Abstract: Systems, methods, and devices for monitoring and controlling communications between autonomous agents are disclosed. The system monitors real-time communications between autonomous agents, intercepting and recording each communication. Communications are translated to a standardized language and processed through communication protocol filters that evaluate compliance with predefined operational policies. The system parses each translated communication to identify policy violations. When violations are detected, the system modifies communications to ensure compliance with operational policies. All communications and modifications are recorded via distributed ledger technology for audit and accountability purposes. This approach enables comprehensive oversight of autonomous agent interactions while maintaining tamper-proof records of all monitoring and control activities.

Abstract: Systems and methods for monitoring emerging faults at a network system are disclosed herein. The system may receive, such as from tool-based monitoring systems, data including measurements and testing results. The system may input an operational metric dataset into a fault detection machine learning model to obtain an indication of and degree of the fault condition at the network system. Responsive to determining that the degree of the fault condition indicates uncertainty of whether fault has occurred, the system may identify second commands. Based on transmitting the second commands, additional parameters may be obtained for generating an augmented dataset. The system may input the augmented dataset into the fault detection machine learning model to obtain an updated indication of the fault condition and updated degree of the fault condition and transmit an alert to an operator device.

Abstract: Systems and methods for secure data-retrieval are disclosed herein. A system for secure data retrieval initializes a session between a user-specific model executing locally at a user device and an entity-specific model at an entity device. The user-specific model has access to a locally stored ontology comprising interaction data. The system can receive a request from the entity device, where resolving the request requires access to the interaction data from the ontology. The system inputs the request into the user-specific model to generate a query for obtaining interaction data from one or more classes of the ontology associated with the request. The system can generate a set of filtering operations, which can be applied to the interaction data to obtain filtered data. The system can use the filtered data to generate and transmit a response.

Abstract: Systems, methods, and devices for error detection and remediation in software applications are disclosed. The system receives functional specifications and user requirement specifications for a software application. A prompt is generated for a generative machine learning model to output dynamic parameters that enable error detection within software elements during application generation phases. These dynamic parameters comprise application-specific rules based on the specifications. The prompt is input into the model to output dynamic parameters. Quality metrics are generated for software elements using these parameters, indicating conformity to specifications. Errors are detected when quality metrics show software elements fail to conform to requirements. Modifications are generated to remediate errors and then automatically implemented in the software elements.

Abstract: Disclosed herein are systems, methods, and computer-readable media for generating and transmitting event artifacts based on events that are detected using artificial intelligence (AI) agents. One or more AI agents can monitor a plurality of sources to detect events associated with available interactions with one or more public entities. In response to detecting an occurrence of a first event, the one or more AI agents can assign a first priority metric to the first event. The first priority metric can be based on particular properties of a primary entity. In response to the first priority metric falling within a first predetermined metric range, the one or more AI agents can generate a first event artifact based on the first event and the properties of the primary entity. The first event artifact can be transmitted to the primary entity via one or more engagement channels of a multimodal interface.

Abstract: The present disclosure provides a system for securing communications between services in different computing environments. A pair-wise secret key may be generated at an inspection layer for a first service in a first environment and a second service in a second environment. Each service may be associated with an inspection layer component, and the pair-wise secret key is provided to both components. When a communication with encrypted payload is received from the first service, an initial decryption vector may be determined using the communication, creating a unique vector for each communication. A unique decryption key may be generated combining the decryption vector and pair-wise secret key. The encrypted payload may then be decrypted using this unique key, and the decrypted communication may be transmitted to the second service for processing.

Abstract: Presented herein are systems and methods for the employment of machine learning models for image processing. A mobile application for client-side image processing and validation, which interacts with and leverages native image processing software of the client device, where the image processing software and the mobile application include any number of machine-learning models for identifying a document and attributes of the document for recognition and validation. This mobile application uses the image processing software from a client operating system to control the camera. The image processing software generates various types of information about a video frame and the document, and the mobile application invokes APIs or software libraries of the image processing software to access the information and validate the frame and document.

Abstract: Systems and methods for generating and testing on-chain programs are disclosed herein. A system for generating on-chain programs may receive a request to generate on-chain program code for an on-chain program and determine a model required for the program. The system may retrieve functions associated with the model and determine blockchain operations to be added to the program. The system may determine parameters for blockchain operations and generate on-chain program code. A system for testing on-chain programs may install an on-chain program onto a test blockchain and retrieve functions associated with a model generated for testing. The system may generate blockchain operations for testing and input each operation into a cryptography-based storage application. The system may cause execution of the operations and receive results. Based on determining that a number of failed results exceed a threshold, the system may generate a notification.

Abstract: Presented herein are systems and methods for the employment of machine learning models for image processing. A mobile application for client-side image processing and validation, which interacts with and leverages native image processing software of the client device, where the image processing software and the mobile application include any number of machine-learning models for identifying a document and attributes of the document for recognition and validation. This mobile application uses the image processing software from a client operating system to control the camera. The image processing software generates various types of information about a video frame and the document, and the mobile application invokes APIs or software libraries of the image processing software to access the information and validate the frame and document.

Abstract: Presented herein are system and methods for integrating application programming interfaces (APIs) for use in network environments. A service of an API management platform may receive, from an administrator device, a request to deploy an API for use in a network environment among one or more applications. The request may include a specification defining the API according to a template for a domain of a plurality of domains. The service may identify, from a plurality of policies corresponding to the plurality of domains, a policy based on the domain for the template with which the specification of the request is defined. The service may determine that the API is validated in accordance with the policy for the domain. The service may generate an indication of approval of the API for use in the network environment among the one or more applications, responsive to determining that the API is validated.

