Abstract: According to some embodiments, systems and methods are provided including receiving a disaster recovery trigger, including an application identifier; retrieving the one or more tasks from a disaster recovery file including the application identifier, wherein the disaster recovery file is a YAML file; validating at least the retrieved one or more tasks and inputs; and migrating the application components including the application identifier from the primary region to the secondary region in response to execution of the validated one or more tasks. Numerous other aspects are provided.

Abstract: According to some embodiments, systems and methods are provided, including receiving a prompt; determining, by at least one of an image component and a text component, a personal identifiable information (PII) status for the prompt; returning a PII response; receiving selection of a large language model (LLM) in a case the PII status is PII-free; determining a large language model status; transmitting the prompt to the selected LLM based on the large language model status; and receiving a large language model (LLM) output. Numerous other aspects are provided.

Abstract: According to some embodiments, systems and methods are provided, including receiving a prompt; determining, by at least one of an image component and a text component, a personal identifiable information (PII) status for the prompt; returning a PII response; receiving selection of a large language model (LLM) in a case the PII status is PII-free; determining a large language model status; transmitting the prompt to the selected LLM based on the large language model status; and receiving a large language model (LLM) output. Numerous other aspects are provided.

Abstract: According to some embodiments, systems and methods are provided, including receiving a prompt; determining, by at least one of an image component and a text component, a personal identifiable information (PII) status for the prompt; returning a PII response; receiving selection of a large language model (LLM) in a case the PII status is PII-free; determining a large language model status; transmitting the prompt to the selected LLM based on the large language model status; and receiving a large language model (LLM) output. Numerous other aspects are provided.

Abstract: Embodiments may provide systems and methods to facilitate automated testing for an enterprise data application layer. An Automated Testing Framework (“ATF”) platform may receive, from a user, data test planning information that defines a test case. The ATF platform may interpret Application Programming Interface (“API”) information to implement a Data Test Automation-as-a-Service (“DTAaaS”) and detect a trigger event that initiates a test execution associated with the test case. In some embodiments, information about the test case is stored via a third-party enterprise team planning system and/or hosting service for software development and version control. Responsive to the detected trigger, the ATF platform may automatically arrange to execute the test case via the DTAaaS. A test result of the executed test case may then be output (e.g., pass, fail, or inconclusive).

Abstract: Some embodiments may facilitate software development and operations for an enterprise. A communication input port may receive information associated with a software continuous integration/deployment pipeline of the enterprise. An intelligent software agent platform, coupled to the communication input port, may listen for a trigger indication from the software continuous integration/deployment pipeline. Responsive to the trigger indication, the intelligent software agent platform may apply system configuration information and rule layer information to extract software log data and apply a machine learning model to the extracted software log data to generate a pipeline health check analysis report. The pipeline health check analysis report may include, for example, an automatically generated prediction associated with future operation of the software continuous integration/deployment pipeline.

Abstract: Some embodiments may facilitate software development and operations for an enterprise. A communication input port may receive information associated with a software continuous integration/deployment pipeline of the enterprise. An intelligent software agent platform, coupled to the communication input port, may listen for a trigger indication from the software continuous integration/deployment pipeline. Responsive to the trigger indication, the intelligent software agent platform may apply system configuration information and rule layer information to extract software log data and apply a machine learning model to the extracted software log data to generate a pipeline health check analysis report. The pipeline health check analysis report may include, for example, an automatically generated prediction associated with future operation of the software continuous integration/deployment pipeline.

Abstract: Some embodiments may facilitate software development and operations for an enterprise. A communication input port may receive information associated with a software continuous integration/deployment pipeline of the enterprise. An intelligent software agent platform, coupled to the communication input port, may listen for a trigger indication from the software continuous integration/deployment pipeline. Responsive to the trigger indication, the intelligent software agent platform may apply system configuration information and rule layer information to extract software log data and apply a machine learning model to the extracted software log data to generate a pipeline health check analysis report. The pipeline health check analysis report may include, for example, an automatically generated prediction associated with future operation of the software continuous integration/deployment pipeline.

Abstract: Some embodiments may facilitate software development and operations for an enterprise. A communication input port may receive information associated with a software continuous integration/deployment pipeline of the enterprise. An intelligent software agent platform, coupled to the communication input port, may listen for a trigger indication from the software continuous integration/deployment pipeline. Responsive to the trigger indication, the intelligent software agent platform may apply system configuration information and rule layer information to extract software log data and apply a machine learning model to the extracted software log data to generate a pipeline health check analysis report. The pipeline health check analysis report may include, for example, an automatically generated prediction associated with future operation of the software continuous integration/deployment pipeline.

Abstract: Some embodiments may facilitate software development and operations for an enterprise. A communication input port may receive information associated with a software continuous integration/deployment pipeline of the enterprise. An intelligent software agent platform, coupled to the communication input port, may listen for a trigger indication from the software continuous integration/deployment pipeline. Responsive to the trigger indication, the intelligent software agent platform may apply system configuration and rule layer information to extract software log data and apply a machine learning model to the extracted software log data to generate a pipeline health check analysis report. The pipeline health check analysis report may include, for example, an automatically generated prediction associated with future operation of the software continuous integration/deployment pipeline.