Abstract: This application describes, amongst other things, methods and systems for building and deploying agents. An example method includes obtaining orchestration data about a set of task-specific components, where each respective task-specific components in the set of task-specific components is configured to assist with a respective task of a plurality of tasks. The method also includes receiving a prompt related to one or more tasks of the plurality of tasks and selecting a subset of task-specific components based on the prompt and the orchestration data. The method further includes coordinating, via a routing agent, interactions between the task-specific components, including providing data related to the prompt to the task-specific components and receiving responses from the task-specific components, and generating a complete response to the prompt that addresses the one or more tasks using the responses from the task-specific components.

Abstract: This application describes, amongst other things, methods and systems for building and deploying agents. An example method includes obtaining orchestration data about a set of task-specific components selected to provide a response to the prompt, where each respective task-specific components in the set of task-specific components is configured to assist with a respective clinical task of the one or more clinical tasks. The method further includes, determining an order in which each respective task-specific components of the set of task-specific components should be utilized to prepare a complete response to the prompt that address the one or more clinical tasks based on the obtained orchestration data about the set of task-specific components. The method also includes, in accordance with the determined order, providing first data related to the prompt to a first task-specific component and receiving a first response from the first task-specific component.

Abstract: This application describes, among other things, systems and methods for deploying a task-specific machine-learning model. An example method includes receiving a prompt associated with one or more commands and a plurality of tokens. A first task-specific machine-learning model is identified based on a first command of the one or more commands. Some or all of the tokens of the plurality of tokens are applied to a node associated with the first task-specific machine-learning model, and a plurality of restricted data is received. Based on evaluating the plurality of restricted data, a correlation between the first node and a second node is determined. If the correlation satisfies a threshold condition, a plurality of text data different than the prompt is generated, otherwise the some or all of the tokens are again provided to the first node. Some or all of the tokens are then traversed and applied to the second node.

Abstract: This application describes, among other things, methods of selecting a task-specific machine-learning model for addressing a clinical task. An example method includes receiving a prompt from a user. Based on determining that the prompt requests assistance with a clinical task, a machine-learning model trained to select from among a plurality of task-specific machine-learning models each trained to assist with one of a plurality of clinical tasks selects a respective task-specific machine-learning model from among the plurality of task-specific machine-learning models based on the prompt. The prompt is provided to the selected task-specific machine-learning model. And a response received from the selected task-specific machine-learning model is provided to the user.

Abstract: This application describes, among other things, methods of generating task-specific agent modules based on user requests. An example method includes receiving a request from a user for performance of a specific task. In response to the request, an agent module to perform the specific task is generated. The generating includes selecting a set of agent building blocks from a plurality of available agent building blocks, where each agent building block in the plurality of available agent building blocks has a respective assigned function, and connecting the set of agent building blocks to form the agent module. The agent module is caused to be executed, and information from the request is provided to the agent module. Based on providing information from the request to the agent module, a response for performing the specific task is received and provided to the user.

Abstract: This application describes, amongst other things, methods and systems for building and deploying agents. An example method includes obtaining orchestration data about a set of task-specific components selected to provide a response to the prompt, where each respective task-specific components in the set of task-specific components is configured to assist with a respective clinical task of the one or more clinical tasks. The method further includes, determining an order in which each respective task-specific components of the set of task-specific components should be utilized to prepare a complete response to the prompt that address the one or more clinical tasks based on the obtained orchestration data about the set of task-specific components. The method also includes, in accordance with the determined order, providing first data related to the prompt to a first task-specific component and receiving a first response from the first task-specific component.

Abstract: This application describes, among other things, machine-learning models for performing specific clinical tasks. An example method includes receiving a prompt at a first computing system in communication with a machine-learning model trained to assist in performing a clinic task that includes generating a report of a patient's medical records, guiding a patient through a care plan, creating patient care guidelines based on a patient's health profile, identifying patients requiring follow-up at a hospital, identifying changes in a standard of care for a disease setting, or evaluating unstructured data associated with a patient to identify a cohort of similar patients. Based on the prompt, a natural language response is generated that is responsive to the prompt and is based on an analysis by the machine-learning model of a repository of data that is determined to be relevant to the prompt. And the natural language response is provided to second computing system.

Abstract: This application describes, among other things, systems and methods for interacting with a task-specific orchestration. An example method includes obtaining medical data from one or more data collections. A set of user interface elements is presented at a user interface, where each respective user interface element of the set of user interface elements represents a respective task-specific orchestration that includes one or more respective machine-learning models fine-tuned for a specific task or domain based on the medical data. When a user interface element representing a respective task-specific orchestration is selected, at least some of the medical data is provided to the respective task-specific orchestration, and a different user interface is presented for communicating with the respective task-specific orchestration. Based on receiving a prompt at the different user interface, a response object is generated by the task-specific orchestration based on the prompt and at least some of the medical data.

Abstract: This application describes, among other things, systems and methods for configuring a task-specific machine-learning model. An example method includes, based on a request to modify a machine-learning model for performing a clinical task, retrieving a corresponding node architecture defining conditional logic for performing the clinical task. The conditional logic is executed based on a first order, including an input node, an output node, and an intermediate node, of a first set of interconnected nodes including a data source node, a machine-learning model node, and a conditional logic node. A representation is generated including a first feature for configuring conditional logic of the node architecture and a second feature for configuring a parameter of a node in the first set of interconnected nodes. A selection of the first or second feature defines a second order of a second set of interconnected nodes, causing the node architecture's conditional logic to be updated.