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 are disclosed comprising instructions to access audio data of a captured interaction involving one or more participating users of a digital conference tool, receive a first narrative summary of the captured interaction comprising a set of first component narratives, generate a second narrative summary comprising a set of second component narratives, identify at least one component narrative among the set of second component narratives corresponding to the participating users, display the second component narratives of the second narrative summary at a user interface of the participating users, receive user feedback data comprising an adjustment to the displayed text content of the at least one second component narrative, generate a third narrative summary using the user feedback data received from each participating user of the captured interaction, and display the third narrative summary for the captured interaction at the user interface of each participating user.

Abstract: The systems and methods disclosed herein receive alphanumeric characters defining operative boundaries for expected model use cases, along with operational data. The expected model use cases share common attributes, which are used by a first AI model to construct observed model use cases from the operational data. Each observed model use case includes features such as a text-based description, expected input and output, AI model(s) generating the expected output from the input, and/or data supporting the AI models. For each observed model use case, a second AI model maps the alphanumeric characters and features to a risk category, selecting from multiple risk categories based on the level of risk associated with the features. The system identifies criteria for the observed model use case within the alphanumeric characters and generates gaps by comparing the criteria with the features of the observed model use case.

Abstract: The systems and methods disclosed herein receives, from a computing device, operational data indicating software or hardware assets used on informational assets, and obtains set of alphanumeric characters defining operative boundaries for expected system assets, which include a set of common attributes. Using the set of attributes, a first set of AI models determines observed system assets from the operational data, each with specific features. A second set of AI models associates each information asset with the corresponding observed system assets. For each observed system asset, a third set of AI models identifies criteria within the alphanumeric characters, compares the criteria with the asset's features to identify gaps, and generates actions to ensure the observed system asset meets the identified criteria.

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: 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 disclosed herein automatically authorize, audit, and manage usage of protected digital content via agentic artificial intelligence (AI) models. A data access/usage request is received (e.g., from a graphical user interface) that is associated with digital assets licensed from third parties. The system uses a first AI agent set to identify the digital content and retrieve corresponding access policies from a distributed database. The system uses a second AI agent set (same as or different from the first AI agent set) to evaluate the request against the retrieved policy to generate a permission set and/or settlement instructions. The system uses a third AI agent set (same as or different from the first and/or second AI agent sets) to embed digital watermarks and/or cryptographic signatures into the accessed content, and to record an audit trail of access, authorization, and/or settlement events in a distributed ledger or database.

Abstract: Systems and methods disclosed herein automatically register, monitor, and authenticate distributed artificial intelligence (AI) agents and their operational contexts using a distributed or federated ledger-based agent knowledge registry. The system obtains a registration or query request (e.g., from an AI agent, orchestrator, or user interface) to identify or store operational context linked to each AI-based agent. The system determines a feature set of agent metadata and operational parameters using a first AI model set, and dynamically generates a cryptographically verifiable registry record set using a second AI model set (same as or different from the first AI model set) based on the operational feature set, to be stored in a distributed ledger database.

Abstract: Systems and methods disclosed herein automatically evaluate, select, and coordinate artificial intelligence (AI)-based agents for collaborative distributed task execution based on dynamic, multi-attribute scoring and resource allocation models. The system obtains a task specification request defining a computational requirement set, a performance metric set, and an available resource set for one or more tasks to be executed by a network of AI-based agents. A first AI model set generates domain-specific test datasets and validates prospective agents by comparing agent-generated fingerprints against predetermined hash values stored on a distributed or federated ledger. A second AI model set constructs a multi-dimensional scoring data structure for each agent by using historical performance metrics to compute weighted composite scores. The system selects a subset of AI-based agents, ranks the agents, and allocates resources proportional to each agent's composite score.

Abstract: Systems and methods disclosed herein automatically detect, analyze, and mitigate anomalous resource distribution among artificial intelligence (AI)-based agents within a distributed computational network. The system receives a resource allocation request specifying computational resources, agent parameters, and performance objectives for a network of agents. A first AI model set monitors agent activity by tracking resource consumption and behavioral deviations from baseline profiles. The system compares resource usage of agents with historical norms and/or predetermined thresholds to generate an anomaly score for each agent. A second AI model set aggregates scores to construct a multi-dimensional data structure that indicates the comparison and anomaly score. The system ranks agents by anomaly severity to isolate agents with high scores (e.g., misaligned agents), and reallocates resources and updates access privileges for the misaligned agents.

