Quantitative finance imposes constraints that stress-test general-purpose agent architectures: data is non-stationary, latency budgets are tight, and subtle errors in temporal reasoning can invalidate an entire research pipeline. At Jump Trading, we build multi-agent systems that operate under these constraints across thousands of instruments and terabytes of daily market data, searching for structure in a regime characterized by extremely low signal-to-noise ratios and adversarial selection against all but the most rigorously designed strategies.

In this talk, we present results from firmwide and trading-specific benchmarks evaluating multi-agent architectures. Starting from a baseline of frontier single-agent systems in commonly used terminal harnesses, we compare variations across harness design, context management, inter-agent communication, parallel execution, and post-training for task specialization. We characterize the architectural choices in task decomposition, context scoping, and workflow structure that justify the additional complexity of harness design and post-training, highlighting environments where multi-agent systems and domain-focused subagents outperform a single context-rich frontier model. Finally, we discuss methods to improve evaluation quality in complex domains such as quantitative finance where proprietary data, scarce human labels, and heterogeneous composition of both tasks and technical environments preclude reliance on publicly available benchmarks. Additionally, we present critical steps towards deriving information-theoretic bounds as a function of entropy that guide the convergence of agent-based processes.