A system for the adaptive orchestration of LLM-driven agents in cross-functional workflows

Abstract

A system for the adaptive orchestration of LLM-driven agents across multi-domain workflows, including: an orchestration module configured to dynamically assign tasks to LLM-driven agents based on contextual understanding, workload distribution, and domain expertise; a multi-agent collaboration module configured to enable seamless communication between multiple LLM-driven agents to exchange task-related information, reduce redundancies, and optimize workflow execution; a real-time decision-making module configured to use machine learning algorithms to evaluate task priority, user intent, and operating conditions to enable adaptive workflow adjustments; an optimization module based on reinforcement learning, configured to continuously refine task delegation rules and workflow strategies based on historical task performance and feedback; a monitoring and analysis module configured to provide real-time insights into the performance of the AI ​​agent, task completion times, and workflow efficiency via visual dashboards; an integration layer that enables seamless interoperability with third-party enterprise systems, APIs, databases, and AI services to ensure cross-domain applicability; and a security and compliance module configured to include access control mechanisms, data encryption, and monitoring of compliance with legal regulations to ensure secure and legally compliant AI operations.

Description

[0001] The present invention relates to artificial intelligence (AI)-based workflow automation. More specifically, it is a system for adaptive orchestration of large language model (LLM) agents across multiple domains, ensuring real-time task delegation, optimization, and contextual adaptation.

[0002] Artificial intelligence (AI) and natural language processing (NLP) have significantly transformed workflow automation in various industries such as healthcare, finance, legal, customer support, and software development. Large language models (LLMs) have proven to be powerful tools for tackling complex tasks such as document summarization, question answering, sentiment analysis, and conversational interactions. However, existing AI-powered workflow systems are limited in effectively managing cross-domain operations due to their reliance on static, rule-based task assignment mechanisms, single-model architectures, and isolated AI agents lacking real-time coordination.

[0003] Traditional workflow automation solutions predominantly use rule-based engines that leverage predefined decision trees and static heuristics for task assignment. While these systems are well-suited to structured environments, they lack adaptability and struggle with complex, dynamic workflows that require frequent manual updates to account for changes in business logic.

[0004] Likewise, some organizations deploy a single AI model to handle all tasks within an organization, leading to inefficiency, increased latency, and lower accuracy due to a lack of domain specialization. Furthermore, multi-agent AI systems, even when used in certain implementations, often operate in isolation without a central intelligence coordinating their activities, leading to redundancies, inefficient task delegation, and poor workflow optimization.

[0005] Other traditional methods involve manually assigning tasks to AI agents, which slows down processes and increases operating costs, making scalability a significant challenge. Another widely used approach is API-based task routing, which invokes various AI models based on predefined input parameters. However, such systems are rigid and cannot dynamically adapt to changing workflow requirements. The lack of real-time learning and self-optimization mechanisms further limits their efficiency in handling complex, cross-domain operations.

[0006] To solve this problem, the present invention provides a system for adaptive orchestration of LLM-driven agents across multi-domain workflows.

[0007] The system for adaptive orchestration of LLM-driven agents across multi-domain workflows, which can enable real-time delegation of tasks to the most appropriate LLM-driven agent based on contextual understanding, domain expertise, and workload balancing.

[0008] The system for adaptive orchestration of LLM-driven agents across multi-domain workflows, which can continuously monitor and refine workflow execution using reinforcement learning, machine learning-based decision models, and historical task performance data.

[0009] The system for adaptive orchestration of LLM-driven agents across multi-domain workflows, which can establish a coordinated communication framework between multiple LLM-driven agents, enabling them to share knowledge, reduce redundancies, and improve task efficiency.

[0010] The system for adaptive orchestration of LLM-driven agents across multi-domain workflows, which can integrate context-aware reasoning mechanisms that evaluate user intent, task priority, and real-time operating conditions to ensure adaptive workflow adjustments.

[0011] The system for adaptive orchestration of LLM-driven agents across multi-domain workflows, which can design an orchestration system that seamlessly integrates with various enterprise systems, APIs, databases, and third-party AI services, enabling smooth adoption across multiple industries.

[0012] The system for adaptive orchestration of LLM-driven agents across multi-domain workflows, which can incorporate AI-driven self-improvement techniques that refine task delegation rules, optimize agent selection, and improve workflow efficiency based on continuous feedback loops.

[0013] The system for adaptive orchestration of LLM-driven agents across multi-domain workflows, which can implement robust access control, data encryption, and compliance monitoring features to ensure that LLM-driven agents operate securely in regulated environments such as finance, healthcare, and legal.

[0014] The system for adaptive orchestration of LLM-driven agents across multi-domain workflows, which can provide visual dashboards and analytics tools that track AI agent performance, task completion time, and workflow efficiency, enabling real-time decision-making and system adjustments.

[0015] The system for adaptive orchestration of LLM-driven agents across multi-domain workflows, adaptable to various applications including customer service automation, financial advisory, legal research, health diagnostics, content creation, and software development.

[0016] In one embodiment, a system for the adaptive orchestration of LLM-driven agents in cross-domain workflows is provided. The system intelligently assigns tasks to specialized AI agents based on context understanding, workload distribution, and domain expertise, thus ensuring efficient and accurate task execution. Unlike conventional rule-based or static AI-driven automation systems, the proposed invention leverages reinforcement learning, machine learning-based decision models, and real-time data analytics to dynamically optimize workflows. The system consists of a multi-agent collaboration framework that enables seamless communication between LLM-driven agents, reducing redundancies and increasing overall efficiency.The system integrates context-dependent decision-making mechanisms that ensure task delegation adapts to real-time user input, operational conditions, and changing priorities. Furthermore, it incorporates autonomous learning and self-optimization techniques to refine task execution strategies over time. The system is designed for scalability and interoperability, enabling integration with enterprise systems, APIs, databases, and third-party AI services in industries such as customer service, financial advisory, legal research, healthcare diagnostics, content creation, and software development. The system also ensures data security, compliance monitoring, and robust access control to meet regulatory standards in sensitive areas.

