<\/a><\/figure>\n\n\t\t\n\t\tCloud Computing has revolutionised how we design applications, but it has also introduced growing complexity. Today, transforming a business need expressed in natural language \u2013 for example, “We need an architecture that stores our customer data securely” \u2013 into concrete, optimised technical infrastructure is a manual, slow, and often costly process.<\/p>\n\n\n\n
It’s a real headache for architects. They must juggle multiple services, security rules, and cost constraints.<\/p>\n\n\n\n
Our Solution: Generative AI at the Helm<\/strong><\/h2>\n\n\n\nThe StructurIT project aims to bridge this gap. Our ambition is to automate the design of technical architectures in the cloud using generative artificial intelligence.<\/p>\n\n\n\n
We are developing a solution based on Large Language Models (LLMs) and AI agents capable of:<\/p>\n\n\n\n
\n- Understanding the user’s business need, even if it’s vague or informal<\/li>\n\n\n\n
- Reasoning about constraints (cost, performance, security, etc.)<\/li>\n\n\n\n
- Generating Infrastructure as Code (IaC) (such as Terraform<\/a>) for infrastructure ready to be deployed<\/li>\n<\/ul>\n\n\n\n
We’re not just seeking to generate text, but to build a true intelligent bridge between a company’s strategic vision and the technical reality of its infrastructure through AI Cloud Architecture.<\/p>\n\n\n\n
A Laboratory of Ideas and Technologies<\/strong><\/h2>\n\n\n\nStructurIT is part of the Autonomous Cloud axis, one of the pillars of Devoteam Research. Our department is dedicated to exploring how AI can make the cloud more intelligent, efficient, and secure. It’s a unique research and experimentation environment. Every idea is tested and every discovery pushes the boundaries of what’s possible.<\/p>\n\n\n\n
For Devoteam consultants, this is an opportunity to:<\/p>\n\n\n\n
\n- Develop cutting-edge expertise in generative AI technologies<\/a>, agent orchestration, knowledge management, and dynamic APIs<\/li>\n\n\n\n
- Actively participate in creating an innovative and highly concrete GenAI use case<\/li>\n\n\n\n
- Apply their skills as an architect, developer, or data scientist to non-trivial problems and exciting AI Cloud architecture challenges<\/li>\n<\/ul>\n\n\n\n
AQG4SD: A Concrete and Interactive Demo<\/strong><\/h2>\n\n\n\nTo illustrate our work, we developed an interactive tool, AQG4SD (Automated GraphQL Query Generation for Service Discovery). This tool automates the discovery and selection of cloud services by translating natural language requirements into executable GraphQL queries.<\/p>\n\n\n\n
The limitations that led to the development of our tool, AQG4SD, stem from a simple but major problem: the Cloud is constantly changing.<\/p>\n\n\n\n
Previously, existing solutions for finding the right cloud service relied on static service catalogues. It’s like using an old paper catalogue to shop. As soon as a service is updated or a price changes, the catalogue becomes obsolete. These solutions used “ontologies” \u2013 very detailed service descriptions that required significant effort to create and were never up to date.<\/p>\n\n\n\n
Our tool solves this problem by adopting a radically different approach. Instead of relying on a fixed catalogue, AQG4SD queries public APIs in real time. These APIs track prices and offerings from major cloud providers (such as AWS, Azure, GCP).<\/p>\n\n\n\n
Imagine instead an assistant that, rather than searching through an outdated paper catalogue, uses an ultra-efficient, up-to-date search engine to instantly give you the best services and their current prices. By automating the generation of GraphQL queries to interrogate these APIs, our tool overcomes the main flaw of previous methods. It adapts to the dynamic nature of the cloud and ensures that the information used for selection is always relevant.<\/p>\n\n\n\n
The Tool in Brief<\/strong><\/h2>\n\n\n\nAQG4SD’s architecture relies on a modular agent-based pipeline, designed to overcome the limitations of monolithic approaches. The key components are:<\/p>\n\n\n\n
