Need guidelines on building architecture diagram for upcoming project

AI Discussions
1

Introduction

Hello everyone,

My name is Mahendra, and I am a Software Engineer based in India. I work primarily on full-stack applications, data platforms, cloud solutions, and AI-powered systems.

Recently, I have been focusing on AI Engineering, Agentic AI systems, Generative AI architectures, RAG applications, multi-agent frameworks, and cloud-native AI deployments on Microsoft Azure. I am particularly interested in learning more about LLM orchestration, evaluation frameworks, AI security, governance, observability, and production-grade AI system design.

I am currently working on designing enterprise-grade Generative AI and Agentic AI solutions and would appreciate guidance from the community on:

  • Designing scalable Generative AI architectures

  • Creating architecture diagrams for AI applications

  • Choosing between frameworks such as LangGraph, Microsoft Agent Framework, Claude SDK, and Codex SDK

  • Implementing RAG, memory, evaluation, and observability layers

  • AI security, governance, compliance, and responsible AI practices

  • Multi-model strategies using Azure AI Foundry-hosted models

I joined this community to learn from experienced practitioners, understand real-world architecture patterns, and connect with others building production AI systems.

If anyone has recommendations, reference architectures, best practices, or resources for designing Generative AI architecture diagrams, I would love to learn from your experience.

Looking forward to learning and contributing to the community.

Thank you!

2

Hi,

There is a lot of guidance and breadth of topics you are asking about, and it’s important to approach this step by step rather than trying to absorb everything at once.

On this learning platform, there are already several structured courses that cover many of the areas you mentioned—such as Generative AI systems, RAG, agentic workflows, evaluation, and deployment patterns.

I would recommend visiting DeepLearning.AI courses and exploring the catalog there. You can search based on your specific interests, whether that’s LLM orchestration, RAG systems, or production AI architectures.

By following a structured learning path and building concepts incrementally, you’ll be in a much better position to develop the practical skills you’re aiming for.

Hope this helps, and best of luck with your learning journey.