Kiro is an agentic AI development platform designed to streamline the software development lifecycle from prototype to production through a paradigm termed "spec-driven development." This methodology aims to bring structure and precision to AI-assisted coding, mitigating the limitations of less structured "vibe coding."

The core methodology of Kiro's spec-driven development unfolds in several stages:

1. Natural Language Prompt to Structured Requirements: Kiro ingests natural language prompts and transforms them into explicit requirements and acceptance criteria, often utilizing EARS (Easy Approach to Requirements Syntax) notation. This process formalizes intent and constraints.
2. Architectural Design Generation: Based on the refined requirements, Kiro analyzes the existing codebase (if applicable) and generates architectural designs, system designs, and recommends technology stacks that align with best practices and the specified needs.
3. Discrete Task Planning: An implementation plan is formulated, breaking down the design into discrete, sequenced tasks, with dependencies explicitly mapped. Optional comprehensive tests can be integrated at this stage.

Kiro leverages advanced AI agents to execute these tasks, operating within an integrated development environment (IDE) based on Code OSS and a command-line interface (CLI). Key features enabling this agentic workflow include:

• Agentic Capabilities: Agents are empowered to perform complex development tasks, such as fixing bugs, accelerating feature iteration, and resolving intricate technical problems across large codebases. They can autonomously execute predefined tasks or respond to specific instructions.
• Context Management: Through "specs" (structured requirements), "steering files" (configurable rules for agent interaction, coding standards, workflows), and intelligent context management, Kiro maintains a deep understanding of the project's state, intent, and codebase. This enables effective implementation of complex features with "fewer shots" (reduced prompt iterations).
• Multimodal Input: Kiro supports multimodal chat, allowing users to provide input beyond text, such as images of UI designs or architectural whiteboarding sessions, to guide the AI's implementation process.
• Agent Hooks: This feature allows developers to delegate tasks to AI agents that trigger automatically on specific events, such as file saves. These agents can then autonomously perform background operations like generating documentation, creating unit tests, or optimizing code performance based on pre-defined prompts.
• Managed Compute Platform (MCP) Integration: Kiro offers native integration with MCPs, enabling connectivity to external resources like documentation, databases, and APIs, including remote access.
• AI Model Selection: Users can choose between specific, reliable models like Claude Sonnet 4.5 or an "Auto" mode. The "Auto" mode dynamically leverages a mix of frontier models (e.g., Sonnet 4.5 and other specialized models) for intent detection and caching, balancing quality, latency, and cost.
• Autopilot Mode: For large or complex tasks, Kiro can operate in an "autopilot mode," autonomously running tasks without requiring continuous step-by-step instructions, while still allowing user oversight and control over script and command execution.
• Intelligent Error Diagnostics: The platform can interpret and diagnose syntax, type, and semantic errors within code, facilitating faster iteration and bug fixing.
• Code Diffing and Review: Kiro provides a clear visualization of code changes as they happen, allowing users to approve, step through, or edit changes with granular control.
• Automated Git Integration: Agents can draft Git commit messages directly from the source control pane.
• Programming Language Support: Kiro is compatible with a wide array of programming languages, including Python, Java, JavaScript, TypeScript, C#, Go, Rust, PHP, Ruby, Kotlin, C, C++, shell scripting, SQL, Scala, JSON, YAML, and HCL.

The platform emphasizes accelerating development cycles, improving code quality, and offering a structured approach to AI-assisted coding, making it suitable for tasks ranging from drafting new modules and tweaking configurations to building entire applications from high-level requirements.