# Kiro Published articles for Kiro. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Convert cloud-native code from Azure to AWS with AWS Transform DevFeed: [Convert cloud-native code from Azure to AWS with AWS Transform]() Original publisher: [Read original article]() Author: David Topchyan Published: 2026-10-29T21:20:52Z Content type: tutorial Language: en Sources: [.NET on AWS Blog]() Topics: [AWS Transform](), [Amazon Web Services](), [Azure](), [.NET](), [Serverless](), [azure functions]() Tags: [amazon-bedrock](), [amazon-dynamodb](), [amazon-s3](), [amazon-sqs](), [aws](), [aws-eventbridge](), [aws-iam-identity-center](), [aws-lambda](), [aws-secrets-manager](), [aws-transform](), [azure](), [azure-cosmos-db](), [azure-functions](), [azure-sdk](), [cloud](), [cloud-native](), [code](), [kiro](), [migration](), [net](), [sdk](), [uncategorized]() ### AI overview AWS Transform for .NET can identify Azure SDK code in .NET applications and convert supported service paths to AWS equivalents. The walkthrough transforms an Azure Queue-triggered Function that writes to Cosmos DB into an SQS-triggered Lambda writing to DynamoDB; the generated code uses a conditional write to preserve insert-only behavior and reports failed SQS records for retry. The article says supported conversions are well tested, with Cosmos DB to DynamoDB in preview. Other Azure services can be requested, but those conversions are best effort and require careful review. A second example customizes the plan to move Cosmos DB to S3 for storage consolidation and requests additional migrations, then uses Kiro to complete, deploy, and test the application. The IDE walkthrough requires Windows, Visual Studio 2026, the AWS Toolkit, AWS Transform setup with IAM Identity Center credentials, and an Azure .NET application; other AWS Transform interfaces are also available. ### Source excerpt Introduction When you bring a cloud-native application over to AWS from another cloud, it may contain code that uses another cloud's SDK. AWS Transform for .NET can convert code from Azure cloud-native applications to use equivalent services from AWS, helping accelerate the migration. In this post, we'll take you on a tour of converting Azure [...] ## Build a repeatable PHP-to-Java migration engine with Kiro DevFeed: [Build a repeatable PHP-to-Java migration engine with Kiro]() Original publisher: [Read original article]() Author: Sarat Guttikonda Published: 2026-10-29T18:14:17Z Content type: tutorial Language: en Sources: [Artificial Intelligence]() Topics: [data migrations](), [Symfony](), [Maven](), [jpa](), [AI Chat]() Tags: [advanced-300](), [application](), [aws-transform](), [build](), [discovery](), [engine](), [first](), [harness](), [java](), [java-migration](), [kiro](), [migration](), [one](), [original](), [php](), [port](), [post](), [process](), [scanner](), [specification](), [technical-how-to](), [this](), [with]() ### AI overview The migration engine makes PHP behavior the specification and delays declaring the Java port complete until a parity harness verifies it. A dependency-free PHP scanner inventories Eloquent models, Doctrine attributes, and ORM XML mappings without executing application code; ambiguous cases are flagged for human review. The eight-phase workflow builds and validates the harness first, develops and stress-tests a rulebook, then scaffolds Spring Boot, translates the model and I/O layers, converts business logic, and checks parity. Repeated errors are addressed by updating the rulebook and regenerating. Kiro guides one application at a time with approval gates; AWS Transform can run the same skill files across many applications in parallel, with shared methodology improvements benefiting both. The sample ports four of 37 models, and its parity harness was never built, which the text identifies as its biggest gap. ### Source excerpt Turn a PHP-to-Java migration from a one-time leap of faith into a measurable, repeatable process with Kiro. This post walks through a reusable engine: a mechanical discovery scanner and an eight-phase, parity-first workflow that treats the original PHP application as the specification and verifies the Java port with a parity harness. ## Publishing LMS courses with a Kiro SCORM pipeline DevFeed: [Publishing LMS courses with a Kiro SCORM pipeline]() Original publisher: [Read original article]() Author: Omar Gonzalez Published: 2026-10-27T17:23:23Z Content type: tutorial Language: en Sources: [AWS Architecture Blog]() Topics: [Kiro](), [Agentic AI](), [Learning](), [CI/CD](), [Publishing]() Tags: [2](), [advanced-300](), [agentic](), [agentic-ai](), [ai](), [automate](), [courses](), [how-to](), [kiro](), [learning](), [learning-management-system](), [management](), [material](), [packages](), [pipeline](), [publishing](), [raw](), [system](), [technical-how-to](), [training](), [upload](), [with]() ### AI overview The article describes a local Kiro pipeline that turns training materials into SCORM 1.2 packages for LMS upload. In conversation, Kiro plans a course manifest, writes one lesson per module, lints lessons, builds an HTML course and ZIP, then validates the package. Steering files preserve SCORM knowledge, authoring conventions, and repository rules; hooks automate linting on save, package validation, and proposed steering updates. Authors can set page dwell time, expected durations, and the passing score. The walkthrough requires Kiro, Node.js 20+, Python 3.11+, and `zip`; it recommends serving the build locally for visual review and testing the ZIP in the target LMS because systems vary. A small sample reportedly packaged in minutes. The pipeline runs as local scripts and a Kiro session, with no cloud resources to remove. ### Source excerpt See how to use Kiro, an agentic AI, to automate converting raw training material into compliant SCORM packages that are ready to upload to a learning management system (LMS). ## Tracing failures across Terraform state boundaries with AWS DevOps Agent DevFeed: [Tracing failures across Terraform state boundaries with AWS DevOps Agent]() Original publisher: [Read original article]() Author: Shruti Saxena Published: 2026-10-26T17:21:23Z Content type: article Language: en Sources: [AWS DevOps & Developer Productivity Blog]() Topics: [Terraform](), [Monitoring](), [DevOps]() Tags: [advanced-300](), [agent](), [application](), [aws](), [cause](), [change](), [devops](), [devops-agent](), [generate](), [kiro](), [post](), [root](), [shows](), [stack](), [state](), [team](), [technical-how-to](), [terraform](), [this](), [traces](), [tracing](), [with]() ### AI overview The demo shows how AWS DevOps Agent can trace an application outage across independent Terraform states: a tightened security group egress rule blocks Lambda's TCP/443 path through a DynamoDB Gateway VPC endpoint, causing timeouts and misleading alarms. The agent correlates the alarms, resource dependencies, logs, metrics, and CloudTrail change; it reasons about AWS resources rather than identifying Terraform stack ownership. VPC Flow Logs provide additional evidence, and without them the network diagnosis has less support. The agent produces a mitigation specification; Kiro uses it to generate a Terraform fix for engineer review, planning, and application. The article notes that CloudTrail attributes Terraform changes to the IAM role that ran apply, not necessarily the individual engineer. Its sample intentionally injects failure and exposes an unauthenticated API, so it is not production-ready. ### Source excerpt When a change in one Terraform stack silently breaks an application in another, the team seeing the symptoms often doesn't own the stack that caused it. This post shows how AWS DevOps Agent traces the root cause across Terraform state boundaries, then pairs with Kiro to generate the fix. ## Work management context for AI agents with Smartsheet MCP on AWS DevFeed: [Work management context for AI agents with Smartsheet MCP on AWS]() Original publisher: [Read original article]() Author: Galina Jordanowa Published: 2026-10-20T17:59:07Z Content type: tutorial Language: en Sources: [Artificial Intelligence]() Topics: [Kiro](), [AI Integration Strategies](), [AI Chat]() Tags: [advanced-300](), [agents](), [ai](), [ai-agents](), [amazon](), [amazon-quick](), [amazon-quick-suite](), [aws](), [business](), [context](), [context-for-ai-agents](), [data](), [developers](), [for-business](), [for-developers](), [kiro](), [management](), [mcp](), [mcp-server](), [model-context-protocol](), [technical-how-to](), [work](), [work-management]() ### AI overview Smartsheet's generally available MCP Server lets AI clients read and manage live Smartsheet data through more than 40 tools, including sheets, rows, columns, and automations. The article outlines two AWS connection paths: Amazon Quick, a managed, organization-wide option with a prebuilt OAuth connector for business teams, and Kiro, a developer-focused IDE setup using a JSON configuration and personal access token (OAuth is recommended for production). Both expose the same toolset. Examples include querying blocked sprint tasks in Quick and having Kiro update a task row after a fix is committed. Access uses Smartsheet's existing user permissions; administrators can control AI features and write access by group. The walkthrough requires an eligible Smartsheet plan and