אינטגרציה של סוכנים AI במערכות Event-Sourced

תקציר מקורי באנגליתA card gets declined and no one, including the customer, can say exactly why. That gray zone is where Divakar Kumar points his agents. In a payments and fraud system, a rule based engine and an ML model already score most transactions cleanly; the hard cases are the ambiguous ones that neither can resolve. His approach adds an agentic layer on top of an existing event sourced architecture rather than replacing it, so the bounded contexts already in the system, transaction, device, and account, become the context the agents reason over. Events flow through change feeds into projections and a semantic layer that the agents read, communicating asynchronously through a message broker in a saga style loop. A risk analyzer agent, a second agent that reaches a verdict, and a third work the case w