Jump Trading searches for predictive signals across thousands of instruments and terabytes of market data each day. That work depends on many disciplines, including ML infrastructure, deep learning for alpha prediction, and agent-based research automation. Each is critical, and each poses its own research problems. This panel focuses on agentic research in a domain where data is non-stationary, signal-to-noise is extremely low, and one look-ahead error can invalidate weeks of work. Panelists will discuss how agents can build on the published literature and where they must go beyond it, and how to give them domain intuition, including knowledge absent from public training data. We will review approaches to detecting and mitigating spurious results at several levels. These range from outright reward hacking to subtler cases where an agent gains on one metric at the expense of others, or meets hard constraints while drifting on soft ones. The panel will also cover infrastructure bottlenecks and the tradeoff between domain-specific structure and general methods that scale with compute. We close with open questions on dividing work between humans and agents, and on which research tasks agents cannot yet do.