appropriate AWS permissions. ### Source excerpt The Smartsheet Model Context Protocol (MCP) Server lets AI agents read, update, and act on live work management data. This post shows two ways to connect it on AWS: Amazon Quick for business teams and Kiro for developers, so your agents can work with Smartsheet without switching tools. ## Analyzing Aurora MySQL issues with Kiro IDE and Aurora MySQL MCP server DevFeed: [Analyzing Aurora MySQL issues with Kiro IDE and Aurora MySQL MCP server]() Original publisher: [Read original article]() Author: Lasita Bhattacharya Published: 2026-10-19T17:59:27Z Content type: tutorial Language: en Sources: [AWS Database Blog]() Topics: [Amazon Aurora](), [MSP MCP](), [Figma AI](), [Monitoring](), [Grafana]() Tags: [advanced-300](), [ai](), [amazon](), [amazon-aurora](), [amazon-cloudwatch](), [amazon-rds](), [aws-cli](), [aws-command-line-interface-aws-cli](), [aws-identity-and-access-management-iam](), [aws-kms](), [aws-secrets-manager](), [deadlocks](), [diagnose](), [ide](), [kiro](), [mcp](), [mcp-server](), [mysql](), [mysql-compatible](), [root-cause-analysis](), [technical-how-to]() ### AI overview This tutorial shows how to configure Kiro IDE with the Aurora MySQL MCP server to investigate MySQL deadlocks and high IOPS events using natural-language prompts. It walks through setup, access controls, diagnostic workflows, and recommendations, while emphasizing independent verification and testing on a clone or restored snapshot before applying changes. ### Source excerpt Learn how to use Kiro IDE with the Amazon Aurora MySQL MCP server to diagnose deadlocks and high IOPS events through natural language prompts, turning hours of manual, expert-dependent investigation into minutes of AI-guided root cause analysis. ## HERE Technologies and AWS: Accelerating NDS.Live with agentic AI DevFeed: [HERE Technologies and AWS: Accelerating NDS.Live with agentic AI]() Original publisher: [Read original article]() Author: Aleksandar Matic Published: 2026-10-19T16:00:46Z Content type: article Language: en Sources: [AWS DevOps & Developer Productivity Blog]() Topics: [Kiro](), [agentic-coding](), [Spec Driven Development](), [CI/CD](), [Agent Skills](), [Code Modularity]() Tags: [advanced-300](), [agent-memory](), [agent-skills](), [agentic-ai](), [apis](), [audit](), [code](), [customer-solutions](), [kiro](), [multi-agent](), [traceability](), [workflow]() ### AI overview HERE Technologies describes using Kiro to orchestrate specialized AI agents that turn NDS.Live product requirements into linked Jira work items. The workflow uses on-demand domain knowledge, source-code indexing, persistent memory, and review gates to support consistent ticket creation and ASPICE traceability. The company reports that internal tasks that previously took hours or days can be completed in minutes, and expects the investigation phase to decrease by more than 50 percent. ### Source excerpt HERE Technologies is accelerating NDS.Live map data development with an orchestrated, multi-agent approach built on Kiro. Kiro powers, the Kiro CLI, a three-layer memory system, and Code Intelligence turned a fragile, manual workflow into a consistent, largely automated process with ASPICE traceability captured as work is created. ## How Dynata scaled AI across the development lifecycle with AI-DLC and Kiro IDE DevFeed: [How Dynata scaled AI across the development lifecycle with AI-DLC and Kiro IDE]() Original publisher: [Read original article]() Author: Madhu Chebrolu Published: 2026-10-15T15:50:31Z Content type: article Language: en Sources: [AWS Architecture Blog]() Topics: [AI-enhanced product development](), [AI-assisted coding](), [Spec Driven Development](), [Product Management](), [Developer experience](), [engineering-culture]() Tags: [ai](), [ai-assisted](), [ai-assisted-coding](), [ai-assisted-development](), [ai-powered-coding](), [amazon-bedrock](), [architecture](), [automate](), [bugs](), [build](), [building](), [business](), [capabilities](), [change](), [coding](), [customer-solutions](), [development](), [intermediate-200](), [kiro](), [kiro-ide](), [methodology](), [team]() ### AI overview Dynata adopted AI-DLC with Kiro IDE to bring AI into requirements, design, planning, and implementation. In its account, the staged workflow reduced development time for some full-stack features from three to four sprints to about one sprint, while surfacing requirements gaps and design questions earlier. The article also describes preparation practices such as refining steering files, briefing participants, and gathering project context before workshops. ### Source excerpt Dynata reimagined its development lifecycle with AI, moving