Abstract: State-of-the-art Integration Platform as a Service (iPaaS) platforms do not enable users to utilize their own artificial intelligence (AI) models (e.g., large language models or other generative model). Disclosed embodiments enable users to bring your own model (BYOM) within an integration platform. In particular, users may build or import their own AI model, and integrate that AI model into their integration processes in the same manner as any other integration step. This may involve dragging a shape, representing the AI model, from a virtual palette onto a virtual canvas, and configuring the corresponding AI step. During execution, the resulting integration process may automatically and dynamically, in real time, receive input data and retrieve contextual metadata to enhance the input data with context. The combination of input data and contextual metadata may be provided to the AI step to perform the desired task with improved accuracy.

Abstract: There is no unified, comprehensive system for the governance of artificial intelligence (AI) agents. Accordingly, disclosed embodiments register a plurality of AI agents, potentially from disparate sources, into a centralized registry. Prior to activation, each AI agent is associated with a governance policy. After activation, each AI agent is monitored in real time, during execution, to determine whether or not the AI agent remains compliant with the associated governance policy. When an AI agent becomes non-compliant with the associated governance policy, corrective action may be taken, and the AI agent may be labeled as untrusted. The corrective action may comprise alerting a user, modifying the AI agent, adjusting an amount of each of one or more computational resources that is allocated to the AI agent, modifying an access of the AI agent to one or more systems, adjusting a communication control of the AI agent, and/or the like.

Abstract: State-of-the-art tracing systems struggle with the analysis and visualization of the debugging data for artificial intelligence (AI) agents. In an embodiment, a trace engine obtains telemetry data during execution of an AI agent, and generates a hierarchical trace structure, comprising a decision trace structure representing a decision layer of the AI agent, an operation trace structure representing an operation layer of the AI agent, and an implementation trace structure representing an implementation layer of the AI agent. A visual debugging interface may query this hierarchical trace structure to generate one or more interactive visual elements for debugging of the AI agent.

Abstract: A system and method for facilitating integrating software systems. An example method includes generating a test dataset (also called instance dataset herein); inputting the test dataset to a first software system and a second software system that are to be integrated; triggering a first set of one or more source messages (also called target messages herein) from the first software system and a second set of one or more source messages from the second output message based on the test dataset; receiving the first set of one or more source messages and the second set of one or more source messages; and analyzing the message sets to determine a mapping sufficient to translate messages exchanged between the first and second software systems, such that the messages are readable thereby.

Abstract: A system and method for facilitating integrating software systems. An example method includes generating a test dataset (also called instance dataset herein); inputting the test dataset to a first software system and a second software system that are to be integrated; triggering a first set of one or more source messages (also called target messages herein) from the first software system and a second set of one or more source messages from the second output message based on the test dataset; receiving the first set of one or more source messages and the second set of one or more source messages; and analyzing the message sets to determine a mapping sufficient to translate messages exchanged between the first and second software systems, such that the messages are readable thereby.

Abstract: A system and method for facilitating integrating software systems. An example method includes generating a test dataset (also called instance dataset herein); inputting the test dataset to a first software system and a second software system that are to be integrated; triggering a first set of one or more source messages (also called target messages herein) from the first software system and a second set of one or more source messages from the second output message based on the test dataset; receiving the first set of one or more source messages and the second set of one or more source messages; and analyzing the message sets to determine a mapping sufficient to translate messages exchanged between the first and second software systems, such that the messages are readable thereby.

Abstract: A system and method for facilitating integrating software systems. An example method includes generating a test dataset (also called instance dataset herein); inputting the test dataset to a first software system and a second software system that are to be integrated; triggering a first set of one or more source messages (also called target messages herein) from the first software system and a second set of one or more source messages from the second output message based on the test dataset; receiving the first set of one or more source messages and the second set of one or more source messages; and analyzing the message sets to determine a mapping sufficient to translate messages exchanged between the first and second software systems, such that the messages are readable thereby.