AI agents in investment research are no longer a prototype exercise. In 2026, a growing number of funds and research teams are running agentic workflows that connect to live data, reason over it, and surface actionable output without a human in every loop. Alternative data is central to this shift: agents are only as useful as the data they can access, and normalized, ticker-mapped alternative data is exactly the kind of structured input these systems need.
This post explains what investment agents actually do, why alternative data is a natural fit, and how teams are deploying these workflows