AI Agent Orchestration Layer for Cloud Database Action Planning
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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, while supporting multi-agent orchestration and flexible configuration through a unified metadata framework and AI models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If generative language models are integrated into cloud-based infrastructure, then functionality and efficiency are enhanced, but system complexity increases
Solution Approach 1:
The patent introduces a computing service as an intermediary layer between cloud infrastructure and generative language models. This service manages the complexity of model integration, deployment, and interaction, allowing the cloud system to benefit from AI capabilities without directly handling the inherent complexity of generative models. The intermediary abstracts away the complexity while enabling efficient functionality.
Solution Approach 2:
The computing service is designed to provide universal access to generative language models through standardized interfaces. It can handle multiple types of models, deployment configurations, and interaction patterns through a single unified service layer, reducing the need for separate specialized systems and simplifying the overall architecture while maintaining high functionality.
2Ease of operation
If autonomous agents are deployed to perform operations, then user autonomy is enhanced, but management complexity increases
Solution Approach 1:
The autonomous agents are designed to perform operations independently based on predefined configurations and goals. The agents self-manage their execution, monitoring, and adaptation without requiring continuous human intervention or complex management overhead. This self-service capability enables high user autonomy while keeping management complexity at acceptable levels.
Solution Approach 2:
The system segments autonomous agent functionality into discrete, manageable components that can be independently configured and monitored. Each agent operates as an isolated unit with its own configuration, allowing complex autonomous behaviors to be managed through composition of simpler, controllable segments rather than monolithic complex systems.
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.