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: Presented herein are system and methods for establishing data provenance by generating one-time signatures. A system may include one or more processors that receive, via an application programming interface (API) request, a request for a one-time signature and data associated with the request, provide a seed identifier and the data associated with the request to an HSM in a set of HSMs, and receive a response message from the HSM, the response message including a one-time signature. In examples, the response message and the one-time signature are provided to the device that transmitted the request for the one-time signature and the data associated with the request. Methods and non-transitory computer-readable mediums are also presented.

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: Metadata corresponding to a set of conditions applicable to an exchange to be performed among a plurality of entities is obtained, the set of conditions determined using one or more trained artificial intelligence models. A set of application programming interfaces (APIs) are generated based, at least in part, on the set of conditions. One or more points to insert the set of APIs to an executable application code to be executed for the exchange are determined. The set of APIs to be called during runtime of the executable application code to implement the set of conditions.

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.

Abstract: Systems and methods for uses and/or improvements to data transfers that mitigate and/or avoid the metadata overwrites. For example, systems and methods for data transfer operations such as those involving, but not limited to, performing multicomputer data transferring across computer networks without staging servers, synthetically generating metadata subject to multicomputer data transferring across computer networks, or performing multicomputer data transfer across computer networks for real-time metadata ingestion. The systems and methods first load data into a buffer and then stream it directly to the target location. By performing data transfers by first loading data into a buffer and then streaming it directly to the target location, the system avoids many metadata-preservation challenges because the data is treated as a raw byte stream rather than as an object being rewritten by the storage system.

Abstract: Systems and methods for executing multi-party validations for complex communications across multiple computer networks using bifurcated digital signing processes. For example, the system may receive a first request, at a bifurcated digital signing process, to perform a first multi-party validation action, wherein the first request has a first processing characteristic. The system may, in response to receiving the first request, process the first request with a first signing routine of the bifurcated digital signing process. The system may determine, based on the first signing routine, whether the first processing characteristic corresponds to a predetermined processing characteristic. The system may, in response to determining that the first processing characteristic corresponds to the predetermined processing characteristic, process the first request with a second signing routine of the bifurcated digital signing process to perform the first multi-party validation action.

Abstract: A system for handling interrupts in a cloud computing environment is disclosed. The system may include a controller process that receives an interrupt indicator from a worker process, signaling a potential interruption. The controller may determine if the worker process is expected to be halted and removed within a predetermined time period. If so, the controller may transmit a status request to the worker process. In response, the controller may receive completed task results and determine uncompleted tasks. The controller may generate a checkpoint data structure indicating completed and uncompleted tasks. A computational payload comprising the completed results and checkpoint data structure may be transmitted for recording within the cloud computing system. This approach may enable efficient management of computational resources and task continuity in cloud environments subject to periodic process halting and removal.

Abstract: The technology may be utilized in network environments that use fiber optic cables to connect components. Components of the network perform functions as transceivers that transmit and receive light signals via the fiber cable. When optic power data received from the transceivers is recognized as a fault, an event is identified. The system logs each received event and converts all measurements into a common unit of measurement. The system analyzes aggregated data from the transceivers to detect patterns and trends, monitor the received data and provide real time fault predictions. For example, a machine learning process may recognize subtle trends or patterns in the data, and use the recognition to predict potential failures. The system uses the received data to create a graphical user interface (“GUI”) that represents the health of the network.

Abstract: Systems and methods for uses and/or improvements to data transfers that mitigate and/or avoid the metadata overwrites. For example, systems and methods for data transfer operations such as those involving, but not limited to, performing multicomputer data transferring across computer networks without staging servers, synthetically generating metadata subject to multicomputer data transferring across computer networks, or performing multicomputer data transfer across computer networks for real-time metadata ingestion. The systems and methods first load data into a buffer and then stream it directly to the target location. By performing data transfers by first loading data into a buffer and then streaming it directly to the target location, the system avoids many metadata-preservation challenges because the data is treated as a raw byte stream rather than as an object being rewritten by the storage system.

