AI Agent Planner Orchestration for Database Service 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 autonomously performs operations such as processing user input, formulating plans, retrieving data, and generating text, while integrating with generative language models to facilitate seamless communication and collaboration among agents, supporting multi-agent and multi-planner frameworks, and enabling proactive task execution.
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:
An intermediary layer is introduced between the cloud computing environment and generative language models to manage interactions. This intermediary handles model deployment, inference requests, and resource allocation, thereby enhancing functionality while abstracting and managing the underlying system complexity.
Solution Approach 2:
The system integrates multiple functions including model deployment, inference processing, resource management, and interaction coordination into a unified cloud-based infrastructure. This multi-functional approach enhances overall efficiency while consolidating complexity into a single manageable system rather than separate components.
2Ease of operation
If autonomous agents are deployed to perform operations autonomously, then user autonomy is enhanced and manual workload is reduced, but control and management complexity increases
Solution Approach 1:
Autonomous agents are deployed to perform operations independently without requiring continuous user intervention. The agents autonomously execute tasks, make decisions, and manage their own workflows, thereby enhancing user autonomy while reducing manual workload. The control complexity is managed through predefined policies and monitoring mechanisms.
Solution Approach 2:
Feedback mechanisms are implemented to monitor and manage autonomous agent operations. The system continuously receives feedback from agent actions and adjusts control strategies accordingly, enabling effective management of multiple autonomous agents while maintaining user autonomy and reducing manual intervention requirements.
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.