Agentically-Orchestrated Foundational Models for Secure Cloud Design
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Developing robust cloud architectures is complex, resource-intensive, and time-consuming, often requiring multiple subject matter experts, and results in architectures that are vulnerable to security exploits without adequate expertise, making it difficult for smaller entities to leverage cloud computing advantages.
Innovation Solution
Utilizing agentically-orchestrated foundational models, such as Large Foundational Models (LFMs), to emulate roles like cloud architects and designers, generating proposed generic component placeholders and visual representations of cloud architectures based on user requirements, reducing the need for extensive expertise and resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple subject matter experts are used to develop cloud architectures, then the quality and robustness of the architecture improves, but the resource consumption and time required increase significantly
Solution Approach 1:
The patent creates virtual copies of subject matter experts through AI agents that emulate the knowledge, reasoning, and decision-making processes of human cloud architects, security experts, and developers. These digital twins can process requirements and generate architecture designs simultaneously without the resource constraints of human experts
Solution Approach 2:
The system segments the expertise of multiple subject matter experts into separate specialized AI agents, each responsible for specific aspects such as security, scalability, and cost-optimization. These segmented agents work in parallel and coordinate their outputs to produce comprehensive architecture designs
2Object-affected harmful factors
If multiple subject matter experts are involved in architecture development, then security vulnerability detection improves, but the complexity and resource requirements worsen
Solution Approach 1:
The patent introduces an orchestration layer that mediates between multiple specialized AI agents, coordinating their interactions and synthesizing their outputs. This intermediary management system reduces the complexity burden on individual agents while maintaining comprehensive security analysis through specialized security-focused agents
3Reliability
If traditional manual methods are used for cloud architecture development, then expertise quality is maintained, but time consumption and resource intensity increase
Solution Approach 1:
The system performs preliminary actions by pre-training AI agents on extensive datasets of cloud architecture patterns, security vulnerabilities, and best practices before actual architecture development. This preliminary knowledge acquisition enables the agents to quickly generate high-quality designs without requiring extensive real-time human expertise
Solution Approach 2:
The patent implements continuous iteration and refinement cycles where AI agents continuously improve architecture designs based on feedback from multiple specialized agents and validation against security and performance criteria, maintaining high quality while reducing time through automated continuous improvement
Data Source
AI summary
User response information is obtained comprising information indicative of cloud architecture requirements for a cloud architecture to fulfill. Based on the user response information, a plurality of agentic orchestration models are used to generate a respective plurality of role outputs, each of the plurality of agentic orchestration models comprising a machine-learned language model prompted to fulfill a corresponding cloud architecting role of a plurality of cloud architecting roles, wherein one of the plurality of role outputs is indicative of a plurality of proposed generic component placeholders for components necessary to meet the cloud architecture requirements. Based on the plurality of role outputs, a proposed architecture output is generated comprising a visual representation of the proposed generic component placeholders.