Abstract: A distributed ledger technology application can establish a first connection with a computing application executed by a banking computing infrastructure and a second connection with one or more nodes hosting a distributed ledger via a second communication protocol. The application can receive, from a client device through the through the first connection using the first communication protocol, a receive instruction to complete an asset transfer to transfer an asset from a source account hosted in the banking computing infrastructure to a DLT-based client account. The application can execute an asset conversion agent to determine whether any asset conversion is required. The application can execute an instruction generation agent to generate a new receive instruction. The application can execute a ledger agent to generate an instruction to cause the one or more nodes to add a record of the asset transfer to the distributed ledger.

Abstract: Systems and methods for uses and/or improvements to data transfers that mitigate and/or avoid the metadata overwrites. For example, systems and methods for data transfer operations such as those involving, but not limited to, performing multicomputer data transferring across computer networks without staging servers, synthetically generating metadata subject to multicomputer data transferring across computer networks, or performing multicomputer data transfer across computer networks for real-time metadata ingestion. The systems and methods first load data into a buffer and then stream it directly to the target location. By performing data transfers by first loading data into a buffer and then streaming it directly to the target location, the system avoids many metadata-preservation challenges because the data is treated as a raw byte stream rather than as an object being rewritten by the storage system.

Abstract: A computer-implemented method comprising receiving a transaction request from a first node within the plurality of nodes, the transaction request corresponding to a pending transaction between the first node and a second node; identifying a distributed ledger associated with the pending transaction, the distributed ledger including a first record having a hierarchy file indicating a hierarchy among the first node, the second node, and the third node; identifying a second record having an executable file to approve the transaction request received from the first node; executing the executable file, wherein the executable file is configured to retrieve data from a data source associated with the third node and analyze data to approve or deny the transaction request; and appending a third record comprising the set of transaction attributes to the distributed ledger.

Abstract: Systems and methods for uses and/or improvements to blockchain and blockchain technology, particularly to provide a scalable solution to the aforementioned security and privacy concerns. As one example, systems and methods are described herein for a double-layer restriction subnet architecture that overcomes the technical limitations of conventional blockchains, whether public or permissioned, and subnets thereof. The double-layer restriction subnet architecture comprises a series of permissioned subnets, which provides both the security/privacy benefits of permissioned blockchains but also the scalability of subnet efficiency.

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: 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: The systems and methods disclosed herein monitor application (e.g., artificial intelligence (AI) model) operations using interactions between the application and a kernel. The systems and methods disclosed herein intercept, using a kernel interface, one or more function invocations transmitted from the application (e.g., an AI model without model modification). Event record(s) are generated for one or more functions to define process identifiers, resource interaction types, timestamps, and/or resource identifiers. Observed pattern(s) for the application are identified by comparing current event record(s) with previous record(s), and the identified observed pattern(s) are evaluated against reference pattern(s) to generate score(s). Data packet(s) that indicate observed pattern(s), corresponding score(s), and/or cryptographic digital fingerprint(s) of the one or more functions are generated. The data packet(s) are transmitted to distributed ledgers for immutable storage.

Abstract: Systems and methods disclosed herein perform privacy-preserving evaluations of artificial intelligence (AI) agents. The system identifies an auditing AI agent from a set of auditing AI agents for assessing target AI agent sets. The system obtains a data structure that defines operative boundaries for a target AI agent set and generates a reference value by applying a first transformation operation set on the data structure. The system transmits the reference value to a multi-agent storage and receives, via the multi-agent storage, a verification artifact from the target AI agent set that indicates an observed value generated by applying a second transformation operation set on an artifact set generated by the target AI agent set. The system determines, via the auditing AI agent, a verification status and responsive to a particular artifact failing to satisfy one or more assessment metrics, generates an action set to modify the target AI agent set.

