There's a peculiar feeling when you realize the tool you're using isn't just helping you, it's thinking alongside you. That moment came for me two weeks into building AEGIS, a cognitive RPA engine that translates natural language into desktop automation. The kind of project that, by all reasonable estimates, should have taken a team of five at least two months.
I built it in under two weeks. With Kiro.
What Kiro Actually Is
Let me be clear about something: Kiro is a VS Code-based IDE. You can use it exactly like VS Code; same shortcuts, same extensions, same muscle memory. But here's where it gets interesting: it has an autopilot mode that doesn't just autocomplete your code. It understands your project.
The first time I opened my dual-workspace setup (Python backend, Flutter frontend), Kiro didn't blink. It grasped the architecture, the relationships between files, the contracts between services. When I asked it to implement WebSocket streaming between the two stacks, it generated type-safe code on both ends that worked on the first run.
That's not autocomplete. That's comprehension.
The AEGIS Vision
AEGIS is ambitious: a cognitive automation engine powered by Google's Agent Development Kit and Gemini. You tell it what you want in plain English; "Open Chrome, navigate to my email, download the latest invoice"; and it figures out the clicks, the keystrokes, the window management. Real-time streaming shows you every step as it happens.
This meant building:
- A FastAPI backend with ADK integration, plan caching, and session management
- A Flutter desktop frontend with Provider state management and WebSocket updates
- Property-based tests, comprehensive error handling, and cross-platform support
The scope was terrifying. The execution was... surprisingly calm.
The Three Features That Changed Everything
1. Specs That Think Ahead
Kiro's spec system isn't documentation, it's a contract with your future self. I structured each feature with requirements.md, design.md, and tasks.md. But here's the magic: Kiro doesn't just read these files. It reasons about them.
When I wrote "User can submit natural language task and see real-time progress," Kiro extrapolated the WebSocket architecture, the state management patterns, the error boundaries. It broke down implementation into granular tasks with surprising foresight and anticipating edge cases I hadn't considered.
Starting was a breeze. The tasks.md interface became my command center.
2. Steering That Remembers
I created steering documents once: product.md, structure.md, tech.md. From that point forward, every conversation with Kiro was grounded in those decisions. No more "remember, we're using Provider not Bloc." No more "the backend uses FastAPI, not Flask."
The steering rules auto-configured from my specs. Kiro maintained consistency across 60+ files without me repeating myself. It felt less like configuring a tool and more like onboarding a very attentive colleague.
3. Hooks That Automate the Tedious
I set up hooks that ran tests on save, reminded me to sync models between stacks, and auto-formatted code. Desktop notifications told me when long-running tasks completed. The feedback loop tightened from minutes to seconds.
But the real win? The hooks freed my mind. I stopped context-switching to run tests. I stopped worrying about forgetting to update the frontend when the backend changed. I just... built.
The Hybrid Workflow
Here's what I learned: spec-driven development and vibe coding aren't opposites. They're complements.
Specs gave me structure for complex, multi-file features; the WebSocket system, the session management, the RPA engine. I could stop mid-feature, return days later, and Kiro picked up exactly where we left off.
Vibe coding gave me speed for polish; UI tweaks, error messages, loading states. Quick conversations, immediate results.
The basic LLM chat remained my entry point for questions, clarifications, and ad-hoc changes. It could initiate refactors prudently, answer architectural questions, and pivot when requirements shifted.
The Numbers
- 60+ files across two workspaces
- Full test coverage with unit, widget, and property-based tests
- Real-time WebSocket streaming between Python and Flutter
- Under two weeks from concept to working prototype
A project that would have required a team, compressed into focused solo sessions with an AI that genuinely understood the vision.
The Epiphany
There's a moment in every ambitious project where you either drown in complexity or find a way to float above it. Kiro gave me that flotation.
It's not that the AI wrote my code for me. It's that it held the context I couldn't hold alone. It remembered the decisions I'd made, anticipated the problems I'd face, and executed with a speed that matched my thinking.
AEGIS isn't finished. But it's real. It works. And it exists because the tooling finally caught up to the ambition.
That's what Kiro is: not a replacement for developers, but an amplifier. The difference between playing every instrument yourself and conducting an orchestra.
I'm still the one with the vision. Kiro just makes sure nothing gets lost in translation.
Building something ambitious? The tools have changed. Maybe it's time your workflow did too.
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