Autonomous Agent Orchestration for AI-Driven Database Actions
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Solution Overview
Problem
Existing systems for managing interactions between cloud computing environments and generative language models are limited, necessitating improved integration of these models into cloud-based infrastructure to enhance functionality and autonomy.
Innovation Solution
A computing services environment equipped with an autonomous agent platform that creates and executes customized autonomous agents, capable of performing operations such as natural language processing, data retrieval, and workflow management, integrating with generative language models and various data sources, and facilitating communication across multiple channels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If generative language models are integrated into cloud-based infrastructure, then functionality and autonomy are enhanced, but system complexity increases
Solution Approach 1:
The patent segments the autonomous agent system into distinct modular components: a configuration layer for agent definition and metadata management, an agent platform for instantiation and execution, and an orchestration engine for task coordination. This modular architecture allows generative language models to be integrated as discrete functional units within the cloud infrastructure, enhancing functionality while managing system complexity through clear separation of concerns and independent deployable modules.
2Extent of automation
If autonomous agents are created with full capabilities for natural language processing and task execution, then user autonomy is enhanced, but computational resources are consumed
Solution Approach 1:
The patent implements partial action by allowing autonomous agents to operate with configurable capability levels. The configuration layer enables selective activation of specific agent functions and metadata schemas based on task requirements, rather than deploying full-capability agents for all operations. This approach provides sufficient user autonomy for common tasks while conserving computational resources by avoiding excessive processing power allocation for every agent instance.
3Reliability
If customized autonomous agents are instantiated for specific tasks, then task performance is improved, but system resource consumption increases
Solution Approach 1:
The patent implements universality through a shared agent platform that can instantiate multiple customized autonomous agents using a common configuration layer and metadata framework. The system supports multi-functionality by allowing the same underlying infrastructure to serve diverse task requirements through configurable agent definitions. This enables specialized agents for different tasks (sales pipeline management, procurement, etc.) while sharing common computational resources, thereby improving task performance without proportionally increasing overall system resource consumption.
Data Source
AI summary
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.