Abstract: The systems and methods disclosed herein monitor application (e.g., artificial intelligence (AI) model) operations using interactions between the application and a kernel. The systems and methods disclosed herein intercept, using a kernel interface, one or more function invocations transmitted from the application (e.g., an AI model without model modification). Event record(s) are generated for one or more functions to define process identifiers, resource interaction types, timestamps, and/or resource identifiers. Observed pattern(s) for the application are identified by comparing current event record(s) with previous record(s), and the identified observed pattern(s) are evaluated against reference pattern(s) to generate score(s). Data packet(s) that indicate observed pattern(s), corresponding score(s), and/or cryptographic digital fingerprint(s) of the one or more functions are generated. The data packet(s) are transmitted to distributed ledgers for immutable storage.

Abstract: The systems and methods disclosed herein orchestrate memory sharing among autonomous (or semi-autonomous) AI agentic models (“agents”) by dynamically organizing agent memories based on operational characteristics, taxonomic classifications, and/or other factors. Received operational data (e.g., token sets) are mapped to predefined taxonomic nodes (e.g., taxonomic node subsets) based on a degree of similarity between the operational data and predefined taxonomic nodes. An ontological reference pointer is determined for the operational data based on a position of the taxonomic node subset within the ontological data structure. The operational data is compressed into a memory capsule data structure using the ontological reference pointer. A blockchain transaction identifier is generated for the memory capsule data structure and stored in a distributed network accessible to multiple agents.

Abstract: Machines, devices, and other objects are configured to use authorization tokens to verify object identities without human input. In examples, the object uses a password to validate the object's identity to an authorization server to obtain an access token for use in multiple applications. In another example, the object uses a certificate to validate the object's identity to an authorization server to obtain an access token. In other examples, any other suitable identifying data may be used to validate the object's identity to an authorization server to obtain an access token. The process of using passwords, certificates, or other validation processes to obtain tokens or other authorization mechanisms allows the object to authenticate themselves without human interaction and to use a single identity to access services from multiple service providers that trust a central authorization server.

Abstract: Methods and systems for cloud-based resource transmission are disclosed. A system may receive a resource allocation message from a computing device in a source encoding. The message may be transformed into a destination encoding using a transformation map to generate an encoded message. The encoded message may be transmitted to a message processing system. An error message with an error code may be received in response. The system may determine which message caused the error based on the error code. It may be determined that the first computing device caused the error. An indication of the error code may be transmitted to the first computing device. This approach may enable efficient handling and error resolution for resource allocation messages across multiple encodings and devices.

Abstract: The systems and methods disclosed herein monitor application (e.g., artificial intelligence (AI) model) operations using interactions between the application and a kernel. The systems and methods disclosed herein intercept, using a kernel interface, one or more function invocations transmitted from the application (e.g., an AI model without model modification). Event record(s) are generated for one or more functions to define process identifiers, resource interaction types, timestamps, and/or resource identifiers. Observed pattern(s) for the application are identified by comparing current event record(s) with previous record(s), and the identified observed pattern(s) are evaluated against reference pattern(s) to generate score(s). Data packet(s) that indicate observed pattern(s), corresponding score(s), and/or cryptographic digital fingerprint(s) of the one or more functions are generated. The data packet(s) are transmitted to distributed ledgers for immutable storage.

Abstract: A computer-implemented method comprising receiving a first event request corresponding to a pending event between a first computing device and a second computing device, the first event request comprising a first set of event attributes corresponding to the pending event; transmitting, each of a plurality of first subsets of event attributes to a different set of nodes; receiving a second event request corresponding to the pending event between the first computing device and the second computing device; and transmitting each of the plurality of second subsets of event attributes to a different set of the plurality of sets of nodes, wherein each of the plurality of sets of nodes compares the first subset of event attributes that the set of nodes receives with the second subset of event attributes that the set of nodes receives; and a block instance corresponding to the pending event to a blockchain.

Abstract: The present disclosure provides systems and methods for hierarchical multi-level model distillation that create specialized artificial intelligence models from large language models. A hierarchy for generating specialized generative models from a large language model is determined, including models associated with different categories and complexity levels for tasks with varying computational requirements. The specialized models are trained using the large language model based on specific categories to provide responses to category-related requests. Each model is pruned based on complexity levels to enable responses to requests of varying complexity. The models are quantized from common precision factors to corresponding precision factors associated with complexity levels. Deployment locations are determined for each pruned model based on complexity levels. Specialized models are transmitted to corresponding locations for deployment across diverse computational environments.