[0017] Furthermore, the system offers real-time monitoring and performance analytics, providing companies with actionable insights into workflow efficiency, task completion times, and agent performance. Companies can also customize workflow rules, AI behavior, and task assignment preferences, allowing the system to be tailored to specific operational requirements. By combining intelligent task orchestration, multi-agent collaboration, real-time optimization, and user-centric customization, the proposed invention significantly improves the efficiency, adaptability, and security of AI-driven workflow automation, overcoming the limitations of traditional systems.

[0018] The invention is explained again below with reference to the figure. It shows: Fig. : a system for the adaptive orchestration of LLM-driven agents across multi-domain workflows.

[0019] Fig.shows a system for the adaptive orchestration of LLM-driven agents across multi-domain workflows. The system for the adaptive orchestration of LLM-driven agents across multi-domain workflows comprises an orchestration module, a multi-agent collaboration module, a real-time decision-making module, a reinforcement learning-based optimization module, a monitoring and analytics module, an integration layer, and a security and compliance module. The orchestration module is configured to dynamically assign tasks to LLM-driven agents based on contextual understanding, workload distribution, and domain knowledge. The multi-agent collaboration module is configured to enable seamless communication between multiple LLM-driven agents to exchange task-related information, reduce redundancies, and optimize workflow execution.The real-time decision-making module is configured to leverage machine learning algorithms to evaluate task priorities, user intent, and operating conditions to enable adaptive workflow adjustments. The reinforcement learning-based optimization module is configured to continuously refine task delegation rules and workflow strategies based on historical task performance and feedback.

[0020] The monitoring and analytics module is configured to provide real-time insights into AI agent performance, task completion times, and workflow efficiency via visual dashboards. The integration layer is configured to enable seamless interoperability with enterprise systems, APIs, databases, and third-party AI services to ensure cross-domain applicability. The security and compliance module is configured to include access control mechanisms, data encryption, and regulatory compliance monitoring to ensure secure and compliant AI operations. The orchestration module leverages a reinforcement learning model to continuously optimize task allocation based on real-time workload conditions and agent efficiency metrics.The multi-agent collaboration module enables LLM-driven agents to share context, resolve dependencies, and coordinate responses for complex multi-step tasks. The real-time decision-making module leverages a combination of natural language processing (NLP), sentiment analysis, and contextual knowledge to refine task allocation strategies. The reinforcement learning-based optimization module dynamically updates agent selection and workflow execution based on feedback loops from past task results. The monitoring and analytics module provides predictive insights into workflow efficiency using AI-driven analytics and performance benchmarking.

[0021] The integration layer supports API-based connectivity, enabling seamless interaction with industry-specific software platforms and external AI services. The security and compliance module ensures compliance with regulatory standards, including GDPR, HIPAA, and industry-specific data privacy laws. The orchestration module includes a user-configurable interface that allows organizations to customize workflow preferences, task priority levels, and AI behavior settings. The adaptive orchestration mechanism operates in a cloud-based or hybrid environment, ensuring scalability and high availability for enterprise-wide deployment. List of reference symbols 100 systems

Claims

[1] A system for adaptive orchestration of LLM-controlled agents across multi-domain workflows, comprising: an orchestration module configured to dynamically assign tasks to LLM-driven agents based on contextual understanding, workload balancing, and domain expertise; a multi-agent collaboration module configured to enable seamless communication between multiple LLM-controlled agents to exchange task-related information, reduce redundancies, and optimize workflow execution; a real-time decision-making module configured to leverage machine learning algorithms to evaluate task priority, user intent, and operating conditions to enable adaptive workflow adjustments; a reinforcement learning-based optimization module configured to continuously refine task delegation rules and workflow strategies based on historical task performance and feedback; a monitoring and analytics module configured to provide real-time insights into AI agent performance, task completion times, and workflow efficiency via visual dashboards; an integration layer that enables seamless interoperability with enterprise systems, APIs, databases, and third-party AI services to ensure cross-domain applicability; and a security and compliance module configured to include access control mechanisms, data encryption, and regulatory compliance monitoring to ensure secure and compliant AI operations. [2] The system of claim 1, wherein the orchestration module uses a reinforcement learning model to continuously optimize task allocation based on real-time workload conditions and agent efficiency metrics. [3] The system of claim 1, wherein the multi-agent collaboration module enables LLM-controlled agents to exchange context, resolve dependencies, and coordinate responses for complex multi-step tasks. [4] The system of claim 1, wherein the real-time decision-making module uses a combination of natural language processing (NLP), sentiment analysis, and context awareness to refine task allocation strategies. [5] The system of claim 1, wherein the reinforcement learning-based optimization module dynamically updates agent selection and workflow execution based on feedback loops from past task results. [6] The system of claim 1, wherein the monitoring and analytics module provides predictive insights into workflow efficiency using AI-driven analytics and performance benchmarking. [7] The system of claim 1, wherein the integration layer supports API-based connectivity that enables seamless interaction with industry-specific software platforms and external AI services. [8] The system of claim 1, wherein the security and compliance module ensures compliance with regulatory standards, including GDPR, HIPAA, and industry-specific privacy laws. [9] The system of claim 1, wherein the orchestration module includes a user-configurable interface that enables organizations to customize workflow preferences, task priority levels, and AI behavior settings. [10] The system of claim 1, wherein the adaptive orchestration mechanism operates in a cloud-based or hybrid environment and ensures scalability and high availability for enterprise-wide deployment.

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