A central LLM.<\/strong> A Large Language Model (LLM), such as Gemini or GPT-4o, serves as the reasoning and generation engine. Its role is to understand user intent and formulate structured queries.<\/p>\n\n\n\nAn advanced prompt engineering strategy.<\/strong> To guide the LLM, we dynamically inject the GraphQL schema of the target API and in-context learning examples. This approach reduces model inconsistencies and “hallucinations.”<\/p>\n\n\n\nTools (MCP).<\/strong> The LLM agent is equipped with the ability to interact with external tools. This is where the MCP (Model Context Protocol) is crucial: it exposes a dedicated tool to the LLM for executing GraphQL queries, allowing the agent to “perform” a concrete action outside the model.<\/p>\n\n\n\nOrchestration logic.<\/strong> The orchestrator, implemented with LangGraph, manages the workflow. It breaks down the user’s query, interrogates the appropriate tools (via the MCP API), and aggregates results to provide a complete response.<\/p>\n\n\n\nThe Multi-Query Orchestration Challenge<\/strong><\/h2>\n\n\n\nOne of the major scientific challenges in AI Cloud Architecture lies in the inherent weakness of public pricing APIs, such as Infracost’s, which don’t natively support complex selection queries. A simple need like “find the cheapest service with at least 8GB of RAM” cannot be translated into a single API query.<\/p>\n\n\n\n
Our solution overcomes this challenge by emulating operators like min, max, avg, order by, limit, etc., commonly found in database query languages like SQL, but missing for querying these GraphQL servers.<\/p>\n\n\n\n
AQG4SD’s orchestrator is capable of:<\/p>\n\n\n\n
Intelligently generating multiple GraphQL queries<\/strong> to obtain all necessary raw data. By “intelligently” we mean that the model is tasked with generating one or more parameterised GraphQL queries, along with value ranges for each parameter. This kills two birds with one stone: reducing the number of tokens to generate (and thus the solution’s cost) while simultaneously increasing the solution’s accuracy by relieving the LLM from generating all possible combinations (the Cartesian product) of parameters.<\/p>\n\n\n\nDelegating to an algorithmic module<\/strong> implemented within the MCP server the generation of all possible parameter combinations, the construction of final queries, and their execution.<\/p>\n\n\n\nApplying filters<\/strong> (for example, for services with a minimum RAM) and sorts (by price) in post-processing of obtained results.<\/p>\n\n\n\nPresenting a single, ordered, and filtered response<\/strong> to the user.<\/p>\n\n\n\nThis multi-query and intelligent aggregation process is at the heart of our AI Cloud architecture, as it enables us to propose a reliable and optimised solution to our problem without having to modify the underlying API.<\/p>\n\n\n\n
Demo<\/strong><\/h2>\n\n\n\n
We’re not just seeking to generate text, but to build a true intelligent bridge between a company’s strategic vision and the technical reality of its infrastructure through AI Cloud Architecture.<\/p>\n\n\n\n
A Laboratory of Ideas and Technologies<\/strong><\/h2>\n\n\n\nStructurIT is part of the Autonomous Cloud axis, one of the pillars of Devoteam Research. Our department is dedicated to exploring how AI can make the cloud more intelligent, efficient, and secure. It’s a unique research and experimentation environment. Every idea is tested and every discovery pushes the boundaries of what’s possible.<\/p>\n\n\n\n
For Devoteam consultants, this is an opportunity to:<\/p>\n\n\n\n
\n- Develop cutting-edge expertise in generative AI technologies<\/a>, agent orchestration, knowledge management, and dynamic APIs<\/li>\n\n\n\n
- Actively participate in creating an innovative and highly concrete GenAI use case<\/li>\n\n\n\n
- Apply their skills as an architect, developer, or data scientist to non-trivial problems and exciting AI Cloud architecture challenges<\/li>\n<\/ul>\n\n\n\n
