161 results about "Autonomous agent" patented technology

Systems and methods disclosed herein perform privacy-preserving evaluations of artificial intelligence (AI) agents. A first AI agent associated with a first entity obtains a machine-readable data structure defining one or more operative boundaries for a second AI agent associated with a second entity. The system generates a unique fixed reference value representing the machine-readable data structure by applying a first transformation operation set, and transmits the unique fixed reference value to a multi-agent storage to store the value. The system receives, via the multi-agent storage, a verification artifact from the second AI agent that indicates an observed value based on internal operational data of the second AI agent corresponding to the operative boundaries. The first AI agent determines a verification status of the verification artifact by comparing the unique fixed reference value with the observed value, and autonomously generates a verification record including a representation of the verification status.

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. Preventative measures may involve updating behavioral policies and adjusting agent parameters to disincentivize problematic behaviors.

The invention discloses an intelligent traffic control system and method based on multi-agent near-end strategy optimization, and belongs to the field of intelligent traffic, Internet of Vehicles and deep reinforcement learning. The method comprises the following steps: firstly, constructing a fog-cloud collaborative three-layer architecture, and realizing real-time monitoring and dynamic regulation and control of traffic flow through cloud global decision and local sensing collaboration of a road side unit (RSU); secondly, designing indexes of'road section overlap ratio 'and'road section time overlap ratio', and solving the problem of secondary congestion caused by rerouting; then, a multi-agent near-end strategy optimization (MAPPO) algorithm is adopted, so that the traffic signal lamp is used as an autonomous agent to dynamically adjust the phase, and the limitation of single-point control is broken; and finally, through integrated optimization of rerouting and adaptive signal control, an original multi-objective optimization problem is converted into a layered multi-agent reinforcement learning problem. According to the invention, vehicle driving time and system energy consumption can be effectively reduced, road traffic efficiency is improved, and active avoidance and dynamic alleviation of urban traffic congestion are realized.

A system for context-sensitive orchestration of autonomous agents in cloud platforms, consisting of: a hardware-based orchestration device configured for integration into a distributed cloud infrastructure; a context inference engine within the orchestration device, wherein the context inference engine is configured to receive and aggregate real-time telemetry data from a variety of distributed nodes, including at least one system-level parameter, at least one application-level parameter, and at least one environment parameter; a semantic inference module within the context inference engine, configured to generate a context-related state representation by correlating the parameters using a knowledge graph-based model of interdependencies; an optimization unit for machine learning within the orchestration device, which is communicatively connected to the context inference engine and is configured to predict resource requirements and operational states using reinforcement learning models trained on historical and real-time data streams; a policy-driven orchestration controller configured to translate the contextual state representation into actionable orchestration decisions by applying dynamic orchestration policies stored in a domain-specific policy repository; and a distributed agent interaction bus configured to delegate orchestration decisions to a variety of autonomous agents deployed on the cloud platform.

Systems and methods for resolving code vulnerabilities through collaborative agents which may include accessing a code base of an identified vulnerability; configuring a plurality of autonomous agents, each comprising a predefined agent role associated with application security remediation process; executing a directed workflow of the plurality of agents, wherein the workflow is a conditional sequence of agent-driven processing steps for generating a proposed resolution to the identified vulnerability; and outputting a candidate resolution for the vulnerability based on results produced by the workflow.

A system for real-time creditworthiness assessment of small and medium-sized enterprises (SMEs) based on agent-based artificial intelligence (AI), the system includes: a large number of autonomous agents connected to each other via secure communication protocols; a data ingestion processing unit configured to capture and preprocess heterogeneous data streams from structured and unstructured sources, including APIs for financial transactions, tax databases, e-invoicing registers, inventory management systems, social sentiment feeds, and IoT-enabled devices, with preprocessing including schema harmonization, anomaly detection, and encryption-based integrity preservation; an ensemble of scoring control units configured to generate dynamic credit scores, wherein the ensemble includes machine learning models, including gradient-enhanced decision trees, recurrent neural networks, and graph neural networks, the graph neural networks being configured to assess relational dependencies between SMEs, suppliers, and customer nodes in a business ecosystem; an audit controller configured to generate regulatory-compliant justification paths for each credit score; a feedback processing unit configured to update the evaluation control unit ensemble using reinforcement learning signals derived from observed repayment behavior, payment default trends, and fraud detection markers; and a user interface configured to display timestamped credit histories, confidence intervals and explanatory justifications, with the entire system running on a containerized cloud-native infrastructure with blockchain-based ledgers for version control of each point update.

