AI Agent Orchestration Layer for Database Retrieval-Augmented Generation
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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 frameworks and multi-planner architectures for seamless communication and task execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
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 an intermediary layer between the generative language models and cloud computing environment that manages interactions, coordinates tasks, and handles integration complexities. This mediator component allows the system to benefit from enhanced functionality while isolating the complexity of model integration from the broader cloud infrastructure.
2Productivity
If autonomous agents are equipped with multiple planners and multi-agent frameworks, then task execution capability improves, but coordination complexity increases
Solution Approach 1:
The patent segments the autonomous agent architecture into distinct functional modules including multiple specialized planners (e.g., for different task types), coordination mechanisms, and execution components. This segmentation allows each planner to focus on specific task domains while the coordination layer manages overall task execution, improving productivity without overwhelming complexity.
Solution Approach 2:
The system dynamically selects and activates specific planners based on task requirements, rather than maintaining all planners in constant operation. This dynamic approach allows the multi-planner architecture to adapt to different task scenarios, improving execution capability while managing coordination complexity through selective activation.
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.