Autonomous Agent Framework for Decentralized Task Coordination
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Solution Overview
Problem
Existing systems face challenges in handling large-scale deployments of autonomous agents due to centralized control bottlenecks, difficulty in agent coordination, lack of interoperability, and insufficient mechanisms for collaboration and security, leading to inefficiencies and performance degradation.
Innovation Solution
A decentralized computing network with a software framework that enables modular and extensible autonomous agents, utilizing a context-builder module, protocol generator, and build executor to facilitate seamless communication, task execution, and secure collaboration across multiple problem domains.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a centralized architecture is used to control autonomous agents, then coordination and communication are simplified, but performance degrades and inefficiencies occur as the number of agents and task complexity increase
Solution Approach 1:
The patent divides the centralized control architecture into decentralized autonomous agents that operate independently. Each agent is segmented with its own decision-making capabilities, allowing parallel processing and eliminating the single-point bottleneck. This segmentation enables the system to scale without performance degradation while maintaining coordination through standardized communication protocols.
Solution Approach 2:
The patent transitions from a single-dimensional centralized control model to a multi-dimensional decentralized network. Agents operate across multiple dimensions including autonomous decision-making, collaborative task execution, and distributed communication. This dimensional expansion allows the system to handle complex tasks through parallel agent operations rather than sequential centralized processing.
2Adaptability or versatility
If autonomous agents operate in a decentralized manner, then system scalability is improved, but coordinating their actions becomes increasingly difficult
Solution Approach 1:
The patent implements universal standardized protocols that enable autonomous agents to communicate and coordinate across diverse domains. These protocols provide multi-functional capabilities including task assignment, information exchange, and collaborative decision-making. This universality allows agents with different functionalities to work together seamlessly, maintaining coordination simplicity while enabling system scalability.
Solution Approach 2:
The patent introduces intermediary communication protocols and frameworks that facilitate coordination between decentralized agents. These intermediaries standardize interaction patterns, enabling agents to exchange information and coordinate actions without direct complex point-to-point connections. This mediation layer reduces coordination complexity while preserving decentralized autonomy and scalability.
3Adaptability or versatility
If existing autonomous agents are used without standardized protocols, then implementation flexibility is maintained, but interoperability challenges arise
Solution Approach 1:
The patent establishes universal standardized protocols that maintain implementation flexibility while ensuring reliable interoperability. These protocols provide common interfaces and communication standards that allow agents from different implementations to work together reliably. The standards are designed to be flexible enough to accommodate various implementation approaches while guaranteeing consistent interaction patterns and data exchange formats.
4Device complexity
If autonomous agents lack robust collaboration mechanisms, then system simplicity is maintained, but problem-solving capabilities are limited
Solution Approach 1:
The patent implements preliminary collaboration mechanisms including pre-established communication protocols, predefined task assignment patterns, and structured information exchange formats. These preliminary frameworks are built into the agent architecture from the start, enabling seamless collaboration without adding operational complexity. Agents can immediately engage in coordinated problem-solving using these pre-configured mechanisms, enhancing productivity while maintaining system simplicity.
Data Source
AI summary
Disclosed is system enabling application of autonomous agents (AAs) across problem domains. System comprises decentralised computing network to implement software framework (SF). SF comprises client-agent device (client-AA) to receive service request (SR), generate objective in vector(s) recorded in vector database (VD), send objective to agent-device (AA/micro-AA). Agent-device comprises context-builder software module (CBSM) to send query(-ies) to machine learning model agent (ML-Model AA) and/or access VD to retrieve task(s) associated with previous queries to obtain tasks associated with objective. CBSM to obtain order of task execution from ML-Model AA, to obtain list including AAs, to block communication signals between client-AA and AAs not associated with objective, to associate AA(s) with task(s); protocol generator software module comprising domain-independent protocol specification language (DIPSL) generate protocol specification(PS(s)) for task execution by AA(s); build executor software module compose task(s) in order, compose AA into further autonomous agent (further-AA), encrypt access to further-AA, further-AA implement PS(s) to execute each task and SR.