AI Agent Orchestration in Database Systems With Multi-Planner Control
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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 environments that manages interactions systematically. This intermediary framework coordinates model inputs/outputs with cloud services, handling complexity internally while presenting simplified interfaces externally, thus enhancing functionality without proportionally increasing observable system complexity.
Solution Approach 2:
The system integrates multiple functions within the language model interactions including text generation, data retrieval, task coordination, and cloud service management. By making the language model interface universal and multi-functional, the system enhances overall functionality while reducing the need for separate specialized components, thereby managing system complexity.
2Productivity
If autonomous agents are implemented with multi-agent frameworks, then task execution capability improves, but coordination overhead increases
Solution Approach 1:
The patent merges coordination functions directly into the language model processing pipeline. Multiple autonomous agents share common language model resources and coordination mechanisms, combining what would otherwise be separate coordination systems. This merging approach enables improved task execution through multi-agent collaboration while reducing overall coordination overhead by eliminating redundant coordination layers.
Solution Approach 2:
Autonomous agents in the system are equipped with self-service capabilities to manage their own coordination needs through language model interactions. Agents can autonomously request tasks, report status, and coordinate with other agents using standardized language protocols, reducing the need for external coordination overhead while maintaining high task execution capability.
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.