Key takeaways
- AWS-backed IDE that pioneered spec-driven development — turning prompts into structured requirements, design docs, and task lists before writing code
- Three-layer agent system: specs for planning, hooks for background automation, and skills for on-demand expertise via the SKILL.md standard
- "Auto" model router uses Sonnet 4.5 plus specialized models for intent detection and caching to balance quality, latency, and cost
- Mixed reception — praised for spec-driven approach but criticized for bugs, reliability issues, and IDE maturity compared to Cursor and VS Code
FAQ
What is Kiro?
Kiro is an agentic AI development environment from AWS that combines spec-driven development, agent hooks for background tasks, and SKILL.md-based skills in a VS Code-compatible IDE.
How does Kiro's spec-driven development work?
You describe what you want in natural language. Kiro converts it into structured requirements, a design document, and implementation tasks. Agents then execute those tasks with human approval gates between phases.
How much does Kiro cost?
Free tier gets 50 credits/month. Pro is $20/month for 1,000 credits. Pro+ is $40/month for 2,000 credits. Power is $200/month for 10,000 credits. Overage is $0.04/credit on paid plans.
What models does Kiro use?
The default "Auto" agent uses a mix of frontier models including Claude Sonnet 4.5 combined with specialized models for intent detection and caching. You can also select Sonnet 4.5 directly.
What Is Kiro?
Kiro is AWS's agentic AI development environment — a VS Code-compatible IDE designed around the premise that AI agents need more structure than a chat prompt to build production software . It launched in mid-2025 and has iterated rapidly through 2026, adding custom subagents, agent skills, and enterprise controls .
The core philosophy: agents work better with structured input. Rather than "vibe coding" from a prompt, Kiro converts natural language into specs, designs, and task lists before any code is written.
How It Works
Three-Layer Agent System
Specs — Kiro's signature feature. You describe what you want; Kiro generates structured requirements, a design document, and implementation tasks. Each phase has a human approval gate. This is philosophically aligned with GitHub Spec Kit's approach but built directly into the IDE.
Hooks — Background automation triggers. When you commit, save, or complete a task, hooks can automatically run agents for documentation updates, unit test generation, or performance optimization. Think CI/CD but for agent workflows inside the IDE.
Skills — Based on the Agent Skills open standard (SKILL.md format) . Skills are modular instruction packages loaded on-demand when the agent identifies a relevant task. Kiro supports two discovery tiers:
- Workspace skills (
~/.kiro/skills/) — personal, available everywhere - Project skills (
.kiro/skills/) — committed to version control, shared with team
Workspace skills take precedence when names collide. Skills can be imported directly from GitHub URLs .
Powers — Kiro's proprietary enhancement layer. Curated packages of MCP servers, steering files, and hooks that can be dynamically loaded. Powers go beyond skills by bundling tooling configuration alongside instructions.
Custom Subagents
Added in Kiro 0.9 , custom subagents let you split context by domain. A frontend-agent loads Chrome DevTools and component libraries. A backend-agent loads database servers and API docs. Each manages its own context window instead of one agent loading everything.
Model Selection
The default "Auto" agent uses a mix of frontier models (Sonnet 4.5 plus specialized models) with intent detection and caching for cost optimization. Users can also select Claude Sonnet 4.5 directly .
Pricing
| Plan | Price | Credits | Overage |
|---|---|---|---|
| Free | $0/mo | 50 | — |
| Pro | $20/mo | 1,000 | $0.04/credit |
| Pro+ | $40/mo | 2,000 | $0.04/credit |
| Power | $200/mo | 10,000 | $0.04/credit |
Enterprise plans add SAML/SCIM SSO via AWS IAM Identity Center, organizational dashboards, and extension registry governance .
Strengths
- Spec-driven development is genuinely novel — forcing structured planning before code generation addresses a real problem with agent-generated code quality
- Three-layer system is well-designed — specs for planning, hooks for automation, skills for expertise hits different needs without overlap
- AWS backing — enterprise credibility, IAM integration, and long-term viability
- Open standards adoption — uses agentskills.io SKILL.md format rather than proprietary skill system
- Autonomous agent mode — can run large tasks independently without step-by-step prompting
Cautions
- Reliability complaints — Reddit feedback includes reports of bugs, agents editing internal snapshots instead of actual files, and inconsistent behavior
- IDE maturity — as a newer VS Code fork, it lacks the extension ecosystem maturity and polish of Cursor or standard VS Code
- Credit system opacity — complex prompts consume multiple credits unpredictably; hooks consume at least 1 credit each
- Claude dependency — most criticism traces back to underlying model limitations rather than Kiro's tooling, but users experience it as Kiro's problem
Competitive Positioning
| Kiro | Cursor | Windsurf | Claude Code | |
|---|---|---|---|---|
| Type | IDE | IDE | IDE | CLI |
| Spec-driven | ✅ Native | ❌ | ❌ | ❌ |
| Skills (SKILL.md) | ✅ | ✅ | ❌ | ✅ |
| Background hooks | ✅ | ❌ | ❌ | ❌ |
| Custom subagents | ✅ | ❌ | ❌ | ❌ |
| Backing | AWS | Anysphere | Codeium | Anthropic |
| Pricing from | Free | $20/mo | $15/mo | Usage-based |
Kiro's differentiator is the spec-driven workflow + hooks combination. No other IDE enforces structured planning before implementation. The tradeoff is maturity — Cursor has years of polish that Kiro is still catching up to.
The Tembo Angle
Kiro's spec-driven approach validates what orchestration platforms like Tembo have been building toward: agents need structured context and planning phases, not just prompts. Kiro does this within a single IDE; Tembo does it across fleets of agents. The hooks system is particularly interesting — it's essentially lightweight orchestration triggers built into the development loop.
Bottom Line
Recommended for: Teams that want structured AI development with planning phases, especially those already in the AWS ecosystem. The spec-driven workflow genuinely reduces the "agent wrote the wrong thing" problem.
Not recommended for: Developers who want a polished, stable daily driver — Cursor is more mature. Solo developers who prefer speed over structure may find specs overhead.
Outlook: Kiro has the right ideas (specs, hooks, skills, subagents) and the right backing (AWS). The execution needs to catch up. If they can solve the reliability issues, the spec-driven approach could become the standard for production-grade agentic development.