Prediction markets are getting complicated fast. Kalshi’s solution: build an AI agent to keep up.
The CFTC-regulated exchange has developed an internally built AI agent called Harrison, designed to help design and stress-test the prediction market contracts that underpin its rapidly growing platform. Co-founder Luana Lopes Lara described Harrison as a critical tool for managing the operational load of a platform now handling millions of daily wagers tied to everything from elections to sports to award ceremonies.
What Harrison actually does
Harrison isn’t a consumer-facing product. It’s an internal tool built to support Kalshi’s workflows in several key areas.
First, contract design. Prediction markets live and die by the precision of their contract language. A poorly worded contract on, say, whether a specific candidate wins a primary can lead to resolution disputes, user frustration, and regulatory headaches. Harrison is designed to help stress-test these contracts before they go live, catching ambiguities and edge cases that human reviewers might miss under time pressure.