AQG4SD: A Concrete and Interactive Demo<\/strong><\/h2>\n\n\n\nTo illustrate our work, we developed an interactive tool, AQG4SD (Automated GraphQL Query Generation for Service Discovery). This tool automates the discovery and selection of cloud services by translating natural language requirements into executable GraphQL queries.<\/p>\n\n\n\n
The limitations that led to the development of our tool, AQG4SD, stem from a simple but major problem: the Cloud is constantly changing.<\/p>\n\n\n\n
Previously, existing solutions for finding the right cloud service relied on static service catalogues. It’s like using an old paper catalogue to shop. As soon as a service is updated or a price changes, the catalogue becomes obsolete. These solutions used “ontologies” \u2013 very detailed service descriptions that required significant effort to create and were never up to date.<\/p>\n\n\n\n
Our tool solves this problem by adopting a radically different approach. Instead of relying on a fixed catalogue, AQG4SD queries public APIs in real time. These APIs track prices and offerings from major cloud providers (such as AWS, Azure, GCP).<\/p>\n\n\n\n
Imagine instead an assistant that, rather than searching through an outdated paper catalogue, uses an ultra-efficient, up-to-date search engine to instantly give you the best services and their current prices. By automating the generation of GraphQL queries to interrogate these APIs, our tool overcomes the main flaw of previous methods. It adapts to the dynamic nature of the cloud and ensures that the information used for selection is always relevant.<\/p>\n\n\n\n
The Tool in Brief<\/strong><\/h2>\n\n\n\nAQG4SD’s architecture relies on a modular agent-based pipeline, designed to overcome the limitations of monolithic approaches. The key components are:<\/p>\n\n\n\n
A central LLM.<\/strong> A Large Language Model (LLM), such as Gemini or GPT-4o, serves as the reasoning and generation engine. Its role is to understand user intent and formulate structured queries.<\/p>\n\n\n\nAn advanced prompt engineering strategy.<\/strong> To guide the LLM, we dynamically inject the GraphQL schema of the target API and in-context learning examples. This approach reduces model inconsistencies and “hallucinations.”<\/p>\n\n\n\nTools (MCP).<\/strong> The LLM agent is equipped with the ability to interact with external tools. This is where the MCP (Model Context Protocol) is crucial: it exposes a dedicated tool to the LLM for executing GraphQL queries, allowing the agent to “perform” a concrete action outside the model.<\/p>\n\n\n\nOrchestration logic.<\/strong> The orchestrator, implemented with LangGraph, manages the workflow. It breaks down the user’s query, interrogates the appropriate tools (via the MCP API), and aggregates results to provide a complete response.<\/p>\n\n\n\nThe Multi-Query Orchestration Challenge<\/strong><\/h2>\n\n\n\nOne of the major scientific challenges in AI Cloud Architecture lies in the inherent weakness of public pricing APIs, such as Infracost’s, which don’t natively support complex selection queries. A simple need like “find the cheapest service with at least 8GB of RAM” cannot be translated into a single API query.<\/p>\n\n\n\n
Our solution overcomes this challenge by emulating operators like min, max, avg, order by, limit, etc., commonly found in database query languages like SQL, but missing for querying these GraphQL servers.<\/p>\n\n\n\n
AQG4SD’s orchestrator is capable of:<\/p>\n\n\n\n
Intelligently generating multiple GraphQL queries<\/strong> to obtain all necessary raw data. By “intelligently” we mean that the model is tasked with generating one or more parameterised GraphQL queries, along with value ranges for each parameter. This kills two birds with one stone: reducing the number of tokens to generate (and thus the solution’s cost) while simultaneously increasing the solution’s accuracy by relieving the LLM from generating all possible combinations (the Cartesian product) of parameters.<\/p>\n\n\n\nDelegating to an algorithmic module<\/strong> implemented within the MCP server the generation of all possible parameter combinations, the construction of final queries, and their execution.<\/p>\n\n\n\nApplying filters<\/strong> (for example, for services with a minimum RAM) and sorts (by price) in post-processing of obtained results.<\/p>\n\n\n\nPresenting a single, ordered, and filtered response<\/strong> to the user.<\/p>\n\n\n\nThis multi-query and intelligent aggregation process is at the heart of our AI Cloud architecture, as it enables us to propose a reliable and optimised solution to our problem without having to modify the underlying API.<\/p>\n\n\n\n
