Abstract: Disclosed herein are system, method, and device embodiments for integrating artificial intelligence technologies, including generative AI with a knowledge-driven automation platform, to realize a practical implementation of Neuro-Symbolic AI. The disclosed techniques mediate interactions with artificial intelligences, grounding and enriching the interactions with context in order to optimize the processing of the interactions, the quality of the related outputs of artificial intelligence technologies, and the related actions of the automation platform, which may be part of a larger activity.

Abstract: Disclosed herein are system, method, and computer program product embodiments for performing Telecom Day 0 tasks as concrete examples of the general model-based, event-driven, policy-controlled platform that generates a customized DevOp automation plan from a generic template, which describes a single platform-based workflow for automating related DevOps tasks end-to-end (e.g., across stages and teams) and top-to-bottom (e.g., across all layers—application, infrastructure and network) for an efficient, seamless, transparent and scalable process. The single, platform-based process commences with a submission that is the onboarding of a new or updated code-package or a composition of objects as a service (higher-level object). The single, platform-based process ends with the registration of the application in a catalog or returns the submission to the submitter for re-working. These capabilities automate continuous delivery, enabling agile development with a consistent developer experience.

Abstract: In some embodiments, a real-time event is detected and context is determined based on the real-time event. An application model is fetched based on the context and meta-data associated with the real-time event, the application model referencing a micro-function and including pre-condition and post-condition descriptors. A graph is constructed based on the micro-function. The micro-function is transformed into micro-capabilities by determining a computing resource for execution of a micro-capability by matching pre-conditions and post-conditions of the micro-capability, and enabling execution and configuration of the micro-capability on the computing resource by providing access in a target environment to an API capable of calling the micro-capability to configure and execute the micro-capability. A request is received from the target environment to execute and configure the micro-capability on the computing resource.

Abstract: In some embodiments, a real-time event is detected and context is determined based on the real-time event. An application model is fetched based on the context and meta-data associated with the real-time event, the application model referencing a micro-function and including pre-condition and post-condition descriptors. A graph is constructed based on the micro-function. The micro-function is transformed into micro-capabilities by determining a computing resource for execution of a micro-capability by matching pre-conditions and post-conditions of the micro-capability, and enabling execution and configuration of the micro-capability on the computing resource by providing access in a target environment to an API capable of calling the micro-capability to configure and execute the micro-capability. A request is received from the target environment to execute and configure the micro-capability on the computing resource.

Abstract: An example method comprises receiving one or more published events by an event hook application program interface (API) from one or more client applications, passing a model to a web server configured to generate web containers in concurrent threads, receiving, by any number of worker nodes, each web container, each of the worker nodes including a system agent program for dynamically assigned functions, the web containers being provided to the any number of worker nodes for logical isolation of system agent execution in memory, and performing the dynamically assigned functions by the system agent program in a blackboard memory, the blackboard memory being a shared memory with non-blocking reads and writes and performing functionality, the dynamically assigned functions being executed in parallel and at least two of the dynamically assigned functions sharing context between inter-dependent processes.

Abstract: A solution providing for the dynamic design, use, and modification of models is provided. The solution can receive an electronic communication identifying a request or event and process the electronic communication in a runtime environment by binding a model of the collection of models to dynamically construct an implementation of the model. Collective properties of the set of related models can emerge dynamically. The binding can comprise late-binding of an application associated with the collection of models to enable at least one user to perform at least one interaction using the environment without disrupting any of the environment or the application.

Abstract: Receive an order, the order indicating a network service model. Identify a context of the order based on metadata. Generate, using the network service model, a deployment plan, the deployment plan facilitating an instantiation of a contextually-motivated network service instance as a set of normalized lifecycle management (LCM) operations performed against at least one of a plurality of associated service entities.

Abstract: In some embodiments, a real-time event is detected and context is determined based on the real-time event. An application model is fetched based on the context and meta-data associated with the real-time event, the application model referencing a micro-function and including pre-condition and post-condition descriptors. A graph is constructed based on the micro-function. The micro-function is transformed into micro-capabilities by determining a computing resource for execution of a micro-capability by matching pre-conditions and post-conditions of the micro-capability, and enabling execution and configuration of the micro-capability on the computing resource by providing access in a target environment to an API capable of calling the micro-capability to configure and execute the micro-capability. A request is received from the target environment to execute and configure the micro-capability on the computing resource.

Abstract: A context of one or more interactions is determined. Base objects are transformed into interpreted objects by interpreting the base objects based on evaluation of the context, and by resolving references of the base objects relative to domain model types and concepts, each of the base objects modeled using a same declarative modeling language, the same declarative modeling language enabling transitions between the interpreted objects, at least one of the interpreted objects including at least one post-condition providing hooks for transition policies which allow the at least one of the interpreted objects to be logically chained in a non-linear process. Transitioning between at least two of the interpreted objects by chaining the at least two interpreted objects based on a particular post-condition of a particular interpreted object to create at least a portion of a particular non-linear process. At least a portion of the particular non-linear process is executed.

Abstract: An example method comprises receiving one or more published events by an event hook application program interface (API) from one or more client applications, passing a model to a web server configured to generate web containers in concurrent threads, receiving, by any number of worker nodes, each web container, each of the worker nodes including a system agent program for dynamically assigned functions, the web containers being provided to the any number of worker nodes for logical isolation of system agent execution in memory, and performing the dynamically assigned functions by the system agent program in a blackboard memory, the blackboard memory being a shared memory with non-blocking reads and writes and performing functionality, the dynamically assigned functions being executed in parallel and at least two of the dynamically assigned functions sharing context between inter-dependent processes.

