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:
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Designing scalable Generative AI architectures
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Creating architecture diagrams for AI applications
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Choosing between frameworks such as LangGraph, Microsoft Agent Framework, Claude SDK, and Codex SDK
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Implementing RAG, memory, evaluation, and observability layers
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AI security, governance, compliance, and responsible AI practices
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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!
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.
@Mahendra7 are those the knowledge stack u need to build a production-grade AI SaaS product?
AI Engineering, Agentic AI systems, Generative AI architectures, RAG applications, Multi-agent frameworks, Cloud-native AI deployments, LLM orchestration, Evaluation frameworks, AI security, governance, Observability, Production-grade AI system design, Designing scalable Generative AI architectures, Creating architecture diagrams for AI applications, 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’m building my own AI SaaS business startup and kinda want to know the knowledge stack i need to build an overall good AI system
would also appreciate your contribution on this @gent.spah
There are many courses on the DeepLearning.AI platform, and quite a few of them are very specific to certain applications or tools, and I am not familiar with all of them.
What I would suggest is to start by searching for the specific software, framework, or topic you are interested in directly on the platform. That usually helps you quickly find the most relevant courses for your goal.
Also, in this community forum, if you search for the keyword “roadmap”, you’ll find several discussions where learners and mentors have shared suggested learning paths for different AI roles and skill levels. Those can be very helpful in giving you a structured direction.
I personally haven’t gone through all the application-specific courses myself, since there are quite a lot and they are quite specialized. But the roadmap discussions and search feature should help you figure out a good path based on what you want to learn.
okay thanks, let me check them out
Here are some standard workflows provided in the Langgraph docs which helped me a lot when I was learning Agentic AI: Workflows and agents - Docs by LangChain
Hope this helps. 
Thanks a lot @Jasmeet_Singh2
let me check it out
@Adrian_Langat We have existing product on node, angular and postgresql. Working on agentic capabilities on existing product with more features for help client to optimized there manual effort. Correctly on POC stage and for POCs i am using codesdk and codexsdk along with microsoft services for security. Clients are mostly on microsoft so need to use microsoft first approch.
Please check codexsdk and claudesdk, now microsoft also launch azure ai project sdk for most of feature. My most imp feature is client will write skills and upload to system and using those skill need to generate reports, so both sdk support this feature.
whoaaa, okay
that’s quite the technical jargon
so you already started on designing your enterprise-grade agentic AI? I’ll def check the codexsdk and claudesdk
So far no one has commented on one salient word from your thread title: diagram.
I think it has quite gone out of fashion, but when I was a young professional there was a lot of attention paid to system design and architecture, and specifically how to express design in a way that reduced project risk. This lead to the creation of the Unified Modeling Language (UML). UML® - Unified Modeling Language | Object Management Group
In contrast to just a drawing or picture, a design expressed in UML has semantics, which can be checked and enforced automatically. Everything is possible when your design is clouds with lightning bolts between them, but a UML model of your system increases the likelihood that what you draw can actually be built. It forces you to think critically about each component, their public interfaces, and the infrastructure required to deploy them.
Maybe passé in this age where so many systems really are built as clouds and lightning bolts and AWS or Azure handles all the messy stuff magikally behind a curtain, but based on my experience, a (set of) UML diagram(s) is a valuable asset for any tech project or company. Grady Booch ( Grady Booch - Wikipedia ) used to say ‘…every system has an architecture- the best ones are intentional.’ UML is a way to express intentional system architecture. If you don’t use it, make sure that choice is also intentional. Cheers.
@Adrian_Langat fyi