Demo<\/strong><\/h2>\n\n\n\n
AQG4SD: A Concrete and Interactive Demo<\/strong><\/h2>\n\n\n\nTo illustrate our work, we developed an interactive tool, AQG4SD (Automated GraphQL Query Generation for Service Discovery). This tool automates the discovery and selection of cloud services by translating natural language requirements into executable GraphQL queries.<\/p>\n\n\n\n
The limitations that led to the development of our tool, AQG4SD, stem from a simple but major problem: the Cloud is constantly changing.<\/p>\n\n\n\n
Previously, existing solutions for finding the right cloud service relied on static service catalogues. It’s like using an old paper catalogue to shop. As soon as a service is updated or a price changes, the catalogue becomes obsolete. These solutions used “ontologies” \u2013 very detailed service descriptions that required significant effort to create and were never up to date.<\/p>\n\n\n\n
Our tool solves this problem by adopting a radically different approach. Instead of relying on a fixed catalogue, AQG4SD queries public APIs in real time. These APIs track prices and offerings from major cloud providers (such as AWS, Azure, GCP).<\/p>\n\n\n\n
Imagine instead an assistant that, rather than searching through an outdated paper catalogue, uses an ultra-efficient, up-to-date search engine to instantly give you the best services and their current prices. By automating the generation of GraphQL queries to interrogate these APIs, our tool overcomes the main flaw of previous methods. It adapts to the dynamic nature of the cloud and ensures that the information used for selection is always relevant.<\/p>\n\n\n\n
The Tool in Brief<\/strong><\/h2>\n\n\n\nAQG4SD’s architecture relies on a modular agent-based pipeline, designed to overcome the limitations of monolithic approaches. The key components are:<\/p>\n\n\n\n
A central LLM.<\/strong> A Large Language Model (LLM), such as Gemini or GPT-4o, serves as the reasoning and generation engine. Its role is to understand user intent and formulate structured queries.<\/p>\n\n\n\nAn advanced prompt engineering strategy.<\/strong> To guide the LLM, we dynamically inject the GraphQL schema of the target API and in-context learning examples. This approach reduces model inconsistencies and “hallucinations.”<\/p>\n\n\n\nTools (MCP).<\/strong> The LLM agent is equipped with the ability to interact with external tools. This is where the MCP (Model Context Protocol) is crucial: it exposes a dedicated tool to the LLM for executing GraphQL queries, allowing the agent to “perform” a concrete action outside the model.<\/p>\n\n\n\nOrchestration logic.<\/strong> The orchestrator, implemented with LangGraph, manages the workflow. It breaks down the user’s query, interrogates the appropriate tools (via the MCP API), and aggregates results to provide a complete response.<\/p>\n\n\n\nThe Multi-Query Orchestration Challenge<\/strong><\/h2>\n\n\n\nOne of the major scientific challenges in AI Cloud Architecture lies in the inherent weakness of public pricing APIs, such as Infracost’s, which don’t natively support complex selection queries. A simple need like “find the cheapest service with at least 8GB of RAM” cannot be translated into a single API query.<\/p>\n\n\n\n
Our solution overcomes this challenge by emulating operators like min, max, avg, order by, limit, etc., commonly found in database query languages like SQL, but missing for querying these GraphQL servers.<\/p>\n\n\n\n
AQG4SD’s orchestrator is capable of:<\/p>\n\n\n\n
