We explore the equilibrium effects of agentic markets, in which AI tools assist consumers and businesses in searching for and transacting in products. Through a mathematical model of sequential search, the authors analyze how reducing search costs and increasing the detail of pre-purchase information impact market learning and consumer welfare. The research highlights a counterintuitive finding: while lower search costs generally improve outcomes, more informative search can actually decrease consumer surplus by weakening competition and causing businesses to be prematurely abandoned. To mitigate these risks, the authors suggest that platforms should record transcripts of agent interactions to better aggregate information. Finally, the study examines endogenous pricing, demonstrating that AI-driven search efficiency can lead to higher prices if it reduces the number of viable competitors for a specific consumer need.