AWS Kiro IDE: Not Just Another AI Toy for Developers

Hello Devs,

Every morning, after my usual routine, I scroll through Reddit. It’s part habit, part curiosity—a way to catch up on what’s happening in the world of AWS and product building.

But let me be clear: I’m not the kind of developer who jumps on every trendy new tool the moment it launches. Unless I see a clear reason it could help me in my real work, I usually wait and watch.

That’s because right now, I’m working under a strict timeline for GTM (Go-To-Market). I’m constantly evaluating anything that might help me ship faster without sacrificing quality.

I have Figma designs ready for most of our screens. Just last week, I built out an entire chat module—including both the UI and backend APIs—using ChatGPT to help speed up the process. Tools like that are no longer just experiments for me; they’re part of how I stay on track to deliver.

So when I saw AWS’s Reddit post about Kiro IDE, I wasn’t sure I’d even try it. Another AI tool? There’s been a flood of those lately.

But there was something different about how AWS positioned Kiro. They weren’t just talking about generating code. They were talking about helping developers go all the way from ideas and specs to production systems. That’s exactly where most AI tools fall short.

Given the tight timelines I’m working under, I figured: If this can help me move faster for real product work, it’s worth testing. So I decided to give it a shot—using a real feature I’m building for NuShift Connect.

At NuShift Connect, we’re building a health-focused social platform. Recently, I’ve been working on an additional feature: Groups. It’s a way for users to create dedicated communities around health topics—like cancer awareness, fitness journeys, or wellness discussions.

This Groups feature isn’t my entire product. It’s one of many pieces I’m layering onto an existing, fairly complex codebase. And that’s why I usually don’t rush into brand-new tools. I can’t afford to break my momentum or disrupt working systems.

But Kiro IDE made me curious because it seemed like it might help me structure my feature properly from the start, keep my thinking organised as I integrate with existing code, and speed up writing UI components in Angular.

I was at the GenAI Loft last week, where I attended a session on the AI Development Lifecycle (AIDLC) by Siddhesh Jog. He talked about how successful product development often requires moving through clear phases—from ideation and requirement gathering to building, testing, and deploying. As I explored Kiro, I could see that it’s built with that same philosophy in mind. It feels like a tool designed to guide product builders step by step through that lifecycle, rather than just spitting out random code

One thing that stood out right away was Kiro’s idea of Specs. Instead of just throwing random prompts at an AI, Kiro pushes you to define what you’re building—almost like writing requirements documentation but right inside your IDE.

For the Groups feature, I wrote out a spec something like this:

• Users can create new groups with a name, description, and cover image

• Groups can be public or private

• Each group has posts, comments, and member lists

• We already have user profiles and authentication in place

• My initial focus is designing the UI and Angular components—not backend APIs yet

The moment I saved this spec, Kiro started suggesting Angular component structures (Group List, Group Detail, Create Group form), folder organization ideas for modularity, and approaches for handling reactive forms and UI state.

Instead of dumping generic code snippets, Kiro was shaping its suggestions around my actual app structure and requirements.

This was the part that felt genuinely useful. I’ve used plenty of AI tools before—like when I built our chat module last week using ChatGPT to generate both frontend components and backend API scaffolding. That experience showed me how much time AI can save if used well.

Kiro felt similar, but with even more context awareness for Angular. It suggested splitting my UI into smart, modular components. It helped me outline reactive forms for group creation, including validation logic. It offered ways to connect state between components. It even reminded me to think about loading states and empty UI screens.

I haven’t wired up the backend yet—that’s the next step. But just for the Angular side, Kiro saved me hours of manual scaffolding and planning.

Another thing that impressed me was how Kiro handles Hooks and Steering.

Hooks are like background automations. For example, when I updated my spec, Kiro prompted me to adjust my component interfaces and possibly update tests. Small nudges, but useful.

Steering lets you guide how the AI writes code. I could tell Kiro to stick to Angular best practices, use TypeScript consistently, and avoid certain libraries we don’t use at NuShift. Instead of me manually rewriting AI code every time, Kiro started adapting to my way of working.

