposes the limitations of current context windows and reasoning capabilities. This talk explores our latest multi-agent system designed to automate the deep scientific research pipeline. We will detail the "AI Scientist," an end-to-end architecture that autonomously generates novel hypotheses, translates concepts into experimental ML pipelines, and synthesizes verifiable research papers. We will unpack the design of specialized sub-agents, particularly focusing on our expert reviewer agent (ScholarPeer) which uses a novel "historian" approach to verify true novelty against historical literature, and the automated visual generator (PaperBanana) which tackles the complex multi-modal challenge of synthesizing accurate methodology figures. Attendees will gain insights into how we orchestrate and evaluate these workflows to reliably accelerate the pace of AI discovery.