System for context-sensitive orchestration of autonomous agents in cloud platforms

Abstract

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.

Description

Technical field

[0001] The present invention relates to distributed computer and cloud-based automation systems. More precisely, it relates to a system and device architecture that enables context-sensitive orchestration of autonomous agents in heterogeneous cloud environments and supports adaptive resource allocation, collaborative task execution, and real-time optimization of multi-agent operations. BACKGROUND OF THE INVENTION

[0002] Cloud computing environments increasingly leverage autonomous agents to perform complex tasks such as dynamic scaling, load balancing, service healing, and application delivery. Existing orchestration frameworks rely heavily on predefined rules and reactive triggers, failing to adaptively address real-time contexts, heterogeneous resource constraints, and the interdependencies of multiple agents. Furthermore, current solutions struggle to coordinate agent behavior across multi-cloud infrastructures, leading to inefficiencies, resource conflicts, and suboptimal performance. Therefore, there is a need for a system that integrates contextual intelligence into the orchestration of autonomous agents, enabling them to collaboratively self-organize and dynamically adapt to changing conditions within a distributed cloud platform.

[0003] Cloud computing platforms have evolved significantly over the past decade, transforming from centralized, monolithic infrastructures to highly distributed, heterogeneous ecosystems that support a wide range of applications and services. These environments leverage virtualization, containerization, and service-oriented architectures to provide end users with flexible, on-demand computing resources. As applications become increasingly complex and distributed across multi-cloud and hybrid infrastructures, the need for automated management and orchestration of the underlying resources has grown considerably. However, traditional orchestration solutions struggle to keep pace with the dynamics of modern workloads. This leads to inefficiencies and operational challenges that limit their ability to deliver optimal performance, cost-effectiveness, and reliability.

[0004] Existing orchestration frameworks typically rely on static, rule-based mechanisms for task scheduling, resource allocation, and troubleshooting. Examples include widely used platforms like Kubernetes, Apache Mesos, and OpenStack Heat, which provide basic automation capabilities for containerized and virtualized environments. While these platforms can manage workloads across distributed infrastructures, they are inherently reactive, responding to predefined events or threshold breaches rather than proactively anticipating context changes. Kubernetes, for instance, uses declarative configuration to maintain a desired state and triggers scaling or repair actions when metrics such as CPU utilization or memory consumption exceed configured limits.While this approach offers basic fault tolerance, it cannot adapt to complex, multidimensional contexts such as fluctuating network conditions, interdependent service performance, or changing cost constraints across multiple cloud providers. This reactive paradigm leads to suboptimal resource utilization, reduced service quality during unpredictable workloads, and unnecessary operating expenses.

[0005] Another significant limitation of current solutions lies in their inability to orchestrate heterogeneous autonomous agents operating across different cloud platforms. In modern environments, autonomous agents are increasingly deployed for specialized tasks such as security monitoring, anomaly detection, load forecasting, and dynamic scaling. However, existing orchestration frameworks treat these agents as isolated entities rather than collaborative participants within a larger ecosystem. This lack of contextual coordination often leads to conflicting actions, such as concurrent scaling decisions by independent agents that disregard global system constraints. For example, a workload balancer agent might initiate scaling operations to reduce latency, while a cost optimization agent attempts to reduce resources to minimize costs.This leads to oscillating behavior, which destabilizes the system.

[0006] Service mesh technologies like Istio and Linkerd attempt to address some aspects of distributed coordination by enabling observability at the communication layer and policy enforcement across microservices. While these technologies improve network management, they are insufficient for higher-level orchestration decisions that require consideration of various contextual factors beyond service-to-service communication. They lack the semantic understanding of global goals, making them unsuitable for orchestrating complex agent collaborations in cloud platforms where multiple potentially conflicting priorities, such as cost, performance, compliance, and energy efficiency, must be balanced.