A symbolic neuroadaptive control system is disclosed for real-time arbitration, consent, and ethical modulation of artificial intelligence agents operating in wearable computing environments. The system integrates multimodal biometric telemetry—including high-resolution EEG signals—with a symbolic kernel that performs logic-driven arbitration over cognitive, emotional, and ethical states. Using Coq-verified invariants and zero-knowledge biometric consent tokens, the system constructs a deterministic symbolic execution graph, gating AI outputs based on internal user states such as trauma, stress, or intentionality. Unlike conventional black-box BCI models, the invention routes EEG-inferred affective-symbolic tokens through a formal ethics layer that enforces real-time interrupt control, utility bounding, and trust verification. The kernel enables AGI systems to defer or modify behavior based on user-state alignment, granting sovereign agency over all downstream actions. This neuro-symbolic architecture redefines the interface between human cognition and intelligent machines, enabling emotionally conscious, morally verifiable, and symbolically transparent AI governance in dynamic, high-stakes contexts.The present invention relates to artificial intelligence and neurotechnology, specifically to a real-time, neuro-symbolic operating system kernel that converts electroencephalography (EEG) signals into structured symbolic data for use in emotional cognition, ethical prioritization, autonomous agent dispatch, and real-time telecommunications routing. The invention bridges brain-computer interface (BCI) inputs with symbolic AI architectures to enable ethically aligned machine response during cognitively or emotionally intense events.

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.

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.

An autonomous communication system includes a language model, a retrieval module, a caching mechanism, an autonomous agent, and a human interface for managing interactions and responses. A method and computer-readable medium for managing communication also include these components for processing, enhancing, summarizing, managing interactions, and allowing human intervention.

A hybrid, redundant, fail-safe architecture provides a unified fail-operational framework for autonomous agents operating across physical and virtual domains. The system employs a multi-modal data source suite, an adaptive hybrid data fusion module, and an intelligent decision-making module. A novel closed-loop interaction enables a health monitoring module that detects an incipient fault in a data source by monitoring ancillary performance metrics. Upon detection, the module generates a fault signature, including a quantitative prognostic estimate of a future failure time, and transmits it to an adaptive data fusion module. The fusion module proactively reconfigures its state estimation algorithm by decreasing reliance on the degrading data source in proportion to the prognostic estimate. This preemptive compensation ensures the system maintains a high-integrity environmental model and achieves true fail-operational continuity. The architecture is applicable to numerous embodiments providing a universal solution for proactive fault management and system resilience.

Systems and methods disclosed herein perform privacy-preserving evaluations of artificial intelligence (AI) agents. A first AI agent associated with a first entity obtains a machine-readable data structure defining one or more operative boundaries for a second AI agent associated with a second entity. The system generates a unique fixed reference value representing the machine-readable data structure by applying a first transformation operation set, and transmits the unique fixed reference value to a multi-agent storage to store the value. The system receives, via the multi-agent storage, a verification artifact from the second AI agent that indicates an observed value based on internal operational data of the second AI agent corresponding to the operative boundaries. The first AI agent determines a verification status of the verification artifact by comparing the unique fixed reference value with the observed value, and autonomously generates a verification record including a representation of the verification status.

The invention discloses a multi-robot cooperative measurement method and system based on crowd intelligence emergence, and aims to solve the problems of single-point fault, poor environmental adaptability and insufficient expandability in the existing centralized and pre-planned multi-robot cooperative measurement method. According to the method, a shared digital environment cooperatively and jointly maintained by all robots through decentration is constructed, a decentration task decision-making mechanism based on digital pheromones is introduced, each measurement robot is used as an autonomous agent, and the concentration of the digital pheromones dynamically determined by the quality of measurement data in the shared digital environment is sensed, so that the concentration of the digital pheromones in the shared digital environment is determined. Measurement target decisions are made independently. According to the method, a robot cluster for multi-robot cooperative measurement can spontaneously emerge a globally optimal cooperative measurement behavior without a central control node, so that the robustness, the adaptability and the expandability of a measurement system are remarkably improved, and efficient and reliable three-dimensional measurement of a large component is realized.

The invention relates to an autonomous agent-oriented large language model element cognitive correction self-updating reward method, which comprises the following steps of: firstly, automatically generating an initial reward function by utilizing a large language model (LLM) according to high-level task description by using the AURA; then, meta-cognition on calculation is realized through a double-loop architecture: a rapid internal loop carries out collaborative evolution of strategies and rewards through a layered attention reward synthesizer; a slower outer cycle serves as a meta-cognitive layer, and a multi-dimensional diagnosis vector composed of strategy dynamics, value-authenticity alignment and reward architecture adaptability is continuously monitored so as to recognize anomalies in the learning process. The closed-loop feedback mechanism endows the autonomous agent with the capability of carrying out introspection and self-correction on the award function of the autonomous agent, and normal form transformation from static target specification to dynamic target correction is formed. And the robustness, the sample efficiency and the final performance of the intelligent agent in the face of sparse rewards, protocol vulnerabilities and environment uncertainty are enhanced.