from AI-assisted coding to the AI-Driven Development Lifecycle (AI-DLC) methodology with Kiro IDE. Learn how the company reduced the development phase of full-stack features from three to four sprints to roughly one, with every team aligned from the start. ## Groundcover lets Claude and Codex hand production investigations to its own agent DevFeed: [Groundcover lets Claude and Codex hand production investigations to its own agent]() Original publisher: [Read original article]() Author: Duncan Riley Published: 2026-10-14T13:00:47Z Content type: news Language: en Sources: [SiliconANGLE]() Topics: [agent observability](), [supervisor agent pattern](), [observability](), [API]() Tags: [agent-mode](), [agent-observability](), [agent-tasks](), [agent-to-agent-observability](), [agents](), [ai-agents](), [amazon-web-services](), [anthropic](), [apps](), [architecture](), [artificial-intelligence](), [bring-your-own-cloud](), [claude](), [codex](), [coding-agents](), [cursor](), [github](), [groundcover](), [kiro](), [kubernetes](), [linear](), [mcp](), [model-context-protocol](), [news](), [observability](), [one-peak](), [openai](), [root-cause-analysis](), [shahar-azulay](), [slack](), [telemetry](), [the-latest](), [wand-cloud]() ### AI overview Groundcover has launched an observability agent that lets coding agents such as Claude, Codex, Cursor and Kiro delegate production investigations. Agents can retrieve telemetry through MCP tools or submit asynchronous tasks that return root-cause analyses or draft dashboards. Engineers can also direct investigations through Agent Mode or Slack. The service runs in customers' cloud accounts, and the capabilities are available now. ### Source excerpt Application observability startup groundcover Inc. today gave outside artificial intelligence coding agents such as Anthropic PBC's Claude and OpenAI Group PBC's Codex a way to hand production investigations to its own observability agent. The launch, which groundcover is calling agent-to-agent observability, is seeking to address what happens after code ships. Coding agents can already review [...] The post Groundcover lets Claude and Codex hand production investigations to its own agent appeared first on SiliconANGLE. ## When to use what: A guide to AWS Transform interfaces DevFeed: [When to use what: A guide to AWS Transform interfaces]() Original publisher: [Read original article]() Author: Everett Dolgner Published: 2026-10-09T13:30:29Z Content type: tutorial Language: en Sources: [Migration & Modernization]() Topics: [AWS Transform](), [.NET](), [CI/CD]() Tags: [aws-transform](), [ci-cd](), [containers](), [foundational-100](), [guide](), [interfaces](), [java](), [kiro](), [mainframe-migration](), [migrate](), [migration](), [net](), [self-serve-migration](), [thought-leadership]() ### AI overview The article explains how teams can use AWS Transform through its Console, IDE integrations, CLI, and MCP, matching each interface to activities such as exploring migration work, executing transformations, scripting batch jobs, or composing AI workflows. It describes shared workspaces and state that support handoffs across those interfaces. ### Source excerpt You just got assigned 40 applications to migrate. Three teams, two time zones, a mix of .NET services and legacy monoliths, and leadership wants a status update every Friday. Where do you start? With AWS Transform, the answer depends on what you're doing right now, not which interface is "correct." There isn't one. There are [...] ## Provisioning AWS Interconnect - multicloud with the Kiro agentic IDE DevFeed: [Provisioning AWS Interconnect - multicloud with the Kiro agentic IDE]() Original publisher: [Read original article]() Author: Tushar Jagdale Published: 2026-10-06T18:49:18Z Content type: tutorial Language: en Sources: [Networking & Content Delivery]() Topics: [Oracle Database@AWS](), [Azure](), [Visual Studio Code]() Tags: [advanced-300](), [amazon-vpc](), [artificial-intelligence](), [aws-direct-connect](), [aws-transit-gateway](), [best-practices](), [cloud-infrastructure](), [google-cloud](), [kiro](), [microsoft-azure](), [multicloud](), [networking-content-delivery]() ### AI overview The post walks through using the Kiro agentic IDE to research, plan, provision, test, and document a private AWS-Google Cloud interconnect. It describes the cross-cloud resource sequence, approval steps for infrastructure changes, and validation results from a single 1 Gbps test deployment, while noting considerations for security, cost, and production use. ### Source excerpt Multicloud is no longer an edge case. Workloads end up spanning AWS and Google Cloud, Microsoft Azure, or Oracle Cloud