Perspectives

Autonomous research systems for markets: data foundations, agent orchestration, reasoning models, and research harnesses.

Foundation

Market data and the coordination of specialized agents in financial research.

Data foundations

Point-in-time data, provenance, and why training cutoffs matter in finance.

What multi-agent systems add, what they cost, and when they pay off.

Intelligence

Models that read evidence, weigh it, and learn from outcomes.

Retrieval helps models read financial documents; expert judgment is still hard to teach.

Reinforcement learning sharpens reasoning; its reward and data set the limit.

Application

The research harness and evaluation, so professionals can verify results and act on them.

Tools, context, and harnesses: the engineering that determines how far research can be relied on.

How to evaluate research systems, and why professionals keep the decision.