A computing services environment may include application servers providing computing services including access to a database system, a unified metadata framework including autonomous agent definitions referencing action definitions defining a plurality of actions capable of being performed within the computing services environment, an agent service configured to instantiate an autonomous agent instance based on an autonomous agent definition, and an orchestration layer configured to determine an orchestration plan based on novel planning text generated by a generative language model. The orchestration plan may include a subset of the plurality of actions identified in the novel planning text. The computing services environment may execute the subset of the plurality of actions within the computing services environment.

A method for risk-aware policy assessment for an autonomous vehicle can include: collecting information associated with an environment of an ego vehicle; determining a set of policy proposals; determining and assessing a set of risks encounterable (e.g., potentially encountered in the future) by the ego vehicle; selecting a policy based on the set of risks, operating the ego vehicle based on the assessed risks, and / or any other suitable elements. Additionally or alternatively, the method can include any or all of: performing a set of simulations, analyzing the simulation results, determining a set of discount profiles, discounting a set of risks, and / or any other processes. The method can be performed with a system as described below and / or any other suitable system.

A computing services environment may include application servers providing computing services including access to a database system, a unified metadata framework including autonomous agent definitions referencing action definitions defining a plurality of actions capable of being performed within the computing services environment, an agent service configured to instantiate an autonomous agent instance based on an autonomous agent definition, and an orchestration layer configured to determine an orchestration plan based on novel planning text generated by a generative language model. The orchestration plan may include a subset of the plurality of actions identified in the novel planning text. The computing services environment may execute the subset of the plurality of actions within the computing services environment.

The invention provides a task agent generation and task processing method based on a meta agent, and relates to the technical field of autonomous agent application. The method mainly comprises the steps of initialization, task input, task analysis, subtask generation, task agent generation and distribution, task execution and cooperation, and result integration and feedback. According to the method, the meta-agent with task understanding, decomposition and planning capabilities is introduced, so that the task agent is dynamically generated according to task requirements, the tool calling process is optimized, the problem of low model processing efficiency caused by tool information overload in the prior art is effectively solved, and the model processing efficiency is improved. And the response speed, the flexibility and the intelligent level of task processing of the automatic software / hardware system are remarkably improved.

A computing services environment may include application gateways receiving application-layer request messages from various sources. The computing services environment may also include an autonomous agent platform configured to instantiate and execute an autonomous agent to evaluate network traffic associated with a portion of the computing services environment. The computing services environment may also include an orchestration engine configured to determine one or more mitigation policies corresponding with one or more of the application gateways based on identification of the application-layer distributed denial of service attack by the autonomous agent. The computing services environment may also include application-layer web application firewalls corresponding to the plurality of application gateways and implementing the one or more mitigation policies to prevent a subset of subsequent application-layer request messages from the subset of the sources from reaching one or more components of the computing services environment.

A computing services environment may include application servers providing computing services including access to a database system, a unified metadata framework including autonomous agent definitions referencing action definitions defining a plurality of actions capable of being performed within the computing services environment, an agent service configured to instantiate an autonomous agent instance based on an autonomous agent definition, and an orchestration layer configured to determine an orchestration plan based on novel planning text generated by a generative language model. The orchestration plan may include a subset of the plurality of actions identified in the novel planning text. The computing services environment may execute the subset of the plurality of actions within the computing services environment.

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.

A container simulation network system based on a link autonomous agent is composed of a visual interaction module, a control plane module, a network simulation module and a resource management module. The modules perform information interaction and task collaboration through a uniform interface so as to realize automatic construction and management of the simulation network, a two-layer architecture design of network bridge containers and user containers is adopted, and each user container corresponds to one network bridge container; a link autonomous agent module is deployed in the network bridge container and is used for collecting a link state, calculating a performance adaptation degree and executing a scheduling operation bit; the visual interaction module realizes visual monitoring and interaction control of a simulation state; the control plane module is responsible for simulation scene analysis, link parameter generation and global scheduling management; and the network simulation module is responsible for calling a container generation tool to generate a specific container instance and carrying out resource monitoring and adjustment on the container.

A computing services environment may include application gateways receiving application-layer request messages from a plurality of sources. The computing services environment may also include an orchestration engine configured to identify an application-layer distributed denial of service attack based on input data characterizing network traffic received at the application gateways and to determine a mitigation plan update to address the application-layer distributed denial of service attack. The computing services environment may also include an autonomous AI agent platform configured to instantiate and execute an autonomous AI agent instance configured to determine whether to approve or reject the mitigation plan update by evaluating the mitigation plan update via a generative language model. The computing services environment may also include application-layer web application firewalls corresponding to application gateways. The orchestration engine may instruct the application-layer web application firewalls to implement the mitigation plan update upon approval by the autonomous AI agent instance.

