From Dev to Multi-Cloud AI Architect: Azure & AWS

Are you facing challenges with AI coding assistants that generate Lambda functions lacking observability, overlooking event source best practices, or producing Infrastructure as Code (IaC) that fails in production? The Agent Plugin for AWS Serverless offers a solution by embedding production-grade guidance directly into Claude Code, Cursor, and Kiro. It dynamically loads expertise for SAM/CDK patterns, EventBridge and Step Functions integrations, and Lambda durable functions with checkpoint-replay for stateful workflows. https://lnkd.in/gn8T-PMf This approach minimizes the blast radius from AI-generated misconfigurations and reduces total cost of ownership (TCO) on rework cycles. It is built on the open Agent Skills format for enhanced portability. What percentage of your AI-assisted serverless code requires significant refactoring before production? #AWS #Serverless #DevOps #SolutionsArchitecture #IaC

🚀 Deployment Success: Shipping a High-Performance AI Application on AWS ECS Fargate! I just wrapped up a challenging project: taking a Heavy-Weight AI Application from local development to a fully automated cloud infrastructure using the DevOps Golden Stack. Deploying AI models isn't just about code; it’s about managing massive container images (3GB+) and ensuring the inference engine stays healthy under load. ☁️🤖 🛠️ The Tech Stack: Infrastructure as Code: Terraform (18+ resources managed) Containerization: Docker (Handling 3GB+ AI models & dependencies) Orchestration: AWS ECS (Fargate) Networking: Application Load Balancer (ALB), VPC, Private/Public Subnets Monitoring: CloudWatch Logs for real-time inference tracking 💡 The "Real" Learning (Mistakes & Fixes): A project without hurdles isn't a project. Here’s how I tackled the challenges: Model Loading Timeouts: Because the AI image was 3GB+, the ALB health checks were failing during the container extraction and model warm-up phase. Lesson: Learned to implement health_check_grace_period to allow the AI engine sufficient time to initialize. Process Management: Debugged the "One Process" container limitation. Ensuring the Next.js frontend and the Python AI backend communicate effectively within the same task was a great lesson in Container Orchestration. Security Group Loops: Managed complex routing to ensure frontend requests reached the internal AI API ports without exposing sensitive model endpoints to the public internet. 🏁 Results: The infrastructure is live, stable, and fully managed via Terraform. This journey involved deep dives into AWS and competitive coding logic to ensure the architecture is as smart as the AI it hosts! Check out the code here: 🔗 GitHub: Hrishikesh - Watchdog Project #DevOps #AI #AWS #Terraform #CloudEngineering #Docker #MachineLearningOps #InfrastructureAsCode #NashikTech

🚀 Simplifying Generative AI Infrastructure with AWS CDK Constructs For anyone building AI applications on AWS, setting up infrastructure for things like RAG pipelines, chatbots, summarization, vector search, and model deployment can be complex and repetitive. That’s where these constructs help. AWS introduced Generative AI CDK Constructs, an open-source extension to the AWS Cloud Development Kit (CDK), providing well-architected patterns to deploy generative AI workloads quickly and consistently. Instead of manually wiring multiple services together, these constructs provide reusable patterns that integrate services like: Amazon Bedrock OpenSearch / Vector databases AWS Lambda Amazon Cognito AppSync / APIs This makes it much easier to deploy common GenAI patterns such as: ✔️ Retrieval Augmented Generation (RAG) ✔️ Question & Answer systems ✔️ Summarization workflows ✔️ Model inference pipelines What I found interesting is how Infrastructure as Code and Generative AI come together here. Using AWS CDK, developers can define complete GenAI architectures in code using familiar programming languages like TypeScript or Python. 📌 Key takeaway: These constructs significantly reduce the effort required to build production-ready GenAI solutions while following AWS best practices.

𝗧𝗵𝗶𝘀 𝗜𝘀 𝗛𝗼𝘄 𝗬𝗼𝘂 𝗗𝗲𝗽𝗹𝗼𝘆 𝗬𝗼𝘂𝗿 𝗙𝗶𝗿𝘀𝘁 𝗔𝗭𝘂𝗿𝗲 𝗔𝗜 𝗔𝗲𝗻𝘁 You want to deploy your first Azure AI agent with Terraform. Here's how you can do it. Azure AI agents are different from direct endpoints. An agent wraps a model deployment with an orchestration layer. This layer maintains conversation threads and decides when to call tools. To deploy your first Azure AI agent, you need to create an AI Services resource with project management enabled. Then you deploy a model and use the Azure AI Agent SDK to create agents. You can use Terraform to provision the infrastructure. The Python SDK creates and manages agents. When a new model launches, you can upgrade by changing one variable. Here are the key differences between direct endpoints and Azure AI agents: - Interaction: single request/response vs multi-turn conversation threads - State: stateless vs thread-based message history - Tool use: manual implementation vs function calling - Reasoning: whatever the model does vs run-based orchestration loop - Deployment: model deployment only vs agent + model deployment + thread management You can find the code for this project in the linked article. Source: https://lnkd.in/gb93HY6u Optional learning community: https://t.me/GyaanSetuAi

Accelerate AI-assisted development with Agent Plugin for AWS Serverless - AWS announces the Agent Plugin for AWS Serverless, enabling developers to easily build, deploy, troubleshoot, and manage serverless applications using AI coding assistants like Kiro, Claude Code, and Cursor. Agent plugins extend AI coding assistants with structured, reusable capabilities by packaging skills,… https://lnkd.in/e9_cybVm

