Abstract: Systems and methods for automatically modifying a process by an AI (artificial intelligence) agent for adjusting user input of the process are provided. Process data associated with a process is received. The process is automatically modified based on the process data by the AI agent to adjust user input of the process. The modified process is output.

Abstract: Automation generation, execution validation, and healing by artificial intelligence (AI) agents is disclosed. In order to generate an automation, a user enters a description of the desired task to accomplish into a prompt or some other description and the AI agent attempts to perform the task by employing a screen understanding and document object model (DOM) extraction engine to extract screen content and/or using an object repository. A user may validate the steps that the AI agent will take when it executes before deploying the automation. AI agents may also be used to heal existing robotic process automations (RPA), application programming interface (API) automations, agentic orchestration processes (AOPs), etc. by searching the object repository for solutions for an RPA workflow and then also applying these solutions to other RPA workflows and then also applying the solutions to similar activities of other RPA workflows.

Abstract: Systems and methods for automatically requesting user input for performing a task by an AI (artificial intelligence) agent are provided. The AI agent is executed on a computing system for performing a task. It is determined, by the AI agent, whether to request user input during the performance of the task. In response to determining to request the user input, a particular user to request the user input from is identified by the AI agent. A request for the user input from the particular user is generated by the AI agent based on the task. The request for the user input is sent to the particular user. The performance of the task is continued by the AI agent based on the user input received from the particular user.

Abstract: Automation generation, execution validation, and healing by artificial intelligence (AI) agents is disclosed. In order to generate an automation, a user enters a description of the desired task to accomplish into a prompt or some other description and the AI agent attempts to perform the task by employing a screen understanding and document object model (DOM) extraction engine to extract screen content and/or using an object repository. A user may validate the steps that the AI agent will take when it executes before deploying the automation. AI agents may also be used to heal existing robotic process automations (RPA), application programming interface (API) automations, agentic orchestration processes (AOPs), etc. by searching the object repository for solutions for an RPA workflow and then also applying these solutions to other RPA workflows and then also applying the solutions to similar activities of other RPA workflows.

Abstract: Systems and methods for configuring an RPA (robotic process automation) platform to perform a candidate process automation are provided. Discovery data relating to a candidate process automation is generated. RPA platform design components for configuring an RPA platform to perform the candidate process automation are generated based on the discovery data. The RPA platform design components are presented to a user via a user interface.

Abstract: In some embodiments, an RPA design interface exposes a set of DevOps activities for creating a DevOps pipeline. Exemplary DevOps activities include activities for pulling a target RPA workflow from a code repository, testing, building, and deploying the respective RPA workflow to a client's production environment. The pipeline may then be executed by conventional RPA robots, as any other RPA workflow. Endowing an RPA design tool with DevOps functionality substantially facilitates RPA development by enabling RPA developers to carry out DevOps without requiring specialized knowledge of DevOps software. Some embodiments further enable RPA developers to create complex DevOps pipelines combining DevOps activities with other robotic activities, such as interacting with GUIs, spreadsheets, and electronic communication applications, among others.

Abstract: An end-to-end automation platform is disclosed that provides a common interface for building applications, data services, and integrations (e.g., processes, queues, entities, connections, etc.). Unified build/project provides a single seamless way of building potentially complex end-to-end automations in certain embodiments, which can be done significantly faster with minimum configuration. The user can build an application in a robotic process automation (RPA) development platform and create workflows in the same interface without leaving the project. The user can leverage the array of activities available in the RPA development platform for handling events in the application, such as button clicks, dropdown value changes, page loads, etc. The user may also build and publish the whole project as a single unit in a conductor application and seamlessly deploy applications along with the attached workflows with a single action.

Abstract: Web-based robotic process automation (RPA) designer systems that allow RPA developers to design and implement web serverless automations, user interface (UI) automations, and other automations are disclosed. Such web-based RPA designer systems may allow a developer to sign in through the cloud and obtain a list of template projects, developer-designed projects, services, activities, etc. Thus, RPA development may be centralized and cloud-based, reducing the local processing and memory requirements on a user's computing system and centralizing RPA designer functionality, enabling better compliance. Automations generated by the web-based RPA designer systems may be deployed and executed in virtual machines (VMs), containers, or operating system sessions.

Abstract: User interface (UI) object descriptors, UI object libraries, UI object repositories, and UI object browsers for robotic process automation (RPA) are disclosed. A UI object browser may be used for managing, reusing, and increasing the reliability of UI descriptors in a project. UI descriptors may be added to UI object libraries and be published or republished as UI object libraries for global reuse in a UI object repository. The UI object browser, UI object libraries, and UI object repository may facilitate reusability of UI element identification frameworks and derivatives thereof.

Abstract: Automatic annotations and technical specification generation for robotic process automation (RPA) workflows using artificial intelligence (AI) is disclosed. AI/ML models may enable smart searching of workflows and automatically generate documentation for the workflows, including descriptions of each activity, input/output parameters, and overall process explanations. Annotations and documentation may be provided for an entire complex business automation that is the sum of multiple workflows and applications. A Process Definition Document (PDD) for the business process may be generated from the RPA workflow code itself when it does not exist. Other documents, such as audit documents, compliance documents required by laws or regulations, etc. may be produced. The process may be iterative, where a generative AI model automatically converts text to RPA workflow code, a runtime automation is produced from this RPA workflow, and the other documentation is generated as well.