Infrastructure after an acquisition, through a footprint that predates any consolidation decision, or because a key vendor operates on the other cloud. Sooner or later a network engineer gets the task: connect two environments privately [...] ## AWS Weekly Roundup: Amazon Bedrock Managed Agents powered by OpenAI, Q3 service availability updates, Kiro workflows, and more (October 5, 2026) DevFeed: [AWS Weekly Roundup: Amazon Bedrock Managed Agents powered by OpenAI, Q3 service availability updates, Kiro workflows, and more (October 5, 2026)]() Original publisher: [Read original article]() Author: Channy Yun (윤석찬) Published: 2026-10-05T15:41:19Z Content type: news Language: en Sources: [AWS News Blog]() Topics: [Amazon Bedrock](), [Amazon Bedrock AgentCore](), [AWS IAM](), [ChatGPT API](), [Amazon Elastic Kubernetes Service]() Tags: [agents](), [amazon-bedrock](), [amazon-bedrock-agentcore](), [amazon-s3-tables](), [anthropic-claude](), [aws-partner-network](), [aws-well-architected](), [aws-well-architected-tool](), [kiro](), [openai](), [strands-agents](), [week-in-review](), [weekly-roundup]() ### AI overview This AWS weekly roundup covers Amazon Bedrock Managed Agents powered by OpenAI, newly available models on Bedrock, service lifecycle changes, and other AWS launches. The managed agents preview lets developers run OpenAI model based agents within AWS using existing identity, permission, and governance controls, with self-hosted compute or Amazon Bedrock AgentCore Runtime as execution options. ### Source excerpt Last week, we announced a public preview of Amazon Bedrock Managed Agents powered by OpenAI, built on a customized version of OpenAI's Agents API engineered to be AWS-native and integrated with AWS resources. You can now build agents optimized for OpenAI models that run entirely inside AWS with the identities, permissions, and governance controls you [...] ## How Mirelo AI brought sound design to the IDE with MCP and Kiro powers DevFeed: [How Mirelo AI brought sound design to the IDE with MCP and Kiro powers]() Original publisher: [Read original article]() Author: Florian Breton Published: 2026-10-01T14:49:24Z Content type: article Language: en Sources: [AWS DevOps & Developer Productivity Blog]() Topics: [MSP MCP](), [google-antigravity](), [AI interaction best practices]() Tags: [ai](), [customer-solutions](), [developers](), [intermediate-200](), [kiro](), [mcp]() ### AI overview Mirelo AI packaged its hosted MCP server as a Kiro power, letting developers generate and edit sound effects through natural-language prompts in their development environment. The article describes the server and power architecture, including on-demand loading of tools and skills, and explains how AWS Enterprise Support helped bring the integration to the Kiro powers marketplace. ### Source excerpt Mirelo AI turned its hosted Model Context Protocol (MCP) server into a Kiro power, so developers can generate production-ready sound effects from a natural-language prompt without leaving their IDE. Learn how Mirelo built the integration and how AWS Enterprise Support helped bring it to the Kiro powers marketplace. ## Accelerating AS/400 business rule extraction with Kiro: Step-by-step guide DevFeed: [Accelerating AS/400 business rule extraction with Kiro: Step-by-step guide]() Original publisher: [Read original article]() Author: Daniel Gray Published: 2026-09-30T15:43:51Z Content type: tutorial Language: en Sources: [AWS DevOps & Developer Productivity Blog]() Topics: [Kiro](), [business logic](), [cobol](), [Tech Debt](), [Loop Engineering](), [Spec Driven Development]() Tags: [agentic-ai](), [cobol](), [customer-solutions](), [developers](), [document-generation](), [kiro](), [modernization](), [modernize](), [rpg](), [specifications](), [technical]() ### AI overview This step-by-step guide explains using Kiro, an agentic AI development environment, to extract business rules from AS/400 RPG and COBOL programs, generate technical specifications, and prepare modernization documentation. It describes analyzing dependencies and execution paths to document legacy fulfillment and shipping logic. ### Source excerpt AS/400 business rule extraction no longer requires months of manual effort. Using Kiro, an agentic AI-powered development environment, this guide shows how to extract business rules from AS/400 RPG and COBOL programs, generate technical specifications, and produce modernization-ready documentation in days instead of months. ## Building a Slack-powered AI development agent with Kiro CLI and headless authentication DevFeed: [Building a Slack-powered AI development agent with Kiro CLI and headless authentication]() Original