Autonomous Agent Orchestration for AI Planning in Database Services
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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, formulating plans, retrieving data, and coordinating with other systems, and providing multi-channel communication.
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 system segments the integration of generative language models into distinct functional layers: a cloud computing environment layer for infrastructure, a generative language model layer for AI processing, and an autonomous agent platform layer for coordination. This segmentation allows each component to be optimized independently while reducing overall system complexity through modular architecture.
Solution Approach 2:
An autonomous agent platform is introduced as an intermediary between the cloud computing environment and generative language models. This intermediary layer handles the coordination, authentication, and integration tasks, simplifying the interaction between the complex components and enabling efficient functionality without directly exposing system complexity to users.
2Ease of operation
If autonomous agents are enabled to perform multiple operations, then user autonomy is enhanced, but device complexity increases
Solution Approach 1:
The autonomous agent platform is designed as a universal system that can perform multiple operations including processing user input, formulating plans, retrieving data, and coordinating with other systems. By building a multi-functional platform with standardized interfaces and capabilities, the system enables diverse agent functions without proportionally increasing complexity, as new functions are added through composition rather than building from scratch.
3Productivity
If complex workflows are automated, then productivity increases, but measurement and detection difficulty increases
Solution Approach 1:
The autonomous agent platform incorporates feedback mechanisms that continuously monitor workflow execution, track agent actions, and measure productivity outcomes. This feedback loop enables the system to detect and measure complex automated workflows by recording intermediate states and results, making visibility into automated processes possible without reducing their complexity or automation level.
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.