🚀 Is Cursor Evolving into a Developer AI Cloud Platform? The conversation around AI coding tools is shifting. It’s no longer just about "which LLM is faster." It’s about which platform owns the Software Development Life Cycle (SDLC). In our latest MLcon deep dive, Juan Antonio Breña Moral explores how Cursor AI is transcending its IDE roots to become a comprehensive Developer AI Cloud. Why this matters for your engineering team: -Beyond Autocomplete: A look at how Plan Mode handles complex refactoring that standard agents miss. -The Background Agents API: How to delegate local tasks to the cloud and automate pipeline workflows. -Data-Driven Impact: We’ve mapped Cursor’s features directly to DORA metrics—from Deployment Frequency to Mean Time to Recover (MTTR). If you’re managing a team in 2026, you aren't just buying a code editor; you're architecting a new way to work. Read the full analysis here: 👉https://lnkd.in/dxY-bvmJ #MLcon #AIEngineering  #CursorAI

A few years ago, building a serverless app on AWS meant jumping between docs, templates, CLI commands, and StackOverflow threads. You’d write some code. Search the docs. Fix the IAM policy. Search again. Deploy. Debug. Repeat. It worked, but it was rarely smooth. Now something interesting is happening. AWS just introduced SAM Kiro Power, which brings deep knowledge of the AWS Serverless Application Model (SAM) directly into the Kiro AI development environment. Instead of an AI assistant that guesses, it now understands the full serverless workflow. Imagine asking: “Create a serverless API with Lambda, API Gateway, and DynamoDB.” And the assistant doesn’t just write a function. It: • generates the SAM template • structures the project • configures permissions • sets up local testing • prepares deployment All following AWS best practices. The real shift here isn’t just faster code generation. It’s AI assistants evolving from autocomplete tools into domain-aware engineering partners. Of course, tools like this don’t replace experience. They amplify it. You still need the judgment to guide the system, review the architecture, and make the right decisions. Less time fighting infrastructure. More time building. Serverless development might finally feel as simple as it was always supposed to be. Curious to see where this goes next. https://lnkd.in/ePsebqrm #AWS #Serverless #AI #DeveloperTools #CloudComputing

Agent plugins are the latest evolution of development assisting tools. Agent plugins extend AI coding assistants with structured, reusable capabilities by packaging skills, sub-agents, hooks, and Model Context Protocol (MCP) servers into a single modular unit. AWS has announced the Agent Plugin for AWS Serverless, enabling developers to easily build, deploy, troubleshoot, and manage serverless applications using AI coding assistants like Kiro, Claude Code, and Cursor.

AI Agents Don't Care About Your AWS Bill Back in late December, Andrej Karpathy sent a shockwave through the engineering world when he described the new era of AI programming as a "magnitude 9 earthquake." He famously called these new coding agents powerful "alien tools" that were handed out with "no manual." Fast forward to a few weeks ago, and Ed Donner dropped exactly that missing manual: his new AI Coder: Vibe Coder to Agentic Engineer course. I’m about a third of the way through the program right now, and watching Claude and multi-agent teams spin up complete products in sandboxes is genuinely mind-blowing. But living in the trenches of FinOps and cloud cost optimization, I’m seeing a massive collision course on the horizon: the "Vibe coding" trap vs. the enterprise cloud bill. Vibe coding is all about frictionless flow. You tell the agent what you want, and boom—the code and infrastructure exist. But here is the enterprise reality: Aliens don't care about your AWS bill. We spend weeks architecting Reserved Instances and Savings Plans just to scrape together a 10% compute savings. All of that hard work can be completely undone in a single afternoon if a "vibe-coded" sub-agent casually provisions heavy EC2 instances or writes a brute-force SQL join because it lacks the context of our enterprise workload limits. The next evolution isn't just going from a Vibe Coder to an Agentic Engineer. It's figuring out how to become a FinOps-aware Agentic Engineer. Speed is incredible, but not if it bankrupts the department. Ed Donner — loving the rollercoaster so far! Curious, as we get deeper into multi-agent workflows running in sandboxes: is there a way to give these agents a strict "budget persona"? How do we teach our AI to check the AWS pricing calculator before it commits?

Supercharging Kubernetes with AI: Amazon EKS MCP Server What if your AI coding assistant could not only write code-but also create, deploy, manage, and troubleshoot your Kubernetes infrastructure in real time? That’s exactly what the Amazon EKS MCP Server brings to the table. By integrating the EKS MCP Server with AI assistants, developers can now interact with Amazon Web Services (AWS) and Kubernetes clusters using natural language, making complex operations simpler and faster. What makes it powerful? AI-driven cluster creation with automated VPC, networking, and node setup Seamless deployment of containerized apps (auto-generate or apply YAMLs) Full lifecycle management of Kubernetes resources (create, update, delete) Real-time visibility into cluster state and infrastructure Built-in troubleshooting (logs, events, insights, metrics) Secure integration with IAM, CloudFormation, and CloudWatch Why this matters This is a big step towards AI-assisted DevOps, where LLMs evolve from passive assistants to active operators in your cloud environment. From cluster setup → deployment → monitoring → debugging Everything becomes faster, smarter, and more intuitive. I’ve put together a step-by-step hands-on guide covering: Prerequisites & IAM setup MCP Server configuration (Kiro, Cursor, VS Code) Key tools & real use cases Security considerations & best practices Full guide Medium link here : https://lnkd.in/eMjGDiXP Curious to hear your thoughts: Would you trust AI to manage your Kubernetes clusters in production? #AWS #EKS #Kubernetes #DevOps #AI #PlatformEngineering #CloudComputing #LLM #Automation #SolutionsArchitecture