Intelligently generating multiple GraphQL queries<\/strong> to obtain all necessary raw data. By “intelligently” we mean that the model is tasked with generating one or more parameterised GraphQL queries, along with value ranges for each parameter. This kills two birds with one stone: reducing the number of tokens to generate (and thus the solution’s cost) while simultaneously increasing the solution’s accuracy by relieving the LLM from generating all possible combinations (the Cartesian product) of parameters.<\/p>\n\n\n\nDelegating to an algorithmic module<\/strong> implemented within the MCP server the generation of all possible parameter combinations, the construction of final queries, and their execution.<\/p>\n\n\n\nApplying filters<\/strong> (for example, for services with a minimum RAM) and sorts (by price) in post-processing of obtained results.<\/p>\n\n\n\nPresenting a single, ordered, and filtered response<\/strong> to the user.<\/p>\n\n\n\nThis multi-query and intelligent aggregation process is at the heart of our AI Cloud architecture, as it enables us to propose a reliable and optimised solution to our problem without having to modify the underlying API.<\/p>\n\n\n\n
Demo<\/strong><\/h2>\n\n\n\n
AQG4SD’s architecture relies on a modular agent-based pipeline, designed to overcome the limitations of monolithic approaches. The key components are:<\/p>\n\n\n\n
A central LLM.<\/strong> A Large Language Model (LLM), such as Gemini or GPT-4o, serves as the reasoning and generation engine. Its role is to understand user intent and formulate structured queries.<\/p>\n\n\n\n An advanced prompt engineering strategy.<\/strong> To guide the LLM, we dynamically inject the GraphQL schema of the target API and in-context learning examples. This approach reduces model inconsistencies and “hallucinations.”<\/p>\n\n\n\n Tools (MCP).<\/strong> The LLM agent is equipped with the ability to interact with external tools. This is where the MCP (Model Context Protocol) is crucial: it exposes a dedicated tool to the LLM for executing GraphQL queries, allowing the agent to “perform” a concrete action outside the model.<\/p>\n\n\n\n Orchestration logic.<\/strong> The orchestrator, implemented with LangGraph, manages the workflow. It breaks down the user’s query, interrogates the appropriate tools (via the MCP API), and aggregates results to provide a complete response.<\/p>\n\n\n\n One of the major scientific challenges in AI Cloud Architecture lies in the inherent weakness of public pricing APIs, such as Infracost’s, which don’t natively support complex selection queries. A simple need like “find the cheapest service with at least 8GB of RAM” cannot be translated into a single API query.<\/p>\n\n\n\n Our solution overcomes this challenge by emulating operators like min, max, avg, order by, limit, etc., commonly found in database query languages like SQL, but missing for querying these GraphQL servers.<\/p>\n\n\n\n AQG4SD’s orchestrator is capable of:<\/p>\n\n\n\n Intelligently generating multiple GraphQL queries<\/strong> to obtain all necessary raw data. By “intelligently” we mean that the model is tasked with generating one or more parameterised GraphQL queries, along with value ranges for each parameter. This kills two birds with one stone: reducing the number of tokens to generate (and thus the solution’s cost) while simultaneously increasing the solution’s accuracy by relieving the LLM from generating all possible combinations (the Cartesian product) of parameters.<\/p>\n\n\n\n Delegating to an algorithmic module<\/strong> implemented within the MCP server the generation of all possible parameter combinations, the construction of final queries, and their execution.<\/p>\n\n\n\n Applying filters<\/strong> (for example, for services with a minimum RAM) and sorts (by price) in post-processing of obtained results.<\/p>\n\n\n\n Presenting a single, ordered, and filtered response<\/strong> to the user.<\/p>\n\n\n\n This multi-query and intelligent aggregation process is at the heart of our AI Cloud architecture, as it enables us to propose a reliable and optimised solution to our problem without having to modify the underlying API.<\/p>\n\n\n\nThe Multi-Query Orchestration Challenge<\/strong><\/h2>\n\n\n\n
Demo<\/strong><\/h2>\n\n\n\n