Generative Language Model Planner for Modular Cloud Agent Orchestration
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Solution Overview
Problem
Existing systems lack effective integration of generative language models with cloud-based computing environments, limiting their utilization in managing interactions and tasks within database systems.
Innovation Solution
A conversational chat system is integrated with a unified metadata framework, enabling interaction between user interfaces, artificial intelligence models, and data sources, with a trust layer for security and a multi-agent, multi-planner framework for plan execution, supporting human-interactive disambiguation and enrichment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If generative language models are integrated into cloud-based infrastructure, then task management and data interaction capabilities are enhanced, but system complexity increases
Solution Approach 1:
The system divides the integration architecture into distinct modular components: a conversational chat system for user interaction, a unified metadata framework for data management, a trust layer for security, and a multi-agent planner for task execution. Each component handles specific functions independently, reducing overall system complexity while enhancing versatility in task management and data interaction.
Solution Approach 2:
The unified metadata framework serves multiple purposes simultaneously: it provides data structure definitions, enables retrieval augmented generation, supports context retention across conversations, and facilitates interaction between different system components. This multi-functionality reduces the need for separate specialized systems, thereby reducing complexity while improving adaptability.
2Productivity
If a unified metadata framework is implemented for integrating AI models with data sources, then data interaction efficiency is improved, but framework complexity increases
Solution Approach 1:
The unified metadata framework performs multiple critical functions through a single cohesive structure: it defines data schemas for AI models, manages data retrieval operations, maintains context across conversations, and enables retrieval augmented generation. This consolidation improves data interaction efficiency while avoiding the complexity of multiple separate frameworks.
Solution Approach 2:
The unified metadata framework acts as an intermediary layer between the conversational chat system and the data sources/AI models. It translates diverse data formats into standardized representations and manages the complexity of data retrieval and context management, thereby improving efficiency without exposing the underlying complexity to the user interface.
3Adaptability or versatility
If a multi-agent, multi-planner framework is used for plan execution, then task execution flexibility is improved, but system complexity increases
Solution Approach 1:
The planning system is divided into multiple independent agents, each responsible for specific aspects of task execution. These agents can operate autonomously or coordinate with each other, providing flexibility in plan execution while maintaining modular architecture that reduces overall system complexity through clear separation of concerns.
Solution Approach 2:
The multi-agent planner enables dynamic adaptation to changing task requirements and data contexts. The system can reconfigure which agents execute which tasks based on real-time conditions, improving flexibility without requiring a completely redesigned system architecture for each scenario.
4Reliability
If a trust layer is added for security in cloud computing environments, then security is improved, but system complexity increases
Solution Approach 1:
The trust layer functions as an intermediary security layer positioned between the conversational chat system and the cloud infrastructure. It handles authentication, authorization, and data protection operations centrally, improving security without requiring security mechanisms to be distributed throughout every component of the system.
Data Source
AI summary
A computing services environment may include a database system storing database records for client organizations accessing computing services including a conversational chat interface, an application server providing access to the conversational chat interface, a metadata repository storing metadata entries describing and defining interaction data for interacting with agents, and an orchestration service configured to execute an orchestration process based on a natural language request message received via the conversational chat interface. An input prompt including the natural language request message and agent descriptions selected from the plurality of metadata entries may be determined and transmitted to a generative language model. A prompt completion including a selection of the designated agent based on the plurality of agent description may be received from the generative language model. Novel text responsive to the natural language request message may be generated by transmitting a request to the designated agent.