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.

Abstract: Methods and systems are described for utilizing machine learning models to generate a response. The system receives an operator input requesting actions to prompt a desired user interaction. The system selects user-specific machine learning models trained on interactions associated with each user of a plurality of users. Based on the operator input, the system determines a model subset and selects a candidate operator action to test. The system inputs the candidate action into each model of the subset to obtain a predicted user interaction. The system then assesses whether the candidate action causes the models to predict the desired interaction. If the candidate action is effective, the system executes the candidate action in relation to the user subset.

Abstract: Systems, methods, and devices that relate to monitoring and managing autonomous agents are disclosed. In one example aspect, the method includes receiving activity data from autonomous agents in an operational environment, deploying static and dynamic observing agents to monitor expected behavior and deviations, detecting a deviation by an autonomous agent, determining the cause through analysis, performing a mitigative action based on the cause, and executing a preventative action to block similar future deviations. The method may also involve configuring observing agents with different observation modalities, periodically modifying observation parameters unpredictably, facilitating direct communication between observing agents, resolving conflicts in observations, and updating observation policies. Mitigative actions can include disabling credentials, rerouting communications, and logging actions.

Abstract: The systems and methods disclosed herein orchestrate task execution among autonomous (or semi-autonomous) AI agentic models (“agents”) responsive to a received query by using a hierarchical semantic fingerprinting framework to generate semantic-aware fingerprints for the query and agents. Queries and descriptions of agent capabilities are processed by a series of hierarchical levels using locality-sensitive hash (LSH) functions, where subsequent layers encode more complex semantic information and generate longer hash values. The hash values are aggregated into a semantic fingerprint. A bloom filter cascade uses a series of increasingly accurate hierarchical bloom filters to reject agent fingerprints that differ from the query fingerprint. The remaining agent fingerprints are compared bitwise to the query fingerprint, and those closest to the query fingerprint are selected to generate a routing path for the query. Responses from the selected agents are aggregated into an output that is responsive to the input.

Abstract: Presented herein are systems and methods for aggregating data from disparate sources using generative models. A computing system may retrieve, from a plurality of data sources, a first plurality of datasets for at least one of a plurality of features for a network environment over a first time period. The computing system may execute a first generative model using the first plurality of datasets to create a second plurality of datasets. The computing system may identify, from the second plurality of datasets, at least one dataset corresponding to a feature of the plurality of features. The one or more processors may execute select at least one machine learning model based on the feature. The computing system may execute the at least one ML model using the dataset to determine an output. The computing system may cause displaying of a visualization of the output via a graphical user interface.

Abstract: A computer can monitor network traffic on a blockchain computing network. The computer can determine a current level of network congestion on the blockchain computing network. The computer can execute a first machine learning model that predicts a timeseries of future transaction costs based on historical data and the current level network congestion level of the blockchain computing network.  The computer can also execute a second machine learning model to predict a timeseries of future transaction sizes and UTXO types for the distributed ledger-based account based on historical transaction data.  The computer can select one or more UTXOs to use to complete the transaction of the transaction request.  The computer can append a block instance containing an identification of the selected one or more UTXOs to the blockchain to complete the transaction.

Abstract: Systems and methods for managing resources across a global and/or cloud network. In particular, systems and methods for mitigating issues related to providing services while resources are off-line (or may potentially go off-line). For example, the systems and methods may mitigate issues related to providing services while resources are off-line (or may potentially go off-line) by monitoring network services at an aggregate level.

Abstract: Presented herein are systems and methods for the employment of machine learning models for image processing. A method may include a capture of a video feed including image data of a document at a client device. The client device can provide the video feed to another computing device. The method can include, by the client device or the other computing device object recognition for recognizing a type of document and capturing an image exceeding a quality threshold of the document amongst the frames within the video feed. The method may further include the execution of other image processing operations on the image data to improve the quality of the image or features extracted therefrom. The method may further include anti-fraud detection or scoring operations to determine an amount of risk associated with the image data.

Abstract: The systems and methods disclosed herein receive a dataset including an observed set of values for a set of variables. The system can use a first set of AI models to identify a set of anomalies in the observed set of values by comparing an observed set of patterns against multiple reference patterns. The system can use a second set of AI models to evaluate the identified anomalies by comparing an observed set of association rules with an expected set of association rules. The system can use a third set of AI models to generate reconfiguration commands to remove the identified anomalies. The reconfiguration commands can be automatically executed to modify the observed association rules to align with the expected association rules.

Abstract: Methods and systems for routing execution requests to autonomous artificial intelligence agents using performance-based selection are disclosed herein. An agent routing system may receive a prompt for completing an action using artificial intelligence agents from a plurality of agents, where each agent may be associated with computer-executable operations configured for autonomous execution on software applications. The system may determine a set of agents enabled to complete the requested action and may access action completion datasets for these agents. Each dataset may include action parameters for completed actions, including status parameters indicating successful completion and timestamps of completion. The system may select an artificial intelligence agent based on a frequency composite parameter generated from successful completion status parameters and a time composite parameter indicating completion timing.