publisher: [Read original article]() Author: Vishal Karlupia Published: 2026-09-29T16:02:43Z Content type: tutorial Language: en Sources: [AWS DevOps & Developer Productivity Blog]() Topics: [Claude Code](), [Slack](), [Postman Agent Mode](), [codex]() Tags: [amazon-api-gateway](), [api-gateway](), [artificial-intelligence](), [authentication](), [aws](), [aws-lambda](), [aws-secrets-manager](), [cli](), [container-image](), [deploy](), [developer-tools](), [devops](), [headless](), [kiro](), [slack]() ### AI overview This tutorial describes a Slack slash-command integration that runs Kiro CLI through AWS Lambda and API Gateway, returning analysis results in Slack. It explains headless API key authentication, with credentials stored in AWS Secrets Manager, and outlines building and deploying the solution. ### Source excerpt Every code review discussion, incident response thread, and standup happens in Slack. But when an engineer needs to analyze a service or debug a failing test, they leave Slack, open a terminal, navigate to the repository, run commands, and paste the output back. That round trip takes 30 seconds for someone who knows exactly where [...] ## 5 Real-World Angular + AI Case Studies Worth Reading in 2026 DevFeed: [5 Real-World Angular + AI Case Studies Worth Reading in 2026]() Original publisher: [Read original article]() Author: Daniel Glejzner Published: 2026-09-28T08:00:16Z Content type: article Language: en Sources: [Daniel Glejzner]() Topics: [Angular](), [Computer science](), [Fine-tuning]() Tags: [agentic](), [amazon-q](), [aws-transform](), [case-studies](), [developer](), [kiro](), [real-world]() ### AI overview The article surveys real-world uses of AI in Angular software teams, including large application migrations, architecture modernization, design system enforcement, and production AI features. One case describes Netsmart using AWS Transform, Kiro, and Amazon Q Developer across Angular applications totaling more than 2 million lines of code, with reported reductions in upgrade and migration effort. ### Source excerpt Real-world Angular and AI case studies covering enterprise migrations, agentic modernization, design-system-aware AI and production LLM applications. ## AI Coding Agents for Enterprise: IP Indemnity, Data Residency and 500-Seat Cost Compared DevFeed: [AI Coding Agents for Enterprise: IP Indemnity, Data Residency and 500-Seat Cost Compared]() Original publisher: [Read original article]() Author: Asif Razzaq Published: 2026-09-27T05:57:51Z Content type: comparison Language: en Sources: [MarkTechPost]() Topics: [GitHub Copilot](), [cursor](), [GitHub Copilot CLI]() Tags: [agentic-ai](), [ai-agents](), [ai-coding-agents](), [cursor](), [editors-pick](), [enterprise](), [enterprise-ai](), [for-devs](), [github](), [harness](), [kiro](), [new-releases](), [software-engineering](), [staff](), [uncategorized]() ### AI overview The article compares enterprise contract terms for GitHub Copilot, AWS Kiro, Cursor, Devin, and Windsurf, focusing on indemnity for generated code, prompt storage, admin logging, and the cost of 500 seats. It reports that Cognition's standard terms exclude outputs from indemnity and advises readers to review final terms with counsel. ### Source excerpt We read the contracts behind GitHub Copilot, AWS Kiro, Cursor, Devin and Windsurf. Copilot and Kiro offer uncapped indemnity on generated code. Cognition's standard terms exclude outputs entirely. Here is how 500 seats compare on legal exposure, prompt storage, audit logs and true cost. The post AI Coding Agents for Enterprise: IP Indemnity, Data Residency and 500-Seat Cost Compared appeared first on MarkTechPost. ## AI best practices for AWS network operations with AI agents and MCP DevFeed: [AI best practices for AWS network operations with AI agents and MCP]() Original publisher: [Read original article]() Author: Vijay Menon Published: 2026-09-25T18:49:16Z Content type: article Language: en Sources: [Networking & Content Delivery]() Topics: [Model Context Protocol](), [Network Operations](), [DevOps](), [Amazon Bedrock AgentCore](), [telemetry](), [Amazon VPC](), [Amazon CloudWatch](), [Kiro](), [AWS IAM](), [Cloud]() Tags: [ai](), [ai-agents](), [amazon-bedrock-agentcore](), [amazon-bedrock-guardrails](), [amazon-cloudwatch](), [artificial-intelligence](), [aws](), [best-practices](), [devops](), [iam](), [kiro](), [mcp](), [network-operations](), [networking-content-delivery](), [thought-leadership]() ### AI overview This AWS post presents best practices for using AI agents and MCP in network operations. It describes correlating telemetry across AWS services, supporting agentic diagnostics, and building an AI NetOps