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
Marouan AZROUR’s Post
More Relevant Posts
-
🚀 AWS SAM Kiro Power: Accelerate Your Serverless Development with AI Exciting news for serverless developers! AWS just launched the AWS Serverless Application Model (SAM) Kiro Power, bringing AI-assisted serverless development directly to your local environment. 🎯 What's New: The SAM Kiro Power integrates serverless application development expertise into Kiro's agentic AI development environment, enabling you to build, deploy, and manage serverless applications with intelligent AI assistance. ⚡ Key Capabilities: - One-click installation from Kiro IDE and Kiro Powers page - Initialize SAM projects with AI guidance - Build and deploy applications to AWS seamlessly - Locally test Lambda functions before deployment - Support for event-driven patterns with Amazon EventBridge, MSK, Kinesis, DynamoDB Streams, and SQS 🔒 Built-in Best Practices: - Enforces SAM resources usage from the start - Integrates AWS Lambda Powertools for observability and structured logging - Includes security best practices for IAM policies 💡 Perfect For: Whether you're building static websites with API backends, event-driven microservices, or full-stack applications, SAM Kiro Power accelerates your journey from concept to production with AI-powered guidance. 🚀 Get Started: Available now with one-click installation! Explore the power on GitHub or check the SAM developer guide to learn more. Ready to supercharge your serverless development workflow? What serverless patterns are you most excited to build with AI assistance? 🔗 Read more: https://lnkd.in/dwhuGHyZ #AWS #Serverless #SAM #Lambda #DevOps #AI #Kiro #CloudDevelopment #EventDriven #Microservices #CloudNative #AWSLambda
-
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
-
𝗧𝗛𝗘 𝗙𝗨𝗧𝗨𝗥𝗘 𝗢𝗙 𝗖𝗟𝗢𝗨𝗗 𝗜𝗦 𝗔𝗟𝗥𝗘𝗔𝗗𝗬 𝗢𝗡 𝗬𝗢𝗨𝗥 𝗟𝗔𝗣𝗧𝗢𝗣. Imagine testing 147 AWS services without a credit card or internet. That’s Robotocore – an MIT‑licensed AWS twin that runs locally, zero telemetry, free forever. It listens on localhost:4566, parses the 12‑digit key, and gives you real‑world behavior for S3, Lambda, DynamoDB, and 144+ more. Spin it up in ONE Docker command, then plug it into your CI pipelines – no flaky network, no cloud bills. Perfect for AI agents, dev‑ops testing, or teaching students a safe sandbox. Multi‑account, multi‑region isolation means each team gets its own in‑memory state – just like real AWS. Over 5,500 unit tests and 11,000 compatibility checks guarantee reliability. What would you build first with a local AWS twin? Drop your ideas below. Read the full article to get the step‑by‑step guide. #CloudDevelopment #OpenSource #Robotocore #DevOps #AI
-
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
-
🚀 LLM Integration Architecture for Microservices with Cloud Deployment Sharing a practical architecture for integrating LLMs into microservices, along with live deployed APIs on Google Cloud that you can try. I built and deployed two GenAI-powered backend services: 🔹 Live Projects (Deployed on GCP) 1. Product Review Sentiment Analyzer 2. Incident Classification Engine These are not just demos — both are running as serverless APIs on Cloud Run and can be accessed via the links below. 🧠 Architecture Overview The attached diagram shows a complete end-to-end GenAI system: Client → FastAPI Service → Gemini LLM → Cloud Run → Secure Access ⚙️ API & LLM Design • FastAPI-based REST APIs • Input/Output validation using Pydantic • Rate limiting to control usage • Prompt engineering: Single-shot Multi-shot • Structured JSON responses • Token usage included (input / output / total) ☁️ Cloud Deployment (Live) Both services are deployed on Google Cloud using a serverless architecture: • Dockerized applications • Deployed via Cloud Run • Artifact Registry for image storage • Auto-scaling APIs exposed via public endpoints 👉 You can directly try these APIs using the links in 'Try It Yourself' section below 🔐 Security • API keys stored in Secret Manager • IAM service accounts for controlled access • No secrets in code or containers 🔗 Try It Yourself 📂 GitHub Repository: 👉 https://lnkd.in/diZn9faU 🌐 Live API Endpoints Swagger Doc with Try option (Cloud Run): 👉 Product Review Sentiment Analyser : https://lnkd.in/dnMyp83E 👉 Incident Classification Engine: https://lnkd.in/dEGbcQqz 💡 Key Takeaways GenAI becomes powerful when combined with: • Strong backend architecture • Controlled LLM usage • Secure cloud deployment • Production-ready API design 📌 What’s Next • Observability for LLM system • Cost optimization strategies #GenAI #LLM #FastAPI #Microservices #CloudRun #GoogleCloud #AIEngineering #SystemDesign #Python
-
-
Seeing developers shift from writing boilerplate code to architecting production-ready serverless apps using AI has been been rewarding. Excited to see how the community uses AI-guided serverless development to build more secure, observable applications faster. Check Shridhar Pandey's post out for some of our recent work on this front with more to follow in the year for our serverless portfolio of products.