A computer-implemented method for improving workflow automation is disclosed. The method can receive a workflow including one or more processes. A process includes a plurality of tasks and links connecting the plurality of tasks. The links define an operation sequence of the plurality of tasks. The method can generate text descriptions of the one or more processes using a large language model and bind a set of tools to the workflow. The set of tools specify application programming interfaces used to perform tasks involved in the workflow. The method can create an autonomous agent configured to execute a selected task of the workflow responding to a user query. The autonomous agent identifies the selected task by comparing the user query and the text descriptions of the one or more processes. Related systems and software for implementing the method are also disclosed.

The invention provides an autonomous agent rumor checking system based on a dynamic causal evidence graph. The autonomous agent rumor checking system comprises a planning module for analyzing a topological structure of the dynamic causal evidence graph read from a memory module and generating an action instruction; the action module is used for executing an action instruction issued by the planning module, converting unstructured evidence information into structured operation on the dynamic causal evidence graph, finding an optimal evidence path and generating a final report; the memory module responds to a reading request from the planning module and a writing request from the action module; the environment module is used for responding to query API calling from the action module and returning original unstructured evidence information; and the information source authority management module responds to the query request from the action module and provides an information source authority rating for the evidence information. According to the method, the intelligent agent taking the dynamic causal evidence-seeking graph as a core memory and reasoning center is constructed, so that deep, accurate and explainable real-time recognition of complex network rumors can be realized.

Disclosed is system for fulfilling service request using computing nodes. The system comprises a decentralized computing network, comprising computing nodes operating as autonomous agents, configured to implement software framework comprising: client-agent device 5 configured to: receive service request and generate objective therefor, and send objective to agent-device configured to send queries to a machine learning model agent and / or obtain tasks, and list of autonomous agents associated with objective; and processing arrangement configured to: generate directed acyclic graph (DAG) having 10 nodes representing first task and edges representing dependency of first task on second task(s) amongst tasks, and initialize Bloom filter of each node to track completion status of second task(s). Autonomous agent is configured to: identify second task(s), check completion status of second task(s), using Bloom filter of first task, upon completion of second 15 task(s), execute first task, and update Bloom filter.

A system and method for fail-operational mission continuity in a multi-agent autonomous system. Each autonomous agent includes an onboard Prognostic Health Management (PHM) module that monitors health using sensor data. Upon detecting an incipient fault, the PHM module calculates a prognostic Remaining Useful Life (RUL). This RUL is transformed into a quantitative Operational Risk Cost (Ω) and communicated to a multi-agent control system. The control system's dynamic task allocation algorithm uses the Ω values as key inputs in a multi-objective optimization process. This enables the system to proactively and autonomously re-allocate a task from a degrading agent to a healthy agent before a failure occurs. The degrading agent is simultaneously commanded to perform a safe contingency maneuver. This integration of real-time prognostics and multi-agent control creates a resilient, self-healing system capable of completing missions despite hardware degradation.

A computing services environment may include application servers providing computing services including access to a database system, a unified metadata framework including autonomous agent definitions referencing action definitions defining a plurality of actions capable of being performed within the computing services environment, an agent service configured to instantiate an autonomous agent instance based on an autonomous agent definition, and an orchestration layer configured to determine an orchestration plan based on novel planning text generated by a generative language model. The orchestration plan may include a subset of the plurality of actions identified in the novel planning text. The computing services environment may execute the subset of the plurality of actions within the computing services environment.

A distributed processing system for transactions of digital assets includes autonomous agents that execute off-chain. Each agent has an encapsulation of state representing at least one digital asset and is configured to process transaction requests that modify the agent state. A proof aggregation layer prevents double spending of the digital assets. A consensus layer forms a decentralized trust anchor that maintains a blockchain and certifies state transitions of the proof aggregation layer. A sending agent issues a request to certify that a particular asset state has not been previously spent and if the proof aggregation layer returns a uniqueness attestation, a recipient agent can use it to verify both correctness of the transaction execution, and validity of the uniqueness attestation. Transaction execution may take place off-chain at the agents without reliance on global consensus or shared state among all network participants.

Disclosed is decentralized transaction system comprising computing nodes to implement platform, computing node(s) is configured to: receive, from first user device (FUD), first request for first trade (FT), first smart contract (FSC) to lock funds for FT; receive, from second user device, second request for second trade (ST), second smart contract (SSC) to lock trade subject (TS) for ST; send, to agent database (AD), first notification, for first autonomous agent (FAA) associated with first user (FU); second notification, for second autonomous agent (SAA) associated with second user (SU); create communication interface, FAA configured to send first set of trade parameters (FSOTP), first digital signature (FDS) to SAA, facilitate agent-based trade match, SAA configured to perform trade match, send FDS, FSOTP and second set of trade parameters (SSOTP) to SSC; employ processing interface for enabling order book-based trade verification, SSC to execute transfer of funds and trade subject to SU and FU, respectively.