[0007] Machine learning-based orchestration solutions have also proven to be a promising approach for improving automation in cloud environments. These systems use predictive analytics to forecast resource requirements or detect anomalies, enabling smarter scaling and optimization decisions. However, most existing ML-based solutions operate as standalone components without holistic integration into the orchestration process. They are often based on narrow, task-specific models trained on historical data. This limits their ability to generalize to unknown scenarios or adapt to evolving workloads in real time.Furthermore, their results are typically fed into existing rule-based orchestration frameworks, which undermine the potential benefits of machine learning by restricting decision-making within rigid policy boundaries. The lack of a unified, context-aware decision level means that while predictions may be accurate, subsequent actions remain suboptimal due to the lack of global situational awareness.

[0008] Another drawback of current orchestration methods is their inadequate handling of multi-cloud environments. Companies are increasingly adopting multi-cloud strategies to leverage the strengths of different providers, avoid vendor lock-in, and increase redundancy. However, existing orchestration systems are often tied to specific cloud ecosystems or require complex, custom integrations to function across providers. This leads to operational silos where each cloud environment is managed independently, limiting the optimization of resource allocation and workload distribution on a global scale. Even federated orchestration solutions typically use simplified resource allocation mechanisms that fail to account for differentiated factors such as varying network latencies, legal data privacy regulations, energy costs, and workload dependencies between clouds.

[0009] Scalability and fault tolerance are further compromised by the centralized architectures of many orchestration frameworks. Centralized control planes can become bottlenecks under high load, leading to decision-making delays and isolated points of failure. While distributed control mechanisms have been proposed, they often lack the necessary coordination capabilities to align local decisions with global objectives. Without robust mechanisms for context-sensitive negotiation and consensus among distributed agents, these systems risk inconsistent states, performance degradation, and even cascading failures in large-scale implementations.

[0010] Security and compliance pose additional challenges for existing orchestration solutions. Autonomous agents in cloud environments must adhere to strict policies regarding data protection, access control, and regulatory compliance. Traditional orchestration frameworks ensure security through static configurations and access policies, which are inadequate in dynamic, adaptive environments with rapidly changing contexts. For example, migrating a workload from one location to another to optimize costs can unintentionally violate data residency requirements if the orchestration system does not contextually consider regulatory constraints. Similarly, the lack of integration of real-time threat intelligence into orchestration decisions leaves systems vulnerable to coordinated attacks on critical workloads.

[0011] Furthermore, the lack of semantic interoperability between different orchestration systems and agents hinders the realization of truly autonomous cloud environments. Each platform often uses its own abstractions, APIs, and configuration languages, making effective collaboration between agents from different vendors or domains difficult. While attempts to standardize orchestration through initiatives like OASIS TOSCA and CNCF projects have established some common ground, they do not enable dynamic, context-driven collaboration across heterogeneous ecosystems. The resulting fragmentation increases operational complexity and reduces the potential benefits of automation.

[0012] In summary, while significant progress has been made in automating cloud infrastructure management, existing solutions remain limited by their reactive, siloed, and context-independent nature. They fail to fully leverage the potential of autonomous agents as collaborative, learning entities capable of handling complex, multidimensional environments. The lack of an integrated, context-aware orchestration framework that can unify diverse agents, interpret dynamically evolving conditions, and optimize operations across heterogeneous cloud platforms represents a critical gap in current technology. Closing this gap requires a system that combines real-time context inference, policy-driven decision-making, distributed coordination, and learning-based optimization within a cohesive orchestration layer.Such a solution would enable cloud platforms to achieve true autonomy, resilience, and efficiency in managing increasingly complex workloads, thus overcoming the limitations of current state-of-the-art approaches. Summary of the invention

[0013] The invention provides a context-sensitive orchestration system for autonomous agents operating within cloud platforms. The system comprises a hardware-based orchestration component and an integrated software framework that dynamically contextualizes runtime parameters such as workload characteristics, network conditions, service-level objectives, and agent dependencies. The orchestration component includes a context inference engine, a policy-driven orchestration controller, a distributed agent interaction bus, and a machine learning-based optimization unit. Using these components, the system enables autonomous agents to perceive global and local context states, negotiate task distributions, and implement cooperative strategies to optimize performance, cost, and reliability across heterogeneous cloud infrastructures.

