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The post describes a reproducible PHP-to-Java migration 'engine' built as versioned Kiro skill files (php-to-java-migration and php-model-discovery) that run an eight-phase, parity-first workflow inside a .kiro workspace. It also notes a native integration so AWS Transform can run the same skill-driven pipeline agentically at portfolio scale while Kiro supports interactive, single-app runs.
Eon implemented a centralized authenticated gateway using Amazon Bedrock AgentCore (Gateway, Runtime, Policy, Identity) to replace per-tool credentials and local MCP processes, giving engineers and autonomous agents unified access to investigation tooling across AWS, GCP, and Azure. The change reduced developer onboarding for investigation tooling from about two days to roughly two minutes and lets Eon's autonomous SRE agent (Claude on Bedrock) access the same tools with separate identities and permissions.
Smartsheet has launched a generally available (GA) Model Context Protocol (MCP) Server hosted on AWS that lets AI assistants read, write, and manage Smartsheet data via a standardized MCP interface. The server supports Streamable HTTP transport, MCP spec versions 2025-06-18 and 2025-03-26, three regional endpoints, OAuth 2.0 or Personal Access Token auth, and is shown integrated with Amazon Quick and the Kiro IDE as MCP clients.
Amazon introduced the aws-ai-ml skill available through the Agent Toolkit for AWS, which plugs into MCP‑compatible coding agents (e.g., Kiro, Claude Code, Codex) to benchmark SageMaker endpoints, recommend deployment configurations, compare performance runs, and generate executable SageMaker Python SDK v3 code. The skill can be used locally via the Agent Toolkit or inside Amazon SageMaker Studio JupyterLab and requires AWS credentials with SageMaker API permissions.
Dutch retailer HEMA created HAL, an internal AI assistant that consolidates fragmented internal knowledge and delivers answers inside existing tools by exposing sources as MCP tools and running agents on Amazon Bedrock AgentCore. Identity is anchored with Microsoft Entra ID, and AgentCore features (Gateway, Runtime, Memory, Guardrails) were used to avoid custom MCP server infrastructure.
Hugging Face published six open-source 'Skills' (GitHub) that let coding agents orchestrate end-to-end deployment of Hugging Face models to Amazon SageMaker AI. The skills automate selecting the correct serving container from AWS Deep Learning Containers, creating real-time or serverless endpoints with autoscaling and CloudWatch alarms, and provide verified teardown paths; they run using Python and the AWS CLI and are designed to prevent fragile or costly agent-made deployment mistakes.
A released open-source collection of 38 'HCLS Agent Skills' (MIT-0) encodes domain decision procedures as structured SKILL.md documents across 11 healthcare and life‑sciences domains to guide agentic AI. The repository and CLI aim to make reasoning and pipeline procedures auditable and portable across agent harnesses (Kiro, AWS Strands/Bedrock AgentCore, Claude Code, OpenAI Codex and others) and the authors report 70–86% win rates for agents with skills vs. without on evaluated workflows.
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