Abstract: Presented herein are systems and methods for the employment of machine learning models for image processing as may be performed by computing devices associated with an end user. A method may include obtaining video data comprising a plurality of frames including a document of a document type. The method may include executing an object recognition engine of a machine-learning architecture using image data of the plurality of frames, the object recognition engine trained to detect edges of documents. The method may include identifying, based on the edge detection, a plurality of boundaries for the document. The method may include validating, based on the plurality of boundaries, the document as the document type. The method may include transmitting via one or more networks, to a computer remote from the computing device, responsive to the validation of the type of document, the image data for the plurality of frames depicting the document.

Abstract: Systems and methods are described herein for novel uses and/or improvements for determining malfunctions within undeployed artificial intelligence agents. A plurality of parameter combinations may be generated from parameters used to train an artificial intelligence agent. The plurality of parameter combinations may be used to generate a plurality of test requests. The plurality of test requests may be input into the undeployed artificial intelligence agent and one or more deployed artificial intelligence agents. The outputs of the agents may be compared to determine a context differential. The context differential may be used to determine one or more categories where the undeployed artificial intelligence agent's performance is deficient.

Abstract: Systems and methods for dynamically managing application programming interface (API) resource allocation and rate limits are disclosed herein. An API monitoring system receives usage monitoring data for APIs, where each API is associated with a corresponding rate limit. A machine learning model processes the usage monitoring data to predict time periods when API usage is expected to exceed rate limits. Based on determining that a matching time period has been predicted for a subset of oversubscribed APIs, the system retrieves resource allocation data and identifies undersubscribed APIs from which resources may be reallocated. The system dynamically adjusts memory, processing power, or network bandwidth allocations and updates corresponding rate limits to balance API demand. By proactively reallocating resources and adjusting rate limits, the system optimizes API performance, prevents failures, and ensures efficient utilization of computing resources.

Abstract: The model generation platform enables generation of machine learning models based on modular storage of user-specified training data. The platform can obtain a dataset from a different heterogenous source (e.g., a structured database, an unstructured database, a semi-structured file system, manual upload of a comma-separated value file, a spreadsheet, and/or big data) through an associated application programming interface and store this data in a first storage medium. The model generation platform can obtain an indication of a portion of the dataset from a user via a user interface and determine a second storage medium for this portion of the dataset based on an associated estimated performance metric. In response to a request for generation of a machine learning model, the model generation platform can generate a machine learning model using training data comprising a subset of the portion of the dataset.

Abstract: The present disclosure provides a malfunction interdiction system with monitoring, detection, and response capabilities specifically designed for distributed AI environments. The system may first detect any AI agents that are malfunctioning and then remediate any malfunctions via quarantine and/or AI agent termination. Upon detection of a malfunction, the system may automatically isolate the compromised agent through a quarantine process. The system may alternatively or additionally, initiate graduated deactivation procedures ranging from partial restrictions to full deactivation.

Abstract: Systems and methods for enabling operation tracking using graphical interfaces are disclosed. The system receives operational files from multiple operation management systems and transforms operations from native to standard encoding. A first graphical interface displays selectable operational file indicators. Upon user selection of a file, the system identifies the corresponding operation management system and generates a second interface with selectable operational indicators. When a user selects an operation indicator, the system determines available functions based on the operation management system. A third interface displays operational details and distinguishes between available and unavailable functions through selectable and non-selectable indicators. This approach enables users to efficiently manage operations across different systems through a unified interface that adapts based on operational context.

Abstract: Systems and methods are disclosed comprising techniques for agentic cross-verification in distributed systems, such as obtaining a request to validate a decision state for a first memory structure of the first multi-agent storage that is accessible to a first AI agent set, transmitting a verification artifact representing the decision state of the first multi-agent storage to a second multi-agent storage that stores the verification artifact, receiving a verification status of the verification artifact that is determined by the second AI agent set, generating a first programmatic workflow set configured to trigger a first sequence of computer-executable commands at the first memory structure, generating a second programmatic workflow set configured to trigger a second sequence of computer-executable commands at the second memory structure, and automatically executing the first programmatic workflow set using the first AI agent set and the second programmatic workflow set using the second AI agent set.

Abstract: Methods and systems for performing database modifications based on event parameters are disclosed herein. The system may receive a resource event comprising one or more event parameters. The system may determine a set of entities associated with the resource event. The system may validate the resource event using the one or more event parameters. The system may determine a plurality of user accounts associated with the resource event. The system may modify resource distributions for each user account of the plurality of user accounts to generate modified resource distributions. The system may transmit, to a central database, one or more commands to update a central record of the resource distributions.

Abstract: Systems and methods are disclosed comprising techniques for agentic cross-verification in distributed systems, such as obtaining a request to validate a decision state for a first memory structure of the first multi-agent storage that is accessible to a first AI agent set, transmitting a verification artifact representing the decision state of the first multi-agent storage to a second multi-agent storage that stores the verification artifact, receiving a verification status of the verification artifact that is determined by the second AI agent set, generating a first programmatic workflow set configured to trigger a first sequence of computer-executable commands at the first memory structure, generating a second programmatic workflow set configured to trigger a second sequence of computer-executable commands at the second memory structure, and automatically executing the first programmatic workflow set using the first AI agent set and the second programmatic workflow set using the second AI agent set.

Abstract: Systems and methods are described herein for novel uses and/or improvements for autonomous configuration of network applications on a network. When a request is received for adding a new application to the network, the system may analyze the request and identify which network devices need a change in configuration. The system may also determine, based on the request, what type of changes are needed. The system may then generate one or more commands for each identified network device and transmit those commands to each device for performing automatic network configuration for the application.