[0014] The main objective of the present invention is to provide a system for the context-sensitive orchestration of autonomous agents in cloud platforms. It overcomes the limitations of existing orchestration frameworks and enables adaptive, intelligent, and coordinated management of distributed workloads. A further objective of the invention is to provide a unified orchestration layer that integrates heterogeneous autonomous agents and enables them to operate together in dynamic cloud environments. Simultaneously, global system objectives such as performance, cost-efficiency, and compliance are met. The invention also aims to introduce a hardware-based orchestration device with real-time context inference and machine learning-based optimization. This ensures proactive, data-driven orchestration decisions that adapt to changing conditions in multi-cloud and hybrid infrastructures.A further objective of the invention is to ensure seamless scalability and fault tolerance through distributed, context-sensitive coordination between agents. This eliminates individual sources of error and guarantees consistent operating states even under high load or in error-prone scenarios. Furthermore, the invention aims to improve security, regulatory compliance, and policy enforcement by embedding context-aware knowledge of operational, legal, and threat-related parameters into the orchestration process. The goal of the invention is to provide a robust, future-proof orchestration system that enables cloud platforms to achieve a higher degree of automation, reliability, and efficiency, thereby allowing them to intelligently adapt to the complex and unpredictable demands of modern computing environments. BRIEF DESCRIPTION OF THE FIGURE

[0015] These and other features, aspects, and advantages of the present invention will be better understood if the following detailed description is read with reference to the accompanying drawing, in which the same symbols consistently represent the same parts. The following applies: Fig. Figure 1 shows a block diagram of a system for context-aware orchestration of autonomous agents in cloud platforms.

[0016] Experts will also recognize that the elements in the drawing are shown for the sake of simplicity and are not necessarily to scale. For example, the flowcharts illustrate the process by highlighting the main steps to enhance understanding of the aspects of this disclosure. Furthermore, with regard to the design of the device, one or more components of the device may be represented in the drawing by conventional symbols, and the drawing may show only the specific details relevant to understanding the embodiments of this disclosure, so as not to clutter the drawing with details that are readily apparent to those skilled in the art after reading this description. Detailed description of the invention

[0017] For a better understanding of the inventive principles, reference is made below to the embodiment shown in the drawing, which is described in specific terminology. However, this does not limit the scope of the invention. Changes and further modifications of the illustrated system, as well as further applications of the inventive principles, are possible, as would normally occur to a person skilled in the art in the field of invention.

[0018] It is clear to the person skilled in the art that the preceding general description and the following detailed description are exemplary and explanatory of the invention and are not intended as a limitation of it.

[0019] References in this specification to “an aspect”, “another aspect”, or similar expressions mean that a particular feature, structure, or property described in connection with the embodiment is included in at least one embodiment of the present disclosure. Therefore, occurrences of the expressions “in one embodiment”, “in another embodiment”, and similar expressions in this specification may all refer to the same embodiment, but need not.

[0020] The terms "includes," "include," or other variations thereof are intended to cover non-exclusive inclusion, such that a process or method that includes a list of steps may not only contain those steps but may also include other steps not expressly listed or inherent in such process or method. Likewise, the statement "includes..." in the case of one or more devices, subsystems, elements, structures, or components does not, without further limitations, preclude the existence of other devices, subsystems, elements, structures, components, or additional devices, subsystems, elements, structures, or components.

[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as understood by a person skilled in the art in the field of the invention. The system, methods, and examples provided here serve only for illustration and are not to be construed as a limitation.

[0022] Embodiments of the present disclosure are described in detail below with reference to the attached drawing.

[0023] In Fig.Figure 1 shows a block diagram of a system for context-sensitive orchestration of autonomous agents in cloud platforms. The system 100 comprises: a hardware-based orchestration device (102) configured for integration into a distributed cloud infrastructure; a context inference engine (104) within the orchestration device configured to receive and aggregate real-time telemetry data from multiple 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 (106) within the context inference engine configured to generate a contextual state representation by correlating the parameters using a knowledge graph-based dependency model;an optimization unit for machine learning (108) 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 (110) 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 (112) configured to transmit orchestration decisions to a variety of autonomous agents (112a) deployed on the cloud platform, the distributed agent interaction bus using a secure publish-subscribe protocol with conflict resolution mechanisms to ensure coordinated task execution.

