Abstract: Systems, devices, methods, and computer-readable media for guided conversations with a generative artificial intelligence (AI) agent to complete an artifact are provided. A method includes generating a prompt, the prompt including a context, the artifact to be completed during a guided conversation, and rules to be followed in conducting the guided conversation, providing the prompt to the generative AI agent, receiving a response to a message from the generative AI agent, receiving a first function call to update a field of the artifact, the function call including a field and a value, determining, based on the value and a schema of the artifact, a result indicating whether the update is valid or invalid, providing, to the generative AI agent, the result, and receiving, from the generative AI agent, the artifact after the conversation is completed.

Abstract: In accordance with examples of the present disclosure, a collaborative platform provides a digital collaboration assistant that continuously monitors and analyzes shared meeting contents (e.g., voice, text chat messages, shared links and documents, presentation materials, and the like) by participants during a collaborative meeting in near real-time, periodically updates a structure summary log of the meeting contents that are deemed important during the collaborative meeting, and interacts with the participants throughout the collaborative meeting in near real-time, for example, to answer questions or provide additional information.

Abstract: In accordance with examples of the present disclosure, a collaborative platform provides a digital collaboration assistant that continuously monitors and analyzes shared meeting contents (e.g., voice, text chat messages, shared links and documents, presentation materials, and the like) by participants during a collaborative meeting in near real-time, periodically updates a structure summary log of the meeting contents that are deemed important during the collaborative meeting, and interacts with the participants throughout the collaborative meeting in near real-time, for example, to answer questions or provide additional information.

Abstract: In accordance with examples of the present disclosure, a collaborative platform provides a digital collaboration assistant that continuously monitors and analyzes shared meeting contents (e.g., voice, text chat messages, shared links and documents, presentation materials, and the like) by participants during a collaborative meeting in near real-time, periodically updates a structure summary log of the meeting contents that are deemed important during the collaborative meeting, and interacts with the participants throughout the collaborative meeting in near real-time, for example, to answer questions or provide additional information.

Abstract: Aspects of the present application relate to machine learning (ML) structured result generation. In examples, an instruction of programmatic code that invokes an ML model indicates a result interface in which model output is to be stored. The result interface is processed to generate a data format description for the result interface, such that the input to the ML model further includes the data format description. As a result of providing the data format description as input to the ML model, the ML model is induced to generate structured model output that corresponds to the result interface. The resulting model output is processed to generate an instance of the result interface, for example having one or more corresponding properties from the structured model output. Accordingly, the programmatic code is able to reliably perform subsequent processing based on the generated instance of the result interface.

Abstract: Aspects of the present disclosure relate to machine learning instancing, where an instance of an agent (e.g., including processing of user input by a machine learning model to generate model output) is encapsulated as an agent object. In examples, an agent object is stored as a file, as a document, and/or in a database, among other examples. An agent object includes a persona definition and/or an object embedding memory, thereby defining various aspects of the agent. Thus, an agent object permits portability the agent, for example between users, across contexts, and/or for a variety of subsequent processing, among other examples.