Abstract: The systems and methods disclosed herein receives, from a computing device, operational data indicating software or hardware assets used on informational assets, and obtains a set of alphanumeric characters defining operative boundaries for expected system assets, which include a set of common attributes. Using the set of attributes, a first set of AI models determines observed system assets from the operational data, each with specific features. A second set of AI models associates each information asset with the corresponding observed system assets. For each observed system asset, a third set of AI models identifies criteria within the alphanumeric characters, compares the criteria with the asset's features to identify gaps, and generates actions to ensure the observed system asset meets the identified criteria.

Abstract: The systems and methods disclosed herein receive alphanumeric characters defining operative boundaries for expected model use cases, along with operational data. The expected model use cases share common attributes, which are used by a first AI model to construct observed model use cases from the operational data. Each observed model use case includes features such as a text-based description, expected input and output,? AI model(s) generating the expected output from the input, and/or data supporting the AI models. For each observed model use case, a second AI model maps the alphanumeric characters and features to a risk category, selecting from multiple risk categories based on the level of risk associated with the features. The system identifies criteria for the observed model use case within the alphanumeric characters and generates gaps by comparing the criteria with the features of the observed model use case.

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 receive alphanumeric characters defining operative boundaries for expected model use cases, along with operational data. The expected model use cases share common attributes, which are used by a first AI model to construct observed model use cases from the operational data. Each observed model use case includes features such as a text-based description, expected input and output,? AI model(s) generating the expected output from the input, and/or data supporting the AI models. For each observed model use case, a second AI model maps the alphanumeric characters and features to a risk category, selecting from multiple risk categories based on the level of risk associated with the features. The system identifies criteria for the observed model use case within the alphanumeric characters and generates gaps by comparing the criteria with the features of the observed model use case.

Abstract: The systems and methods disclosed herein receives, from a computing device, operational data indicating software or hardware assets used on informational assets, and obtains set of alphanumeric characters defining operative boundaries for expected system assets, which include a set of common attributes. Using the set of attributes, a first set of AI models determines observed system assets from the operational data, each with specific features. A second set of AI models associates each information asset with the corresponding observed system assets. For each observed system asset, a third set of AI models identifies criteria within the alphanumeric characters, compares the criteria with the asset's features to identify gaps, and generates actions to ensure the observed system asset meets the identified criteria.

Abstract: The systems and methods disclosed herein receives artifacts generated using a first set of models within a multi-model superstructure. The multi-model superstructure includes a second set of models to test the first set of models. The multi-model superstructure dynamically routes the artifacts of the first set of models to one or more models of the second set of models by (i) determining a set of dimensions of the artifacts against which to evaluate the artifacts and (ii) identifying the models in the second set used to test the particular dimension. The second set of models then assesses each artifact against a set of assessment metrics. If an artifact fails to meet one or more assessment metrics, the second set of models generates actions to align the artifact with the set of assessment metrics.

Abstract: The systems and methods disclosed herein receive alphanumeric characters defining operative boundaries for expected model use cases, along with operational data. The expected model use cases share common attributes, which are used by a first AI model to construct observed model use cases from the operational data. Each observed model use case includes features such as a text-based description, expected input and output, AI model(s) generating the expected output from the input, and/or data supporting the AI models. For each observed model use case, a second AI model maps the alphanumeric characters and features to a risk category, selecting from multiple risk categories based on the level of risk associated with the features. The system identifies criteria for the observed model use case within the alphanumeric characters and generates gaps by comparing the criteria with the features of the observed model use case.

Abstract: The systems and methods disclosed herein receives artifacts generated using a first set of models within a multi-model superstructure. The multi-model superstructure includes a second set of models to test the first set of models. The multi-model superstructure dynamically routes the artifacts of the first set of models to one or more models of the second set of models by (i) determining a set of dimensions of the artifacts against which to evaluate the artifacts and (ii) identifying the models in the second set used to test the particular dimension. The second set of models then assesses each artifact against a set of assessment metrics. If an artifact fails to meet one or more assessment metrics, the second set of models generates actions to align the artifact with the set of assessment metrics.

Abstract: The systems and methods disclosed herein receives artifacts generated using a first set of models within a multi-model superstructure. The multi-model superstructure includes a second set of models to test the first set of models. The multi-model superstructure dynamically routes the artifacts of the first set of models to one or more models of the second set of models by (i) determining a set of dimensions of the artifacts against which to evaluate the artifacts and (ii) identifying the models in the second set used to test the particular dimension. The second set of models then assesses each artifact against a set of assessment metrics. If an artifact fails to meet one or more assessment metrics, the second set of models generates actions to align the artifact with the set of assessment metrics.