[0024] The system is based on tangible processing components rather than abstract software constructs. The orchestration device is implemented as a dedicated hardware module, such as a rack server, blade, or embedded computing appliance, and includes microprocessors, memory units, and high-speed communication interfaces. The context inference engine, semantic reasoning module, and machine learning optimization unit are implemented as hardware-accelerated processing blocks running on CPUs, GPUs, or FPGAs (field-programmable gate arrays) integrated into the orchestration device. The associated memory circuitry stores real-time telemetry data and knowledge graph structures.The policy-driven orchestration controller is supported by non-volatile memory hardware for managing domain-specific policy repositories and integrates programmable logic circuits for enforcing orchestration decisions at the hardware level. The distributed agent interaction bus is implemented as a physical connection and includes network cards, switches, and routing circuits. It is configured to transmit orchestration signals to autonomous agents via high-speed communication links over the cloud platform. In this way, each claimed component is realized by hardware-based units and physical processing circuits, ensuring that the invention is geared towards a concrete technical implementation.

[0025] In one embodiment, the context inference engine (104) also includes a multi-stage aggregation pipeline configured to preprocess telemetry data by performing temporal alignment, noise filtering, and feature extraction prior to contextual correlation, thus harmonizing data from heterogeneous sources, including hypervisors, container runtimes, and service APIs, for accurate context generation.

[0026] In one embodiment, the semantic reasoning module (106) within the context inference engine uses a dynamically updatable knowledge graph representing resource dependencies, service-level targets, regulatory constraints, and capabilities between agents, and wherein the knowledge graph is continuously revised based on feedback from the autonomous agents and changes in the operational environment to maintain an accurate and up-to-date context model.

[0027] In one embodiment, the machine learning optimization unit (108) comprises a hierarchical reinforcement learning architecture configured to select orchestration actions at multiple levels of granularity, including node-level resource scaling, cluster-level load balancing, and workload migration between clouds, while the architecture simultaneously optimizes for multiple goals, including latency minimization, cost efficiency, and energy saving.

[0028] In one embodiment, the policy-driven orchestration controller (110) comprises a runtime policy compiler configured to translate high-level orchestration goals defined in a domain-specific language into executable control logic, and wherein the controller dynamically adapts the control logic in response to changes in the context state to ensure that the orchestration remains adaptive and goal-oriented.

[0029] In one embodiment, the distributed agent interaction bus (112) uses a two-phase commit protocol in combination with a conflict resolution algorithm based on the ranking of agent capabilities to resolve conflicts in task assignment between autonomous agents and thus prevent deadlocks, race conditions or oscillating behavior in coordinated operations.

[0030] In one embodiment, the orchestration device (102) further comprises a hardware-accelerated inference module implemented using a field-programmable gate array (FPGA) or an application-specific integrated circuit (ASIC), wherein the module is configured to perform low-latency context inference computations, including workload anomaly pattern recognition and real-time fault condition prediction.

[0031] In one embodiment, the orchestration device (102) is configured to operate in a multi-cloud environment by interacting with APIs of heterogeneous cloud providers, and the device maintains a federated orchestration layer that optimizes the distribution of the workload across clouds based on contextual factors, including jurisdictional compliance requirements, latency metrics between clouds, and cost fluctuations.

[0032] In one embodiment, the autonomous agents (112a) are configured to participate in context-dependent negotiations facilitated by the orchestration device. These negotiations involve the exchange of capability descriptors, local state information, and utility functions, and the orchestration device evaluates the negotiation results against global orchestration goals before issuing final execution instructions.

[0033] In one embodiment, the orchestration device (102) includes a security and compliance enforcement module configured to integrate real-time threat information, continuously monitor agent activity for anomalous behavior, and enforce context-dependent access control and data processing policies to ensure operational security and regulatory compliance during orchestration.