stack with scoped IAM boundaries and human approval gates. ### Source excerpt This post is for platform and SRE teams running 24/7 network operations who want AI Agents and Model Context Protocol (MCP) for intelligent, automated event response, and for individual engineers who want agentic diagnostics during development and triage. If you are in the first group, focus on AWS DevOps Agent, Amazon Bedrock AgentCore, and the [...] ## Accelerate troubleshooting with AWS Observability as a Kiro power DevFeed: [Accelerate troubleshooting with AWS Observability as a Kiro power]() Original publisher: [Read original article]() Author: Naseer Sayyad Published: 2026-09-25T17:34:16Z Content type: tutorial Language: en Sources: [AWS Cloud Operations Blog]() Topics: [Kiro](), [observability](), [Model Context Protocol](), [Amazon CloudWatch](), [AWS Lambda](), [AWS CloudTrail](), [Prometheus]() Tags: [ai-agent](), [api](), [aws](), [aws-lambda](), [deployments](), [distributed](), [kiro](), [latency](), [logs](), [management-tools](), [mcp](), [mcp-servers](), [observability](), [slo](), [traces](), [troubleshooting]() ### AI overview A hands-on guide to using AWS Observability as a Kiro power to investigate distributed-application incidents from Kiro IDE. It combines MCP servers and steering files to correlate alarms, traces, logs, deployments, and security events, and demonstrates a Lambda-backed API troubleshooting workflow. ### Source excerpt Troubleshooting a distributed application means correlating signals across alarms, traces, logs, and deployments, usually across several consoles while the clock is running. Imagine your on-call engineer gets paged at 2 AM. P99 latency on the checkout API has spiked past the SLO threshold. What follows is a familiar scramble: open Application Signals to check service [...] ## Migrate faster with AWS Transform and your AI coding agent DevFeed: [Migrate faster with AWS Transform and your AI coding agent]() Original publisher: [Read original article]() Author: Diego Dalmolin Published: 2026-09-24T20:48:35Z Content type: tutorial Language: en Sources: [Migration & Modernization]() Topics: [AWS Transform](), [Model Context Protocol](), [AI-assisted coding](), [AI Development](), [Claude Code](), [cursor](), [Kiro]() Tags: [agents](), [ai-coding-agents](), [amazon-quick-suite](), [aws-for-vmware](), [aws-transform](), [best-practices](), [claude](), [claude-code](), [cursor](), [foundational-100](), [integration](), [kiro](), [mainframe-migration](), [migration](), [tasks](), [terminal](), [thought-leadership]() ### AI overview This AWS post explains how to connect AWS Transform to AI coding agents such as Kiro, Claude Code, and Cursor through the Model Context Protocol. The integration combines migration-specific capabilities, including discovery, application grouping, wave planning, and infrastructure work, with coding-agent workflows for coordinating migration jobs and artifacts. ### Source excerpt Introduction If you already work with an AI coding agent, you know how much they can do. Tools like Kiro, Claude Code, and Cursor read repositories, reason over code, and drive multi-step tasks from an IDE or terminal. They're excellent general-purpose AI coding agents, built to help with everyday software development tasks. But planning and [...] ## Announcing Spark Connect on Amazon EMR on EC2: Interactive PySpark anywhere DevFeed: [Announcing Spark Connect on Amazon EMR on EC2: Interactive PySpark anywhere]() Original publisher: [Read original article]() Author: Al MS Published: 2026-09-23T18:28:32Z Content type: release Language: en Sources: [AWS Big Data Blog]() Topics: [Apache Spark](), [Amazon EC2](), [ide](), [Python](), [debugging](), [gRPC](), [TLS (Transport Layer Security)]() Tags: [advanced-300](), [amazon-ec2](), [amazon-emr](), [amazon-sagemaker](), [amazon-sagemaker-unified-studio](), [announcements](), [debugger](), [ec2](), [emr](), [grpc](), [ide](), [jupyter](), [kiro](), [python](), [spark](), [tls]() ### AI overview Amazon EMR on EC2 now supports Spark Connect, allowing developers to debug and develop PySpark locally through SageMaker Unified Studio Data Notebooks or IDEs while Spark runs on an EMR cluster. The post explains the client-server architecture and how to get started. ### Source excerpt Amazon EMR on EC2 now supports Spark Connect, so you can develop and debug PySpark interactively from Amazon SageMaker Unified Studio Data Notebooks or your own IDE while Spark runs on your cluster. This post shows you how to get started from both a Data Notebook and a local IDE. ## From insight to innovation: How Kiro Crew, Dynatrace, and AWS are helping teams do more DevFeed: [From