Abstract: An order is received indicating a network service model. A context of the order is identified. A deployment plan is generated using the network service model, the deployment plan facilitating an instantiation of a contextually-motivated network service instance as a set of normalized lifecycle management (LCM) operations performed against each of a plurality of associated service entities. The deployment plan is deployed, the deploying including binding each of the normalized LCM operations, based on the context of the order, to one or more respective micro-capabilities, each of the respective micro-capabilities having previously been onboarded to the system as one or more corresponding modeled objects capable of being declaratively composed, each of the corresponding modeled objects including a mapping of object properties, object behaviors, and standard LCM operations to one or more existing micro-capabilities of the system.

Abstract: Receive an order, the order indicating a network service model. Identify a context of the order based on metadata. Generate, using the network service model, a deployment plan, the deployment plan facilitating an instantiation of a contextually-motivated network service instance as a set of normalized lifecycle management (LCM) operations performed against at least one of a plurality of associated service entities.

Abstract: In some embodiments, a real-time event is detected and context is determined based on the real-time event. An application model is fetched based on the context and meta-data associated with the real-time event, the application model referencing a micro-function and including pre-condition and post-condition descriptors. A graph is constructed based on the micro-function. The micro-function is transformed into micro-capabilities by determining a computing resource for execution of a micro-capability by matching pre-conditions and post-conditions of the micro-capability, and enabling execution and configuration of the micro-capability on the computing resource by providing access in a target environment to an API capable of calling the micro-capability to configure and execute the micro-capability. A request is received from the target environment to execute and configure the micro-capability on the computing resource.

Abstract: A context of one or more interactions is determined. Base objects are transformed into interpreted objects by interpreting the base objects based on evaluation of the context, and by resolving references of the base objects relative to domain model types and concepts, each of the base objects modeled using a same declarative modeling language, the same declarative modeling language enabling transitions between the interpreted objects, at least one of the interpreted objects including at least one post-condition providing hooks for transition policies which allow the at least one of the interpreted objects to be logically chained in a non-linear process. Transitioning between at least two of the interpreted objects by chaining the at least two interpreted objects based on a particular post-condition of a particular interpreted object to create at least a portion of a particular non-linear process. At least a portion of the particular non-linear process is executed.

Abstract: An example method comprises receiving one or more published events by an event hook application program interface (API) from one or more client applications, passing a model to a web server configured to generate web containers in concurrent threads, receiving, by any number of worker nodes, each web container, each of the worker nodes including a system agent program for dynamically assigned functions, the web containers being provided to the any number of worker nodes for logical isolation of system agent execution in memory, and performing the dynamically assigned functions by the system agent program in a blackboard memory, the blackboard memory being a shared memory with non-blocking reads and writes and performing functionality, the dynamically assigned functions being executed in parallel and at least two of the dynamically assigned functions sharing context between inter-dependent processes.

Abstract: An order is received indicating a network service model. A context of the order is identified. A deployment plan is generated using the network service model, the deployment plan facilitating an instantiation of a contextually-motivated network service instance as a set of normalized lifecycle management (LCM) operations performed against each of a plurality of associated service entities. The deployment plan is deployed, the deploying including binding each of the normalized LCM operations, based on the context of the order, to one or more respective micro-capabilities, each of the respective micro-capabilities having previously been onboarded to the system as one or more corresponding modeled objects capable of being declaratively composed, each of the corresponding modeled objects including a mapping of object properties, object behaviors, and standard LCM operations to one or more existing micro-capabilities of the system.

Abstract: A solution providing for the dynamic design, use, and modification of models is provided. The solution can receive an electronic communication identifying a request or event and process the electronic communication in a runtime environment by binding a model of the collection of models to dynamically construct an implementation of the model. Collective properties of the set of related models can emerge dynamically. The binding can comprise late-binding of an application associated with the collection of models to enable at least one user to perform at least one interaction using the environment without disrupting any of the environment or the application.

Abstract: A software application includes work order resources, each of which defines an atomic operation for the software application, and a construction service resource, which processes the work order resources in response to all interaction requests for the software application. Each interaction request is received from a client and identifies a corresponding work order, which the construction service processes to dynamically construct a set of deliverables, which can include a custom representation of the work order. While processing the interaction request, the construction service, as directed by the work order, can make one or more requests to context resources for context information corresponding to an activity for which the interaction was requested to construct the set of deliverables.

Abstract: A solution providing for the dynamic design, use, and modification of models using a declarative software application meta-model that provides for self-modification of a collection of the models is provided. The solution can enable continuous real-time testing, simulation, deployment, and modification of the collection of the models. A model in the collection of the models can represent an entity or a function and can be included in a set of related models. Additionally, a set of related models can include a plurality of sets of related models. The collection of the models can represent, for example, one or more software applications, processes, and/or the like.

Abstract: In some embodiments, a real-time event is detected and context is determined based on the real-time event. An application model is fetched based on the context and meta-data associated with the real-time event, the application model referencing a micro-function and including pre-condition and post-condition descriptors. A graph is constructed based on the micro-function. The micro-function is transformed into micro-capabilities by determining a computing resource for execution of a micro-capability by matching pre-conditions and post-conditions of the micro-capability, and enabling execution and configuration of the micro-capability on the computing resource by providing access in a target environment to an API capable of calling the micro-capability to configure and execute the micro-capability. A request is received from the target environment to execute and configure the micro-capability on the computing resource.