Abstract: A design time smart analyzer and a runtime smart handler for robotic process automation (RPA) are disclosed that use a cognitive artificial intelligence (AI) layer to provide suggestions for an RPA workflow and an RPA automation, respectively. The smart analyzer analyzes an RPA workflow under development to check for errors and inefficiencies. The smart handler attempts to optimize and/or repair RPA automations at runtime.

Abstract: A design time smart analyzer and a runtime smart handler for robotic process automation (RPA) are disclosed that use a cognitive artificial intelligence (AI) layer to provide suggestions for an RPA workflow and an RPA automation, respectively. The smart analyzer analyzes an RPA workflow under development to check for errors and inefficiencies. The smart handler attempts to optimize and/or repair RPA automations at runtime.

Abstract: Automatic annotations and technical specification generation for robotic process automation (RPA) workflows using artificial intelligence (AI) is disclosed. AI/ML models may enable smart searching of workflows and automatically generate documentation for the workflows, including descriptions of each activity, input/output parameters, and overall process explanations. Annotations and documentation may be provided for an entire complex business automation that is the sum of multiple workflows and applications. A Process Definition Document (PDD) for the business process may be generated from the RPA workflow code itself when it does not exist. Other documents, such as audit documents, compliance documents required by laws or regulations, etc. may be produced. The process may be iterative, where a generative AI model automatically converts text to RPA workflow code, a runtime automation is produced from this RPA workflow, and the other documentation is generated as well.

Abstract: Automatic code generation for robotic process automation (RPA) that uses a cognitive artificial intelligence (AI) layer to automatically generate computer program code, text, or other items based on an input source is disclosed. Expressions and/or code snippets may be generated for RPA workflows that provide code in a programming language or convert code from one language to another. The input source may include natural language sentence(s), source document(s), pseudocode written by a user for a desired task, etc. These inputs may be used to modify an RPA workflow, generate a new RPA workflow, create a document describing a task, generate an application, etc.

Abstract: Multi-target libraries, projects, and activities for robotic process automation (RPA) are disclosed. Some embodiments multiple target platforms can be handled in the same project. The target platform(s) can be specified at the automation and/or activity level in order to provide the supported functionality for each. This may also allow previously built automations to be applied to new target frameworks without starting from scratch.

Abstract: Automatic data transfer between a source and a target using semantic artificial intelligence (AI) for robotic process automation (RPA) is disclosed. A user may be provided with the option of selecting a source and a target and indicating through an intuitive user interface that he or she would like to copy data from the source to the destination, regardless of format. This may be done at design time or at run time. For instance, the source and/or target may be a web page, a graphical user interface (GUI) of an application, an image, a file explorer, a spreadsheet, a relational database, a flat file source, any other suitable format, or any combination thereof. The source and the target may have different formats. The source, target, or both may not necessarily be visible to the user.

Abstract: Using a cognitive artificial intelligence (AI) layer (e.g., a generative AI model and one or more other AI/ML models) to perform robotic process automation (RPA) robot repair is disclosed. Computer vision (CV) and/or optical character recognition (OCR) models may be called by RPA robots when performing activities in RPA workflows to identify graphical elements and text, respectively, in a user interface (UI). However, the CV or OCR model may not find a graphical element or text targeted by the activity of the RPA workflow, or the RPA robot may encounter an exception and otherwise fail when performing the activity. In such cases, a cognitive AI layer including a generative AI model may be used to find the target graphical element or text, or to address the robot failure.

Abstract: Automatic data transfer between a source and a target using semantic artificial intelligence (AI) for robotic process automation (RPA) is disclosed. A user may be provided with the option of selecting a source and a target and indicating through an intuitive user interface that he or she would like to copy data from the source to the destination, regardless of format. This may be done at design time or at run time. For instance, the source and/or target may be a web page, a graphical user interface (GUI) of an application, an image, a file explorer, a spreadsheet, a relational database, a flat file source, any other suitable format, or any combination thereof. The source and the target may have different formats. The source, target, or both may not necessarily be visible to the user.

Abstract: Systems and methods for configuring an RPA (robotic process automation) platform to perform a candidate process automation are provided. Discovery data relating to a candidate process automation is generated. RPA platform design components for configuring an RPA platform to perform the candidate process automation are generated based on the discovery data. The RPA platform is configured based on the RPA platform design components.

Abstract: An artificial intelligence (AI)/machine learning (ML) recommendation engine for robotic process automation (RPA) is disclosed. An AI/ML model may be trained to provide recommendations for a next activity, a next sequence of activities, and/or modifications to parameters for one or more existing activities to include during RPA workflow development. The recommendations may be based on the context of where the user is in the RPA workflow. For user interface (UI) automations, the AI/ML model may be linked to an object repository and trained to make recommendations therefrom. The AI/ML model may also be trained to generate new UI descriptors for the object repository.