AI Agent Runtime In Database Systems
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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 generating text, while supporting multi-channel communication and a unified platform for accessing AI agents.
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 patent introduces an AI agent runtime as an intermediary layer between cloud computing environments and generative language models. This runtime manages the complexity of model integration, execution, and interaction, allowing cloud systems to leverage AI capabilities without directly handling the underlying complexity of generative models. The runtime abstracts the interactions, providing a simplified interface while maintaining efficient model integration.
Solution Approach 2:
The AI agent runtime is designed as a universal platform that can manage multiple generative language models and various types of AI agents within the cloud infrastructure. It provides multi-functional capabilities including model deployment, execution management, interaction coordination, and resource allocation, reducing the need for separate specialized systems for each function.
2Ease of operation
If autonomous agents are deployed to perform operations autonomously, then user autonomy is enhanced, but control and monitoring difficulty increases
Solution Approach 1:
The AI agent runtime implements feedback mechanisms that enable continuous monitoring of autonomous agent operations. It tracks agent actions, outcomes, and performance metrics, providing feedback loops that allow system administrators to observe, measure, and control autonomous operations. This feedback capability maintains transparency and control while preserving user autonomy.
Solution Approach 2:
The system employs dynamic management of autonomous agents, where the runtime can adjust agent behaviors, priorities, and execution parameters in real-time based on operational context and performance. This dynamic control allows the system to adapt to changing conditions while maintaining observability and control over autonomous operations.
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.