insight to innovation: How Kiro Crew, Dynatrace, and AWS are helping teams do more]() Original publisher: [Read original article]() Author: Erick Leon Published: 2026-09-23T00:07:22Z Content type: article Language: en Sources: [Dynatrace news]() Topics: [observability](), [dynatrace](), [Amazon Web Services](), [AI interaction best practices](), [AI Integration Strategies](), [Deployment Strategies]() Tags: [agentic-workflows](), [ai](), [aws](), [dynatrace](), [kiro](), [observability](), [partners]() ### AI overview The article describes an open-source Dynatrace app for Kiro Crew that uses dtctl and DQL queries against Grail to display problems, services, and applications on a Kanban board. A Kiro Crew agent ranks open problems with explanations and can start read-only investigations or prepare summaries, with a human approving actions that affect live systems. ### Source excerpt Modern businesses are sitting on a goldmine of information. Your observability data can help you understand what is breaking, what is underperforming, and where your next improvement should come from. The gap isn't data - it is the distance between seeing something and being able to do something about it That's exactly what Kiro Crew, [...] The post From insight to innovation: How Kiro Crew, Dynatrace, and AWS are helping teams do more appeared first on Dynatrace news. ## Introducing Amazon CloudWatch Omni: AI-powered observability for generative AI and agentic workloads DevFeed: [Introducing Amazon CloudWatch Omni: AI-powered observability for generative AI and agentic workloads]() Original publisher: [Read original article]() Author: Daniel Abib Published: 2026-09-22T22:21:13Z Content type: release Language: en Sources: [AWS News Blog]() Topics: [Amazon CloudWatch](), [observability](), [observability ai agents](), [Artificial Intelligence](), [Generative AI](), [telemetry](), [ide](), [Kiro](), [experiments](), [dashboards](), [Single sign-on (SSO)]() Tags: [ai-agents](), [amazon-cloudwatch](), [dashboards](), [experiment](), [generative-ai](), [ide](), [kiro](), [launch](), [login](), [management-tools](), [news](), [observability](), [trace]() ### AI overview Amazon CloudWatch Omni is a unified observability, evaluation, and experimentation solution for generative AI and agentic workloads. It provides tracing, built-in quality evaluators, prompt comparison, test datasets, experiments, and regression detection through IDE extensions and a standalone web experience. ### Source excerpt Learn how Amazon CloudWatch Omni delivers AI-powered observability purpose-built for generative AI and agentic workloads. Trace, evaluate, and experiment with AI agents across any framework--directly from your IDE or a standalone web experience--using open standards and built-in evaluators for quality, correctness, and coherence. ## 'Vibe coding is not going away': AWS' Darko Mesaroš on AI and the future of coding DevFeed: ['Vibe coding is not going away': AWS' Darko Mesaroš on AI and the future of coding]() Original publisher: [Read original article]() Author: Bijin Jose Published: 2026-09-18T07:51:40Z Content type: news Language: en Sources: [Technology | The Indian Express]() Topics: [Artificial Intelligence](), [Software Engineering](), [Vibe coding](), [Kiro](), [Spec Driven Development](), [AI Development](), [ide](), [Requirements](), [Developer Tools]() Tags: [agentic-ai](), [ai](), [ai-agents](), [ai-and-coding](), [ai-coding-tools](), [ai-development-india](), [ai-software-development](), [artificial-intelligence](), [aws-darko-mesaros](), [aws-developer-advocate](), [aws-developer-ecosystem](), [aws-india](), [darko-mesaros-interview](), [developer](), [developers](), [developers-and-ai](), [development](), [engineering](), [future-of-software-development](), [ide](), [indian-developers](), [kiro](), [kiro-ai](), [kiro-crew](), [kiro-ide](), [requirements](), [sdd](), [software-development](), [software-engineering](), [spec-driven-development](), [technology](), [technology-artificial-intelligence](), [vibe-coding]() ### AI overview AWS developer advocate Darko Mesaroš discusses how AI is changing software development, arguing that developers' roles are expanding beyond coding. He contrasts exploratory vibe coding with the more structured, requirements-first approach of spec-driven development and discusses AWS Kiro, AI coding tools, and the continuing importance of software engineering skills such as systems design, scaling, operations, and understanding customers. ### Source excerpt AWS Distinguished Developer Advocate Darko Mesaroš explains how AI is changing software development and why developers still need to look beyond coding. [Next page]()