Abstract: There is disclosed a method and system for executing commands. The method comprises configuring an input event topic subscriber and a command orchestrator process. The input event topic subscriber is invoked. The input event topic subscriber receives an event. The event comprises an event context and associated data. The event is transformed into a command. The command orchestrator is invoked. The command is input to the command orchestrator. The command orchestrator adds contextual information to the command. The command orchestrator schedules execution of the command. The execution of the command is tracked. A returned data object corresponding to the command is received and output.

Abstract: There is disclosed a method and system for executing commands. The method comprises configuring an input event topic subscriber and a command orchestrator process. The input event topic subscriber is invoked. The input event topic subscriber receives an event. The event comprises an event context and associated data. The event is transformed into a command. The command orchestrator is invoked. The command is input to the command orchestrator. The command orchestrator adds contextual information to the command. The command orchestrator schedules execution of the command. The execution of the command is tracked. A returned data object corresponding to the command is received and output.

Abstract: There is disclosed a method and system for operating an event-driven architecture. The event-driven architecture comprises a first machine-learning (ML) agent operating a first service and a second ML agent operating a second service. The first ML agent comprises a first model and first model metadata. The second ML agent comprises a second model and second model metadata. The method comprises generating, by the first ML agent, an event associated with event metadata. The event comprises results generated by the first model. The event metadata comprises an event identifier (ID). The first ML agent publishes the event in a virtualized dedicated space. The second ML agent determines whether the event is to be processed by the second ML agent. If a determination is made that the message is to be processed by the second ML agent, the second ML agent processes the event to generate an output.