Architecture-as-Code Framework for Traceable Multi-Cloud Automation
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Solution Overview
Problem
Enterprises face challenges in creating and managing multi-cloud environments due to vendor lock-in, which restricts flexibility and increases costs, and existing AI/ML solutions can produce hallucinations, leading to inefficiencies and operational complexities.
Innovation Solution
A unified framework for architecture as code (AaC) using AI-driven cloud solutions, with self-healing capabilities, modular design, and technology agnosticism, enabling seamless integration and customization of infrastructure management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional approaches are used to create architecture solutions, then the architecture can be structured for a specific platform, but traceability is lost and operational complexities increase
Solution Approach 1:
The patent introduces an intermediary layer between business requirements and infrastructure deployment. This intermediary is represented by the architecture-as-code framework that translates business requirements into actionable infrastructure configurations, maintaining traceability throughout the entire pipeline from requirement to deployment.
Solution Approach 2:
The patent implements feedback mechanisms through automated validation, testing, and monitoring systems that continuously check infrastructure deployments against architectural specifications. This feedback loop ensures traceability is maintained while automatically resolving operational complexities by identifying and correcting deviations from the intended architecture.
2Extent of automation
If AI/ML solutions are used to generate cloud infrastructure, then automation can be improved, but hallucinations occur leading to inefficiencies
Solution Approach 1:
The patent implements self-service capabilities where the system automatically validates, tests, and monitors its own generated infrastructure configurations. The architecture-as-code framework enables the system to self-verify against predefined architectural specifications and self-correct errors, reducing reliance on human intervention while maintaining high reliability.
Solution Approach 2:
The patent replaces manual mechanical verification processes with automated computational validation systems. Instead of human reviewers manually checking infrastructure configurations, the system uses automated code validation, unit testing, and integration testing mechanisms that reliably detect hallucinations and errors in AI-generated infrastructure code.
3Ease of manufacture
If static architecture solutions are provided to engineering teams, then the architecture can be defined once, but adaptability to changing cloud environments is reduced
Solution Approach 1:
The patent transforms static architecture definitions into dynamic, living specifications that can automatically adapt to changing cloud environments. The architecture-as-code framework uses parameterized templates and configuration files that can be dynamically adjusted based on current cloud provider capabilities, workload requirements, and best practices, enabling the architecture to evolve without requiring complete redesign.
Solution Approach 2:
The patent segments the architecture definition into modular, reusable components that can be independently configured and combined. This segmentation allows the architecture to be easily adapted to different cloud environments by selectively combining and configuring modular components rather than redesigning the entire architecture, thereby maintaining ease of definition while achieving high adaptability.
4Adaptability or versatility
If multi-cloud environments are implemented, then flexibility and cost optimization can be improved, but device complexity and operational challenges increase
Solution Approach 1:
The patent creates a universal architecture-as-code framework that can be applied across multiple cloud providers without requiring separate operational procedures for each provider. The framework uses standardized, provider-agnostic code templates and configuration formats that can be executed on different cloud platforms, enabling multi-cloud flexibility while reducing operational complexity through uniform management processes.
Data Source
AI summary
Provided is a cloud management platform, including modular components. Each component includes its own application programming interface (API), ensuring seamless integration and customization of infrastructure management capabilities. The components include an input interface, an architect generative pre-trained transformer (AGPT) assistant, a model parser, a code template repository, a mapping engine, a monitoring service, an artificial intelligence (AI)/machine learning (ML)/large language model (LLM) analytics engine, an automated detection and remediation engine, and a chaos testing service application.