Database AI Agent Orchestration for Autonomous Cloud Workflows
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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 efficiency.
Innovation Solution
A computing services environment equipped with an autonomous agent platform that integrates generative language models, enabling autonomous agents to perform operations such as processing user input, retrieving data, generating text, and coordinating with other systems, and supporting multi-agent frameworks for seamless communication and collaboration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If generative language models are integrated into cloud-based infrastructure, then system functionality and efficiency are enhanced, but system complexity increases
Solution Approach 1:
The patent introduces an agent service as an intermediary layer between the generative language model and cloud computing resources. This agent service manages model instantiation, resource allocation, and interaction coordination, thereby enhancing system functionality while containing complexity through modular abstraction rather than direct integration of the model into the core infrastructure.
2Extent of automation
If autonomous agents are enabled to operate independently with full autonomy, then user autonomy and process optimization improve, but control and monitoring difficulty increases
Solution Approach 1:
The system implements feedback mechanisms where autonomous agents report their operations, resource usage, and performance metrics to the agent service. This allows the system to maintain control and monitor agent activities while preserving agent autonomy, as the feedback loop enables coordination and oversight without micromanaging individual agent decisions.
Solution Approach 2:
The agent service provides universal management functions that work across multiple autonomous agents simultaneously. By creating a standardized interface and common control plane that serves all agents, the system reduces control complexity despite having many independent agents, as the same management mechanisms apply universally rather than requiring custom control for each agent.
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.