While exploring Kiro’s UI, I saw something called MCP Servers. From what I gather, this lets Kiro connect to real tools and data sources, like fetching live DynamoDB schemas, reading API specs, or integrating with CI/CD systems. I haven’t tested this yet, but the idea that Kiro could be aware of my real environment—not just write generic code—is intriguing for the future.

It’s Still Early, But It Feels Different

Like any new tool, Kiro isn’t perfect yet.

• Sometimes its suggestions were too generic, especially for UI styling details. Even when I gave it Figma screenshots, it didn’t always pick up the exact styles or color codes. I found myself needing to do more prompting to match our design system.

• To be fair, this is something I could solve better by using Kiro’s Specs feature upfront. You can define your product’s brand guidelines, color palettes, typography, and other design rules directly in specs. I simply hadn’t included those details in my initial prompts—but next time, I plan to try it.

• It occasionally misunderstood the relationships between my existing components. For example, our codebase has some duplicate components and modules left over from older versions, with similar names scattered in different folders. Instead of deleting unused modules, previous developers just added new ones alongside them. That created confusion for Kiro, which sometimes pulled from the wrong places.

• It’s a reminder that tools like Kiro work best if your codebase is clean—or at least if you set ground rules or do some upfront cleanup so the AI knows what’s current and what’s legacy.

• Even when there was an error running ng serve, Kiro didn’t automatically detect it or suggest fixes. I had to explicitly prompt it to run the command, read the error output, and help debug the issue.

• I was thinking it would be amazing if Kiro could automatically stop ng serve after making changes, rerun it, and check for errors. But I also realize that might get tricky. If the error doesn’t get fixed properly, it could send Kiro into an infinite loop of stopping, starting, and trying again—something I’ve definitely seen happen with ChatGPT during debugging.

I haven’t tested how well it handles full-stack API integrations yet.

That said, it’s worth remembering Kiro is brand new, and everyone—from developers to tech media—is still exploring what it can really do.

But even with those limitations, I came away feeling like this is not just another AI toy.

For the first time, I felt like an AI tool was working with me to build a real feature—not just generating isolated snippets of code.

Why Product Builders Should Pay Attention

I’m usually cautious about new tools, especially trendy ones. But Kiro felt different because it fits how product builders actually work.

We don’t just write code—we design systems and think about integration. We have existing codebases, not blank slates. We need speed—but also clarity and maintainability.

Kiro helped me think through my Groups feature in a structured way. It saved time on my Angular scaffolding. And it kept me focused on building something that fits into NuShift Connect—not just playing with isolated code.

I have a strict GTM timeline and a long list of features to deliver. Recently, AI tools like ChatGPT have helped me build faster—like with our new chat module. But Kiro feels like a step forward because it’s built for product builders, not just for writing isolated pieces of code.

If AWS keeps evolving it, Kiro could be one of the most useful developer tools they’ve launched since Lambda.

My Takeaway

My advice? If you’re curious, don’t just read blog posts (even this one). Pick a real feature you’re working on and try building it in Kiro IDE. That’s when you’ll see whether it’s just another AI experiment—or a glimpse of how we’ll build products in the future.

Next, I’m planning to see how Kiro handles backend integrations for Groups—especially how it manages API design and DynamoDB schemas. I’ll share those results once I’ve tested it further. Right now, Kiro IDE is free during its preview period. I’m not sure what pricing AWS will introduce later—but for now, it’s worth trying if you’re curious.

TL;DR: Kiro IDE isn’t just an AI code generator. It feels like a real partner helping me think through product features and ship faster under tight deadlines.

References Worth Exploring

If you’re curious to dig deeper into Kiro IDE, here are some good places to start:

Introducing Kiro: The Agentic AI IDE (AWS Blog)

Kiro IDE Official Site & Documentation

Kiro: The new Agentic AI IDE from AWS (DEV.to)

Kiro Agentic AI IDE — Beyond a coding assistant (DEV.to)