Abstract: Agentic digital assistant methods and systems for generating a response to a user query are disclosed. A computer-implemented method includes accessing a query, obtaining an agent execution plan that identifies one or more agent actions to be executed and an order in which the one or more agent actions are to be executed, executing the agent execution plan to obtain one or more results for the one or more agent actions, and generating a response to the query using the one or more results.

Abstract: Agentic digital assistant methods and systems for generating a response to a user query are disclosed. A computer-implemented method includes accessing a query, executing planner modules in parallel to generate respective executable actions to retrieve information for answering the query, using a primary planner module of the planner modules to generate an execution plan for executing the executable actions, executing the executable actions per the execution plan to generate a set of results for the executable actions, and generating a response to the query using the set of results.

Abstract: Agentic digital assistant methods and systems for generating a response to a user query are disclosed. A computer-implemented method includes accessing a query, obtaining an agent execution plan that identifies one or more agent actions to be executed and an order in which the one or more agent actions are to be executed, executing the agent execution plan to obtain one or more results for the one or more agent actions, and generating a response to the query using the one or more results.

Abstract: Agentic digital assistant methods and systems for generating a response to a user query are disclosed. A method includes accessing a query regarding a person and database schema information for a database storing an electronic record for the person. An input for a generative machine learning model is generated based on the query and the database schema information. The input is provided to the generative machine learning model to an execution plan for executing actions in one or more stages to generate a set of results for use in generating a response to the query. The execution plan is executed to obtain the set of results. A response to the query is generated using the set of results.

Abstract: Systems and methods for generating an interference prediction for a target well are disclosed herein. A computing system generates a plurality of interference metrics for a plurality of interference events. For each well, the computing system generates a graph based representation of the well and its neighboring wells. The computing system generates a predictive model using a graph-based model by generating a training data set and learning, by the graph-based model, an interference value for each interference event based on the training data set. The computing system receives, from a client device, a request to generate an interference prediction for a target well. The computing system generates, via the predictive model, an interference metric based on the one or more metrics associated with the target well.

Abstract: Systems and methods for generating an interference prediction for a target well are disclosed herein. A computing system generates a plurality of interference metrics for a plurality of interference events. For each well, the computing system generates a graph based representation of the well and its neighboring wells. The computing system generates a predictive model using a graph-based model by generating a training data set and learning, by the graph-based model, an interference value for each interference event based on the training data set. The computing system receives, from a client device, a request to generate an interference prediction for a target well. The computing system generates, via the predictive model, an interference metric based on the one or more metrics associated with the target well.

Abstract: Systems and methods for generating an interference prediction for a target well are disclosed herein. A computing system generates a plurality of interference metrics for a plurality of interference events. For each well, the computing system generates a graph based representation of the well and its neighboring wells. The computing system generates a predictive model using a graph-based model by generating a training data set and learning, by the graph-based model, an interference value for each interference event based on the training data set. The computing system receives, from a client device, a request to generate an interference prediction for a target well. The computing system generates, via the predictive model, an interference metric based on the one or more metrics associated with the target well.

Abstract: Systems and methods for generating an interference prediction for a target well are disclosed herein. A computing system generates a plurality of interference metrics for a plurality of interference events. For each well, the computing system generates a graph based representation of the well and its neighboring wells. The computing system generates a predictive model using a graph-based model by generating a training data set and learning, by the graph-based model, an interference value for each interference event based on the training data set. The computing system receives, from a client device, a request to generate an interference prediction for a target well. The computing system generates, via the predictive model, an interference metric based on the one or more metrics associated with the target well.