[0034] The present invention provides a system for the context-sensitive orchestration of autonomous agents in cloud platforms. A dedicated orchestration device integrates multiple hardware and software components to enable adaptive, intelligent, and coordinated workload management in heterogeneous, distributed environments. The orchestration device functions as a central orchestration node, embedded in a cloud platform or as a standalone hardware device with an interface to public and private cloud infrastructures. Its architecture is designed to process large volumes of telemetry data, generate actionable context representations, optimize orchestration strategies through machine learning, and coordinate autonomous agents in accordance with global operational objectives.

[0035] The core of the orchestration device is the context inference engine, responsible for aggregating and interpreting data from various sources, including system-level performance metrics, application-level indicators, and environmental factors such as power availability or cost fluctuations. The context inference engine employs a multi-stage aggregation pipeline that first normalizes raw telemetry data received from hypervisors, container runtimes, service APIs, and network monitoring tools. The pipeline then performs temporal alignment to synchronize incoming data streams, filters out noise using adaptive signal processing methods, and extracts relevant features that indicate workload patterns and infrastructure health.These features are fed into a semantic reasoning module that uses a dynamically updated knowledge graph to model relationships between resources, services, and operational constraints. The knowledge graph encodes dependencies, such as which services rely on specific computer clusters, which workloads are latency-sensitive, and which agents are bound by legal compliance requirements. As the system evolves, the knowledge graph is continuously updated through agent feedback and detected environmental changes. This ensures that the orchestration device maintains an accurate and comprehensive contextual understanding of the platform.

[0036] The context inference engine also integrates anomaly and pattern recognition algorithms to identify emerging problems before they impact service-level objectives. For example, if latency patterns in a particular region deviate from historical norms, the inference engine flags potential network degradation and augments the contextual state representation with this information. This representation is then used by the machine learning optimization unit, which determines optimal orchestration strategies based on predicted future system states. The optimization unit utilizes a hierarchical reinforcement learning (HRL) architecture consisting of multiple layers of policy agents trained to optimize decisions at varying levels of granularity.At the lowest level, node-level agents decide on immediate actions such as CPU scaling or memory reallocation, while cluster-level agents handle load balancing between nodes. A top-level policy agent manages workload migration between clouds and evaluates factors such as cost fluctuations, regulatory compliance, and latency between clouds. These agents are trained on both historical data and simulated workloads to maximize a reward function that considers latency minimization, cost efficiency, energy savings, and compliance. The HRL framework supports continuous learning, enabling the system to adapt to previously unknown workload patterns without manual reconfiguration.

[0037] The optimization unit's output feeds into the policy-driven orchestration controller, which translates high-level orchestration goals into executable control logic. These goals are defined in a domain-specific orchestration language that allows for the expression of complex conditions, dependencies, and objectives. The controller includes a runtime policy compiler that transforms these abstract goals into control sequences tailored to the current context. For example, a policy might require a critical analytics workload to have a latency of less than 50 ms while adhering to a specific cost budget. The compiler dynamically generates orchestration rules to meet these requirements, adjusting priorities and resource allocations in response to real-time conditions reported by the inference engine and recommendations from the optimization unit.The controller also supports the adaptation of runtime policies. If contextual conditions change, such as an unexpected cost increase in a particular region, the controller recompiles and updates the execution logic without requiring downtime or human intervention.

[0038] To implement these orchestration decisions, the device relies on the Distributed Agent Interaction Bus, which establishes a secure, low-latency communication structure connecting the autonomous agents deployed in the cloud environment. The bus uses a publish-subscribe protocol with a two-phase commit process to ensure that orchestration instructions are reliably transmitted and that the agents agree on coordinated actions. An integrated conflict resolution mechanism prioritizes decisions using a capability-assessment algorithm that evaluates each agent's ability to perform tasks based on its reported capabilities and contextual relevance.For example, if two agents propose conflicting scaling actions for a shared resource, the bus calls the evaluation algorithm to select the action of the agent with the higher proven effectiveness in similar contexts. This prevents deadlocks, race conditions, and oscillating behavior that commonly occur in distributed, uncoordinated systems.