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: A network system to analyze a combined output of various input files from data-based applications. The system provides custom profiling of the data from each application based on an application of one or more sets of rules. The system stores the data from any other number of applications in a base level of granularity to allow direct comparison of the data from each application output. Because the data is stored at a same level of granularity, the data may be compared or processed regardless of the application from which the data is received. The system applies rules to compare the data across the applications to identify outliers, trends, or commonalities. The system may also search for and identify data fitting a specific rule across the applications to extract, modify, or label the data. The system provides a visualization of the data based on the rules applied.

Abstract: A computer-implemented method for seamlessly processing transactions using distributed ledger technology. The method may comprise: linking one or more conventional accounts hosted in a conventional banking infrastructure to one or more DLT-based client accounts hosted on a distributed ledger, wherein the DLT application comprises a routing address configured to be used in conventional transaction infrastructure using conventional communication protocols; storing one or more wallet identifications for the one or more DLT-based client accounts and a mapping of the one or more wallet identifications to the one or more conventional accounts hosted in the conventional banking infrastructure; exchanging a sequence of messages to execute an asset transfer and complete a transaction lifecycle, the sequence of messages based on the first asset type; updating the distributed ledger based on the asset transfer; and sending appropriate messages to clients.

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 that modifies network configuration settings to block communication with network devices while maintaining connectivity to monitoring systems for forensic analysis and potential remediation. The system may additionally or alternatively initiate graduated deactivation procedures ranging from partial restrictions to full deactivation.

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: The technology is directed to a system for separately encrypting parameters of an access token. The system may receive a request for an access token and may determine computing devices for receiving the access token and sets of parameters that each computing device is to access. The system may identify encryption keys associated with the computing devices. Each computing device may store a corresponding decryption key for decryption a corresponding set of parameters. The system may encrypt each set of parameters using a corresponding encryption key to generate encrypted sets of parameters. The system may generate the access token by adding the encrypted sets of parameters to the access token. The system may then transmit the access token to a requesting device.

Abstract: The systems and methods disclosed herein orchestrate task execution among autonomous (or semi-autonomous) AI agentic models (“agents”) using a gateway router that dynamically coordinates the agents based on prompt characteristics, user context, and/or real-time operational factors. Received inputs (e.g., prompts) are segmented into subcomponents (e.g., sub-queries), which are routed/mapped to candidate agents based on the output parameters of the subcomponent (e.g., performance thresholds, cost thresholds) and operational parameters (e.g., cost, performance metric values, user access restrictions, timing restrictions) of each agent. The gateway router maintains dynamic routing data structures for each agent that are continuously updated based on environmental stimuli (e.g., geo-political stimuli, sensor stimuli, agent stimuli). For example, the gateway router causes agents to dynamically switch between rule engines identified by the routing tables in response to detecting environmental stimuli.

Abstract: Presented herein are systems and methods for managing networked environments. A computer system may maintain a plurality of process flows to manage a plurality of services in a network. Each respective process flow of the plurality of process flows may identify: (i) a respective trigger to invoke the respective process flow to initiate a first process of a plurality of processes on at least one of the plurality of services. The computing system may select, responsive to detecting a trigger, a process flow from the plurality of process flows based on the trigger. The computing system may execute, in accordance with the process flow, at least one process of the plurality of processes on at least one of the plurality of services in the network.

Abstract: Techniques and systems described herein relate to data storage and contextual retrieval of images in a database. In one example, an image is stored along with a unique vector embedding to a database. The image can then be retrieved based on a similarity between an input query and the image or the unique vector embedding.

Abstract: A set of software criteria including a condition that a software test be successfully performed prior to deploying a software package to a user device and location information indicating a storage location for status information of the software test is obtained. A testing status of the software package is analyzed using the status information at the storage location and software package is deployed to the user device as a result of the testing status confirming that the condition has been met in response to a request to deploy the software package to the user device.

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.