Kiro: The (Power)ful Agentic IDE

  • Paweł
    Paweł
    Engineer

Because an agent without powers is just a very confident autocomplete.

Agentic IDEs are everywhere right now. Most of them promise the same thing: “Just talk to the AI and it will build things for you.” Sometimes that works. Often, it doesn’t. And when it fails, you’re usually left wondering why the agent hallucinated a fix - or why it did nothing at all.

Kiro takes a different approach. It rejects the idea that software development is one long, unstructured chat. Instead, it splits the process into explicit phases, gives the agent autonomy with clear boundaries, and treats intent as a first-class concept. The goal isn’t to provide more magic - it’s to deliver predictability.

This is where Kiro changes the game.

A Quick Look at Agentic IDEs

At a high level, agentic IDEs try to answer one question:

How do we move from “AI helps me write code” to “AI helps me build software”?

Most current tools in this space share a few ideas. If you’ve used tools like Cursor, GitHub Copilot Chat, Claude Code, or Windsurf, the pattern will feel familiar:

  • Conversational Interfaces.
  • Codebase Awareness.
  • The Ability to generate, refactor, and explain code.
  • Some level of task execution.

Where tools differ is structure. Some IDEs treat development as a single chat stream. In this model, planning, design, requirements, and implementation all blur together. This can feel fast at first - until requirements grow, edge cases appear, or more people get involved.

Kiro assumes that structure is not optional. It designs the whole experience around that assumption.

Where Kiro Really Shines

Kiro is especially strong when things are already… messy.

Legacy systems, partial knowledge, missing documentation. This is where most agentic IDEs fall short. They are designed to generate code, but they don’t help you understand what you’re working with.

Kiro doesn’t write code; it audits it. Because Kiro treats requirements and design as explicit artifacts, it can be used to reverse-engineer complex systems, the agent helps you bridge the gap between implementation and intent:

  • Read existing implemented code.
  • Extract implicit business rules.
  • Transform rules into human-readable documents.

Flipping the Workflow

Instead of guessing how a system behaves, the agent helps you:

  • Read the implementation and tell you what the code is actually doing.
  • It turns that logic into clear, human-readable requirements.
  • You can take those requirements to your team or stakeholders and say, "Is this what we want to do?"

For teams dealing with legacy code, this is huge. Documentation stops being a chore you do at the end of a project and becomes a way to discover what you already have.

Kiro doesn't just help you write new features, it gives you the way to know what should stay, what needs to change, and what should be deleted.

Recently, this approach was tried on a legacy project where business rules were scattered across multiple services and condition branches. Instead of manually tracing execution paths, Kiro was used to extract requirements directly from the implementation. The result wasn't perfect, but it was good enough to validate assumptions with stakeholders – and that alone saved hours of meetings and digging through code.

Vibe Mode: Exploration Without Commitment

Most modern AI IDE workflows look something like this: You have a problem, you describe what you want, and the agent starts generating code. You iterate, refine the prompt, and gradually move toward a solution.

This conversational, iterative way of working has become familiar thanks to tools like Cursor, Copilot Chat, or Claude Code. Vibe Mode in Kiro works in a very similar way, this is where you:

  • Explore ideas.
  • Ask open-ended questions.
  • Experiment before you fully understand the problem.

If you’ve used other agentic IDEs, this will feel natural. The interaction is conversational and intentionally loose. Vibe Mode works best when:

  • Requirements are fuzzy.
  • You’re still shaping the problem.
  • Speed matters more than precision.

But Kiro doesn’t pretend that “vibes” are enough for building real software. As complexity grows, it’s the signal to move from Exploration to Execution.

Spec Mode: Making Intent Explicit

In most AI-driven workflows, the transition from exploration to implementation is blurry. You start with a conversation, refine prompts, and at some point code just starts appearing. Planning, design, and implementation often happen in the same stream.

This works well for small tasks. But as complexity grows, the lack of structure starts to hurt. Decisions get buried in chat history, requirements drift, and it becomes harder to understand why something was built a certain way.

Spec Mode exists to make that transition explicit. Instead of jumping straight into code, you move through a defined workflow: Requirements → Design → Tasks. This flow is reflected directly in the project via the .kiro directory:

.kiro/
└── user-stories/
    └── user-story-name/
        ├── requirements.md
        ├── design.md
        └── tasks.md
You can create specs manually or ask Kiro to generate them for you.
In an ideal world, documentation would always exist upfront - but we all know reality usually looks different.

Each file answers a different question:

  • Requirements: What problem are we solving? → Provides Business Visibility.
  • Design: How are we going to solve it? → Provides Architectural Clarity.
  • Tasks: What needs to be done, step by step? → Removes Agent Guesswork.

By working with these documents instead of inferring intent from chat history, the agent works with explicit, versionable documents.

Powers: Capabilities with Boundaries

One of the biggest risks in agentic IDEs is ambiguity. An agent can “do things,” but what exactly does that mean? Can it modify files, run commands, or refactor large parts of the codebase? When these boundaries are unclear, behavior becomes unpredictable and harder to trust. Kiro addresses this by making capabilities explicit through Powers. Instead of assuming what the agent can or cannot do, Powers clearly define what actions are allowed, which tools the agent can use, and how far its autonomy goes. This makes agent behavior easier to reason about, safer in real projects, and far less surprising.

Beyond “Just an MCP”

It’s easy to assume Powers are just MCP with a different label. While Kiro leverages MCP under the hood, the difference is in the governance. Unlike standard MCP servers that often load every tool at once, cluttering context and driving up token costs. Powers are on-demand, scoped, and bundled.

In Kiro, you don't assume what an agent can do, you grant it specific capabilities. This shift from "blind autonomy" to "granted access" makes agent behavior:

  • Easier to reason about.
  • Safer in real projects.
  • Far less surprising.

Summary

Kiro doesn’t try to be clever. Instead, it’s designed to be intentional. By separating exploration from execution and treating agent capabilities as granted "Powers," Kiro moves beyond the limitations of the typical chat-driven IDE. It stops treating AI as a magic wand and start treating it as a great engineering partner.

Ultimately, Kiro is a choice between two philosophies:

  • If you enjoy unstructured conversations and magical outputs, Kiro might feel strict.
  • If you care about clarity, predictability, and working with real-world codebases, it might feel refreshingly honest.

Sometimes, the most powerful agent isn’t the one that promises to do everything, it’s the one that knows its limits and works within yours.