[0039] In one embodiment, the orchestration device includes a hardware-accelerated inference module implemented on a field-programmable gate array (FPGA). This enables the low-latency execution of computationally intensive tasks such as pattern recognition and fault prediction. By offloading these tasks from the main processor, the device achieves real-time response, even under heavy telemetry loads. The inference module accelerates deep learning models used in the anomaly detection pipeline and supports the parallel processing of large telemetry data streams.

[0040] The orchestration device also features a security and compliance enforcement module that ensures orchestration decisions adhere to legal and operational requirements. This module continuously monitors agent activity, compares it against dynamic access control policies, and integrates real-time threat intelligence. If the system detects a potential policy violation, such as an agent attempting to migrate sensitive data to a non-compliant region, the enforcement module intervenes, blocks the action, and notifies the policy controller to adjust the orchestration logic accordingly.

[0041] The overall operation of the system proceeds as follows: Telemetry data is collected, processed, and contextualized by the inference engine; the machine learning unit generates predictions and optimization strategies; the orchestration controller creates and applies adaptive guidelines; and coordinated instructions are executed across agents via the interaction bus. During this process, feedback from agents and changes in the environment continuously updates the knowledge graph and retrains the learning models. This ensures that the system remains adaptive and self-improving.

[0042] This integrated architecture enables the invention to perform proactive, context-aware orchestration that surpasses existing solutions. By combining semantic reasoning, hierarchical learning, dynamic policy compilation, and distributed coordination, the system ensures optimal performance, cost control, security, and compliance in complex multi-cloud environments. The orchestration device thus forms a fundamental component for next-generation cloud automation, enabling autonomous, resilient, and intelligent operation in the face of evolving requirements and uncertainties.

[0043] The system consists of a physical orchestration device housed in a modular rack server unit integrated into the cloud infrastructure. The device includes a multi-core processing unit for executing orchestration algorithms, a high-speed interface for communication with distributed cloud nodes, and a hardware-accelerated inference module optimized for real-time context processing.

[0044] At the heart of the device is the Context Inference Engine, which continuously aggregates telemetry data from various sources, including system-level metrics (CPU utilization, memory consumption, network latency), application-level parameters (transaction throughput, error rates), and environmental factors (power availability, cost fluctuations). The engine employs a hybrid inference approach that combines semantic modeling of contextual relationships with a reinforcement learning-based adaptation loop to derive actionable situational awareness.

[0045] The inference engine is coupled to the policy-driven orchestration controller, which enforces dynamic orchestration policies based on context-dependent decisions. The controller manages a policy repository defined in a domain-specific orchestration language, supporting conditional constraints, goal-directed behavior, and multi-agent coordination rules. The controller dynamically binds these policies to active agents and adapts their execution strategies to changing context conditions.

[0046] The device also features a Distributed Agent Interaction Bus, which establishes a secure, low-latency communication channel between autonomous agents across multiple cloud clusters. The bus utilizes a publish-subscribe mechanism with integrated conflict resolution and priority scheduling to ensure coordinated task execution without deadlocks or race conditions.

[0047] To optimize resource allocation and workload distribution, the device's integrated Machine Learning Optimization Unit analyzes historical and real-time data streams to predict workload peaks, potential failures, and optimal scaling decisions. The optimization unit interacts bidirectionally with the orchestration controller and provides prescriptive adjustments to improve efficiency and resilience.

[0048] The system enables autonomous agents to dynamically self-organize in task-oriented coalitions. When a new workload arises, the agents negotiate responsibilities based on contextual priority and skill matching. The orchestration device evaluates the negotiation results against predefined objectives such as latency constraints, cost budgets, or compliance requirements and generates a global orchestration plan.

[0049] In one embodiment, the device is deployed as a standalone hardware unit in a private data center and connected to public cloud APIs to extend orchestration capabilities to hybrid environments. In another embodiment, the device is integrated as a virtualized orchestration node and utilizes containerized microservices for rapid scaling.

[0050] The described invention thus introduces a robust, context-aware orchestration framework that goes beyond static, rule-based automation and enables autonomous agents to operate with adaptive intelligence in complex, distributed cloud environments.

