Aws

If you’re running more than a handful of MCP-backed agents in production, you’ve already hit the wall: point-to-point connections between every agent and every MCP server create an N×M integration nightmare, credential sprawl, and zero visibility into who’s calling what. AWS’s answer in 2026 is a two-layer governance stack — Bedrock AgentCore for per-agent MCP server integration and access control, and the A2A Gateway for centralized agent registry, discovery, routing, and rate limiting across your entire fleet. Here’s how to set it up, what each piece actually does, and where the sharp edges are. ...

AWS Agent-EvalKit is an open-source toolkit (Apache 2.0, released June 11, 2026) that runs AI agent evaluation directly inside your coding assistant via slash commands. Instead of treating agent evaluation as a post-deployment activity, it brings a six-phase workflow — Plan, Data, Trace, Run Agent, Eval, Report — into Claude Code, Kiro CLI, or Kilo Code, combining code-based evaluators with LLM-as-judge scoring through Amazon Bedrock. I’ve been running evaluations against AI agents for the last two years, and the pattern I kept seeing was: teams either buy a managed eval platform or cobble together Python scripts and a prompt template. Agent-EvalKit splits the difference — it’s a CLI that reads your agent source code, generates test cases, instruments tracing, runs the trials, and recommends fixes with file-level accuracy. In this tutorial, I’ll walk through installing it, running your first evaluation, and the real-world case study where it caught a hallucination problem that output-level testing missed entirely. ...

Amazon Bedrock AgentCore is AWS’s production platform for deploying, securing, observing, and governing AI agents built with frameworks such as LangGraph, CrewAI, LlamaIndex, and Strands Agents. Use it when your agent needs managed runtime isolation, enterprise identity, tool governance, memory, evaluation, and AWS-native operations instead of another prototype server. What Is Amazon Bedrock AgentCore? Amazon Bedrock AgentCore is a managed AWS platform for taking code-first AI agents from local development to production operations with runtime hosting, memory, identity, tool access, observability, policy, browser automation, and code execution. AWS made AgentCore generally available on October 13, 2025, and GA added VPC, AWS PrivateLink, AWS CloudFormation, and resource tagging across its services. The important detail is that AgentCore is not a new prompt format or a single agent framework. It is the production control plane around agents you already build with frameworks such as LangGraph, CrewAI, LlamaIndex, and Strands Agents, and it can work with different foundation models. The platform matters because production agents fail in places demos ignore: credentials, network boundaries, tool authorization, memory drift, tracing, replay, cost, and incident response. The takeaway: Amazon Bedrock AgentCore is the AWS operations layer for serious agent deployments. ...

If you want an AWS-native AI workflow fast, Strands is the practical middle ground: you get a lightweight agent framework, native MCP-style tool ergonomics, and an upgrade path to Bedrock AgentCore without rewriting core logic. In the first 20 minutes you can run a tool-calling agent that answers real customer questions, saves session context, and is deployable to Lambda. Why is this tutorial AWS-native (Strands vs alternatives)? Strands is an agent SDK that gives AWS-focused teams a small orchestration surface and practical escape hatches, while keeping the execution model familiar enough to adopt quickly. The strands agents quickstart aims for a first working agent in under 20 minutes, and AWS’s own serverless guidance says enterprise adoption of agentic capabilities could rise to 33% by 2028 from under 1% today. In July 2026 GitHub statistics still show the ecosystem split: Strands SDK has around 6,106 stars, far smaller than LangGraph’s 34,458 and OpenAI Agents Python’s 27,084, which means it is lighter and less opinionated but not yet overengineered. For teams already shipping on AWS, Strands’ advantage is reduced infrastructure churn: you can start with plain Lambda functions and later move to Bedrock AgentCore when runtime controls and session management become a governance requirement. For this reason, Strands is usually the right first move when speed and AWS-native operations matter. ...

AWS Frontier Agents reached general availability on March 31, 2026, marking the most significant expansion of Amazon’s AI portfolio since Bedrock launched in 2023. Two production-ready agents — the AWS Security Agent and the AWS DevOps Agent — are now available to all AWS customers, backed by Amazon Bedrock and powered by Claude models from Anthropic. This review covers architecture, performance benchmarks, competitive positioning against Azure AI Agent Service and Google Cloud Agent Builder, and a practical deployment guide so you can get Frontier Agents running in your own pipeline today. ...

Amazon Q Developer is AWS’s full-spectrum AI coding assistant that covers IDE completions, agentic task execution, security scanning, and deep AWS infrastructure context — all for $0 on the free tier or $19/user/month on Pro. If your team runs heavily on AWS, it’s the only AI tool that actually understands your real infrastructure. If you’re cloud-agnostic, there are better options. What Is Amazon Q Developer? Amazon Q Developer is AWS’s AI-powered software development assistant, launched in 2024 as the successor to Amazon CodeWhisperer and rapidly expanded into a full-spectrum tool covering IDE completions, CLI integration, AWS Console Q&A, agentic multi-file coding, security scanning, and legacy code transformation. Unlike GitHub Copilot or Cursor, which are cloud-agnostic by design, Amazon Q Developer is purpose-built for teams operating on AWS — it can answer questions about your actual infrastructure, generate CloudFormation templates from your existing account context, and identify cost anomalies in your running services. In 2026, AWS reports the transformation agent alone has saved over 4,500 developer years and driven $260 million in annual cost savings across enterprise customers. The tool is available in 11 default AWS regions plus 8 opt-in regions (19 total), supports over a dozen languages including C#, Go, Kotlin, Rust, and Terraform, and integrates with VS Code, JetBrains IDEs, and the AWS CLI. For teams where AWS represents the majority of daily work, Q Developer’s tight infrastructure integration changes the value calculation compared to every other AI coding tool on the market. ...

Kiro is AWS’s spec-driven AI IDE built on VS Code that turns your feature description into structured Requirements, Design, and Task artifacts before writing a single line of code — a deliberate rejection of “vibe coding” that trades instant gratification for production-grade repeatability. What Is Kiro AI IDE? Kiro is an AI-powered IDE launched by AWS in July 2025 that reached general availability with a free tier by March 2026. Unlike Cursor or GitHub Copilot, which bolt AI onto the traditional code-as-you-type workflow, Kiro introduces a fundamentally different programming model: you describe what you want to build, the agent generates a structured specification (requirements document, design document, task list), and only then does it execute code. Built on Code OSS — the same open-source foundation as VS Code — Kiro ships with Amazon Bedrock model access, routing tasks to Claude, Amazon Nova, or other foundation models depending on complexity. The 128K token context window and fractional credit billing (tracked in 0.01 increments) are designed for professional workloads. VibeCoding’s production-focused review rated it 8.4/10; a post-incident review from Heyuan110 gave 7.5/10 after the December 2025 AWS outage event. The gap between those scores is the gap between what Kiro can do when used correctly and what happens when autonomous agents meet production permissions without guardrails. ...