Abstract: The systems and methods disclosed herein receives artifacts generated using a first set of models within a multi-model superstructure. The multi-model superstructure includes a second set of models to test the first set of models. The multi-model superstructure dynamically routes the artifacts of the first set of models to one or more models of the second set of models by (i) determining a set of dimensions of the artifacts against which to evaluate the artifacts and (ii) identifying the models in the second set used to test the particular dimension. The second set of models then assesses each artifact against a set of assessment metrics. If an artifact fails to meet one or more assessment metrics, the second set of models generates actions to align the artifact with the set of assessment metrics.

Abstract: A network system to use machine learning systems to create chaos testing scenarios on cloud-based applications. The system uses inputs from applications that are implemented on user computing devices to allow users to interface with a network or other system. The system creates a model of the application based on input data received from a network of applications, the model representing a structure, method, and dependencies of the application. The system identifies points of failure of the application and generates one or more chaos testing simulation scenarios that target the identified points of failure. The system performs the chaos testing based on the received simulation scenarios and logs the results of the testing. The system generates recommendations to revise code of the application based on the outcome of the chaos testing. A large language model may be used to provide documentation and analysis of the chaos testing.

Abstract: A network system to use machine learning systems to create chaos testing scenarios on cloud-based applications. The system uses inputs from applications that are implemented on user computing devices to allow users to interface with a network or other system. The system creates a model of the application based on input data received from a network of applications, the model representing a structure, method, and dependencies of the application. The system identifies points of failure of the application and generates one or more chaos testing simulation scenarios that target the identified points of failure. The system performs the chaos testing based on the received simulation scenarios and logs the results of the testing. The system generates recommendations to revise code of the application based on the outcome of the chaos testing. A large language model may be used to provide documentation and analysis of the chaos testing.

Abstract: The technology evaluates the compliance of an AI application with predefined vector constraints. The technology employs multiple specialized models trained to identify specific types of non-compliance with the vector constraints within AI-generated responses. One or more models evaluate the existence of certain patterns within responses generated by an AI model by analyzing the representation of the attributes within the responses. Additionally, one or more models can identify vector representations of alphanumeric characters in the AI model's response by assessing the alphanumeric character's proximate locations, frequency, and/or associations with other alphanumeric characters. Moreover, one or more models can determine indicators of vector alignment between the vector representations of the AI model's response and the vector representations of the predetermined characters by measuring differences in the direction or magnitude of the vector representations.

Abstract: Systems and methods for improving the efficiency and accuracy of network operation validation anomaly detection in conglomerate-application-based ecosystems are disclosed. The disclosed anomaly evaluation platform can provide a first network operation to a first software application for generation of a second network operation, as in a flow-based processing system. The platform can generate a communication map that characterizes the architecture and/or performance of the system. In response to providing the communication map and the network operations to a validation model, the anomaly evaluation platform can determine a validation status and execute a corrective action to cure detected anomalies preventing validation of the network operation. As such, the anomaly evaluation platform enables dynamic monitoring, evaluation, and mitigation of detected anomalies in real-time and in a performance-dependent manner.

Abstract: The technology evaluates the compliance of an AI application with predefined guidelines. The technology obtains a set of guidelines defining operation boundaries of the AI application and constructs test cases associated with each guideline. Each test case can include a prompt, an expected outcome, and an expected explanation. The technology supplies the prompts to the AI application, receives case-specific outcomes and explanations from the AI application, and compares them with the expected outcomes and expected explanations. A compliance indicator is generated based on the evaluation results, indicating the degree of compliance of the AI application with the guidelines.

Abstract: Systems and methods for reducing wasted computational resources by detecting network operation validation anomalies in conglomerate-application-based ecosystems are disclosed. The system provides a first network operation to a first software application of a set of software applications, where (i) the first network operation is configured to be provided as input to the first software application and (ii) the first software application generates a second network operation as output, using the first network operation, to be provided to a second software application. The system receives a first message associated with a processing of the first network operation at the first software application. The system then performs a determined validation process on the first message. In response to detecting that the determined validation process results in a failure to validate the first message, the system prevents the second network operation from being provided to the second software application.

Abstract: A network system to provide testing, resiliency, chaos and performance testing in a single tool that operates on cloud-based applications in real time. When applications are deployed on a cloud environment, the applications must be validated based on functional aspects, performance aspects, resiliency aspects, and test-code coverage ratio aspects. The system combines all these validations in a single package. The system provides a rich set of modules for functional validations, which can be combined as nodes to allow applications to be quickly validated for functionality. The extensible model of the same functional modules may also be used for performance testing. The system also provides resiliency and chaos testing mechanisms that push the applications into a constant state of perturbation. The system does so by detecting the application code and modifying the code at runtime to inject perturbation. The system monitors the recovery of the system from the testing.