[0051] The proposed system offers significant advantages, including real-time adaptation to fluctuating conditions, improved coordination between autonomous agents, enhanced resource utilization, and seamless operation across multi-cloud infrastructures. By integrating hardware-accelerated context inference and machine learning-based optimization, the system achieves greater responsiveness and efficiency compared to existing orchestration methods.

[0052] The invention relates to the field of distributed computing and cloud-based automation. In particular, it relates to systems and architectures for orchestrating autonomous agents within cloud platforms through context-sensitive decision-making. The invention integrates hardware-based context inference, machine learning-based optimization, and distributed coordination mechanisms to enable adaptive, intelligent orchestration in heterogeneous multi-cloud environments. It is applicable to cloud resource management, workload optimization, fault tolerance, and compliance-driven orchestration in modern, large-scale computing infrastructures.

[0053] The drawing and the preceding description show examples of embodiments. Those skilled in the art will recognize that one or more of the described elements can be combined to form a single functional element. Alternatively, certain elements can be divided into several functional elements. Elements of one embodiment can be added to another embodiment. For example, the sequence of the processes described here can be changed and is not limited to the manner described here. Furthermore, the actions of a flowchart need not be implemented in the sequence shown; nor does it necessarily have to be performed by all actions. Actions that are not dependent on other actions can also be performed in parallel with the other actions. The scope of the embodiments is in no way limited by these specific examples.Numerous variations are possible, whether explicitly stated in the specification or not, such as differences in structure, dimensions, and material use. The range of embodiments is at least as broad as specified in the following claims.

[0054] Advantages, further benefits, and problem solutions have been described above with reference to specific embodiments. However, the advantages, benefits, problem solutions, and all components that can lead to an advantage, benefit, or solution occurring or becoming more apparent are not to be construed as critical, necessary, or essential features or components of individual or all claims. REFERENCES 100 A System for Context-Aware Orchestration of Autonomous Agents in Cloud Platforms. 102 Hardware-based orchestration device 104 Context Inference Engine 106 Module on Semantic Thinking 108 Optimization Unit for Machine Learning 110 Policy-driven Orchestration Controller 112 Distributed Agent Interaction Bus 112a Multiple Autonomous Agents

Claims

[1] 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. [2] System according to claim 1, wherein the semantic reasoning module within the context inference engine uses a dynamically updatable knowledge graph representing resource dependencies, service level targets, regulatory requirements and capabilities between agents, and wherein the knowledge graph is continuously revised based on feedback from the autonomous agents and changes in the operational environment to maintain an accurate and up-to-date context model. [3] System according to claim 1, wherein the machine learning optimization unit comprises a hierarchical reinforcement learning architecture configured to select orchestration actions at multiple levels of granularity, including node-level resource scaling, cluster-level load balancing, and workload migration between clouds, and wherein the architecture simultaneously optimizes for multiple objectives, including latency minimization, cost efficiency, and energy saving. [4] System according to claim 1, wherein the orchestration device further comprises a hardware-accelerated inference module implemented using a field programmable gate array (FPGA) or an application-specific integrated circuit (ASIC), wherein the module is configured to perform low-latency context inference computations, including workload anomaly pattern recognition and real-time fault condition prediction. [5] System according to claim 1, wherein the orchestration device is configured to operate in a multi-cloud environment by interacting with APIs of heterogeneous cloud providers, and wherein the device manages a federated orchestration layer that optimizes the distribution of workload across clouds based on contextual factors, including jurisdictional compliance requirements, latency metrics between clouds, and cost fluctuations. [6] System according to claim 1, wherein the autonomous agents are configured to participate in context-dependent negotiations facilitated by the orchestration device, wherein the negotiations involve the exchange of capability descriptors, local status information and utility functions, and wherein the orchestration device evaluates the negotiation results against global orchestration goals before issuing final execution instructions. [7] System according to claim 1, wherein the orchestration device includes a security and compliance enforcement module configured to integrate real-time threat information, continuously monitor agent activity for anomalous behavior, and enforce context-dependent access control and data processing policies to ensure operational security and regulatory compliance during orchestration.

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