Your API isn’t an MCP tool! What’s intuitive for humans often overwhelms agents. Discover how to design MCP tools with AI agents in mind — and why AgentExperience (AX) matters — in my latest article in AINativeDev.

Out of the blue, AWS just dropped Kiro , a new AI-powered IDE — a VS Code–based, agentic platform that some are calling a Cursor killer . At its core is Anthropic’s Claude Sonnet 4, and it introduces a structured approach called spec-driven development . The idea? Take vibe-coding one step further by splitting the process into two clear phases: Plan first — Define what needs to be built through structured specs. Execute second — Let agents carry out the work, task by task. It’s a clean break from chaotic code generation — and a signal that the future of AI coding tools might be less “guess and generate” and more “think, then build.” Let’s look at the 4 new features that Kiro enables for vibe-coders: 1. Spec Coding — Plan First, Code Later! One of the most significant innovations introduced by AWS and Kiro is the use of structured spec files before any code is generated. This planning-first approach helps provide clarity and context for both developers and AI agents. Kiro generates three key files: requirements.md – Defines what needs to be built using user stories, acceptance criteria, and functional requirements. design.md – Describes the high-level architecture, components, and interfaces involved. tasks.md – Breaks the project into smaller, actionable tasks that can be executed independently. By defining the scope and structure upfront, this approach enables large language models to produce more reliable, production-ready code, addressing a key limitation in many current AI-powered IDEs. 2. Hooks — DRY! In the Infrastructure as Code world, the principle of Don’t Repeat Yourself (DRY) is key to reducing redundancy. Kiro’s Hooks follow the same idea — designed to automate and standardize repetitive tasks in your workflow. Hooks can be triggered automatically (e.g. on file save or commit) or manually. When triggered, they send a predefined prompt to an AI agent to carry out a task. These can be used for: Generating or updating unit tests Refreshing documentation Performing security checks or code audits 3. Agent Steering — Keep Your AI from Going Rogue! To maintain consistency across a codebase, Kiro supports steering files that provide additional context to the AI engine. The default files include: product.md – to describe product goals and business logic tech.md – to document technologies, APIs, or libraries in use structure.md – to define project structure, naming conventions, or folder layout You can also create custom steering files. These files act as long-term knowledge sources that ensure the AI aligns with how your team works. 4. Auto Pilot Mode — Go get a coffee! Kiro supports two modes of execution for tasks: Autopilot , where tasks run end-to-end with minimal input Supervised , where each change is shown as an inline diff before being applied Final Thoughts: The AI IDE space is evolving quickly. We’re seeing major players enter with different ideas about how AI should support software development. Kiro stands out with its structured, spec-first approach and focus on long-term code quality. Still, this space is in its early stages. It’s too soon to say which tools or approaches will become the standard. But one thing is certain: we’re at the beginning of a major shift in how software is built and maintained. P.S. Follow me on LinkedIn