Principal PM @ AWS | Leading end-to-end serverless & GenAI developer experience and platform operator products at scale
🚀 Serverless development just became agent-native. The team has been at work on this for a while, and I'm thrilled to see it come together. The AWS Serverless Agent Plugin is now in the official Anthropic plugins marketplace. And it's open source. Install it in Claude Code, Kiro, or Cursor. Your AI agent can then analyze your codebase, recommend services, generate IaC, estimate costs, run security scans, and deploy (Anthropic Marketplace: https://lnkd.in/g39pPREm | GitHub: https://lnkd.in/gGNXhkf5) /plugin install aws-serverless@claude-plugins-official But that's just one piece. Over the past two weeks, we've shipped 2 more capabilities that mark this shift. AI agents aren't just writing Lambda functions, they're architecting, securing, and deploying serverless applications with deep built-in expertise. * AWS SAM Kiro power: agents can initialize SAM projects, wire up event-driven architectures (EventBridge, SQS, Kinesis, DynamoDB Streams), enforce least-privilege IAM, and instrument Powertools for observability; from the start, not bolted on after (https://lnkd.in/geCCDMUE). * Lambda Durable Functions Kiro Power: agents now guide developers through building checkpointed executions, suspend-and-resume callbacks with zero compute cost, and workflows that run up to a year. One of the most powerful patterns in serverless, now accessible through AI-guided development (https://lnkd.in/gw94sSrn). The common thread: best practices by default. Security, observability, resilience built into the AI-guided workflow from day one, not afterthoughts. Whether you're in Kiro, Claude Code, or Cursor, your AI agent now has real serverless expertise. The result: developers spend less time on boilerplate security, observability, and IaC; and more time on what makes their application unique. We're moving from "AI writes code" to "AI architects cloud-native applications." This is just the beginning. Try them out — I'd love to hear what you think. #AWS #Serverless #AWSLambda #AIAgents #DeveloperExperience #CloudNative #Kiro #GenerativeAI #BuildOnAWS
-
-
Super excited about how our team continues to lean in and support developers. Check out the official AWS Serverless Agent Plugin on the Anthropic marketplace. It'll help your coding agent quickly come up to speed with serverless best practices!
Principal PM @ AWS | Leading end-to-end serverless & GenAI developer experience and platform operator products at scale
🚀 Serverless development just became agent-native. The team has been at work on this for a while, and I'm thrilled to see it come together. The AWS Serverless Agent Plugin is now in the official Anthropic plugins marketplace. And it's open source. Install it in Claude Code, Kiro, or Cursor. Your AI agent can then analyze your codebase, recommend services, generate IaC, estimate costs, run security scans, and deploy (Anthropic Marketplace: https://lnkd.in/g39pPREm | GitHub: https://lnkd.in/gGNXhkf5) /plugin install aws-serverless@claude-plugins-official But that's just one piece. Over the past two weeks, we've shipped 2 more capabilities that mark this shift. AI agents aren't just writing Lambda functions, they're architecting, securing, and deploying serverless applications with deep built-in expertise. * AWS SAM Kiro power: agents can initialize SAM projects, wire up event-driven architectures (EventBridge, SQS, Kinesis, DynamoDB Streams), enforce least-privilege IAM, and instrument Powertools for observability; from the start, not bolted on after (https://lnkd.in/geCCDMUE). * Lambda Durable Functions Kiro Power: agents now guide developers through building checkpointed executions, suspend-and-resume callbacks with zero compute cost, and workflows that run up to a year. One of the most powerful patterns in serverless, now accessible through AI-guided development (https://lnkd.in/gw94sSrn). The common thread: best practices by default. Security, observability, resilience built into the AI-guided workflow from day one, not afterthoughts. Whether you're in Kiro, Claude Code, or Cursor, your AI agent now has real serverless expertise. The result: developers spend less time on boilerplate security, observability, and IaC; and more time on what makes their application unique. We're moving from "AI writes code" to "AI architects cloud-native applications." This is just the beginning. Try them out — I'd love to hear what you think. #AWS #Serverless #AWSLambda #AIAgents #DeveloperExperience #CloudNative #Kiro #GenerativeAI #BuildOnAWS
-
-
If you Claude Code, Cursor or Kiro and you build on AWS, install this skill! How to use: The plugin's skills activate automatically based on context. Try prompts like "Create a new Lambda function triggered by an SQS queue", "Deploy my serverless app using SAM", "Set up an EventBridge rule to route events to my Lambda", "Build a durable function workflow with retry and checkpoint logic", or "Help me debug cold starts in my Lambda function".
Principal PM @ AWS | Leading end-to-end serverless & GenAI developer experience and platform operator products at scale
🚀 Serverless development just became agent-native. The team has been at work on this for a while, and I'm thrilled to see it come together. The AWS Serverless Agent Plugin is now in the official Anthropic plugins marketplace. And it's open source. Install it in Claude Code, Kiro, or Cursor. Your AI agent can then analyze your codebase, recommend services, generate IaC, estimate costs, run security scans, and deploy (Anthropic Marketplace: https://lnkd.in/g39pPREm | GitHub: https://lnkd.in/gGNXhkf5) /plugin install aws-serverless@claude-plugins-official But that's just one piece. Over the past two weeks, we've shipped 2 more capabilities that mark this shift. AI agents aren't just writing Lambda functions, they're architecting, securing, and deploying serverless applications with deep built-in expertise. * AWS SAM Kiro power: agents can initialize SAM projects, wire up event-driven architectures (EventBridge, SQS, Kinesis, DynamoDB Streams), enforce least-privilege IAM, and instrument Powertools for observability; from the start, not bolted on after (https://lnkd.in/geCCDMUE). * Lambda Durable Functions Kiro Power: agents now guide developers through building checkpointed executions, suspend-and-resume callbacks with zero compute cost, and workflows that run up to a year. One of the most powerful patterns in serverless, now accessible through AI-guided development (https://lnkd.in/gw94sSrn). The common thread: best practices by default. Security, observability, resilience built into the AI-guided workflow from day one, not afterthoughts. Whether you're in Kiro, Claude Code, or Cursor, your AI agent now has real serverless expertise. The result: developers spend less time on boilerplate security, observability, and IaC; and more time on what makes their application unique. We're moving from "AI writes code" to "AI architects cloud-native applications." This is just the beginning. Try them out — I'd love to hear what you think. #AWS #Serverless #AWSLambda #AIAgents #DeveloperExperience #CloudNative #Kiro #GenerativeAI #BuildOnAWS
-
-
IaC isn't just about scripting; it’s about codifying smart architectural decisions. 🏗️☁️ I’ve just published my latest project: an automated serverless image tagging pipeline built entirely with Terraform. 🛠️✨ In this build, I didn't just pick services at random. I analyzed the trade-offs to ensure the architecture was lean, fast, and cost-effective. Here is the logic behind the stack: ⚫ The Trigger: I used a direct S3 Event Notification instead of routing through SNS. ⚡ Decision: Since I only had one consumer (Lambda), skipping the SNS topic reduced architectural complexity and removed an unnecessary latency hop. ⚫ The Compute: I utilized AWS Lambda triggered immediately by the upload. 🚀 Decision: While I could have used a scheduled EventBridge (CloudWatch) trigger to poll the bucket, real-time event-driven execution is far more efficient and avoids paying for "empty" checks when no images are present. ⚫ The AI: I integrated Amazon Bedrock (Claude 3 Haiku). 🧠 Alternative: I considered using AWS Rekognition or even an external model like Google Gemini (Vertex AI), but Haiku provided the best balance of rich, generative image descriptions and lightning-fast performance within the AWS ecosystem. Building this was a great reminder that great cloud engineering is about evaluating alternatives based on speed, effectiveness, and budget. 📉✅ If you missed my previous post, I’m dropping the full Loom walkthrough in the comments below! 👇 🎥 What are your go-to "lean" architecture patterns for serverless? Let’s swap ideas in the comments! 💬👇 #AWS #Terraform #IaC #CloudArchitecture #Serverless #CloudAutomation #GenerativeAI #AWSBedrock #DevOps #EngineeringMindset
-
The Azure Skills Plugin is a new extension for specialized AI agents (like Semantic Kernel and ChatGPT) that allows them to interact directly with Azure services using natural language. Key Features: - Natural Language Cloud Management: Enables developers and ops teams to query resources, check statuses, and manage environments through simple conversational commands instead of complex CLI syntax. - Seamless Service Integration: Connects out-of-the-box with core Azure services, allowing agents to perform tasks like listing web apps, checking resource groups, or monitoring deployments. - Extensible Architecture: Built on the OpenAI plugin standard, making it easy to incorporate into existing AI workflows and custom "Copilot" experiences. - Rapid Prototyping: Ideal for developers who need to quickly spin up or inspect cloud infrastructure during short-cycle Proof of Concepts without deep-diving into the Azure Portal. - Enhanced Productivity: Reduces context-switching by bringing cloud orchestration directly into the development chat interface, bridging the gap between intent and execution. Check it out: https://lnkd.in/e2Hb8z8u
