Kiro: AI Coding Assistant

What is Kiro?

Kiro is an AI coding assistant built around spec-driven development and agentic execution. You describe what you want to build, and Kiro converts that prompt into structured requirements, an architecture design, and a sequenced list of tasks. Agents then implement those tasks in parallel, run tests, and verify that the code matches your original intent using property-based testing.

This AI coding assistant is not a plugin or a chat wrapper. It is a full IDE built on Code OSS and developed by a team inside AWS. It runs locally on macOS, Windows, and Linux, and also works via a CLI or web browser.

Features & Benefits

• Spec-Driven Development: convert a natural language prompt into structured requirements, system design, and implementation tasks before any code is written.
• Parallel Agent Execution: run multiple AI agents simultaneously to implement sequenced tasks across a codebase at the same time.
• Property-Based Code Verification: validate that generated code is correct and matches your original intent using deterministic testing tools.
• Event-Driven Agent Hooks: trigger agents to run background tasks automatically on events like file save, covering docs generation, unit test writing, and performance optimization.
• Autopilot Mode: let Kiro execute large, complex tasks end-to-end without requiring step-by-step input from you.
• Agent Focus Mode: manage multiple agents running in local sandboxes from a single command-center interface, then switch back to the full IDE when needed.
• Steering Files: configure AI tool behavior per project or globally by defining coding standards, naming conventions, preferred tools, and context constraints.
• Kiro Powers: attach domain-specific context and tools to agents on demand so they work with expert knowledge for specific task types.
• Session Memory: persist knowledge and context across agent sessions so agents build on previous work.
• Intelligent Error Diagnostics: read and interpret syntax, type, and semantic errors to surface actionable fixes.
• Real-Time Code Diffs: review code changes as they happen, approve all changes at once, step through them individually, or edit inline.
• Git Commit Message Generation: draft commit messages directly from the source control pane using the agent in one click.
• Image-to-Code Input: drop a UI design screenshot or whiteboard photo into the agent to guide implementation.
• Quick Plan Mode: clarify intent and set constraints before building, without running a full spec flow.
• VS Code Compatibility: import existing VS Code settings, themes, and Open VSX plugins during onboarding.
• Multi-Language Support: write and edit code across Python, Java, JavaScript, TypeScript, Go, Rust, C#, PHP, Ruby, Kotlin, C, C++, SQL, Scala, shell scripting, YAML, HCL, and JSON.
• Model Selection: choose Claude Opus 4.8 for advanced reasoning or Auto mode, which routes across frontier models to balance quality, speed, and cost.
• Per-Prompt Credit Tracking: see how many credits each prompt uses in real time.

What can Kiro do?

• Turn a text prompt into a structured engineering spec
• Run parallel agents to build features across a large codebase
• Verify generated code correctness with property-based tests
• Generate unit tests on file save with event-driven agents
• Generate documentation automatically in the background
• Optimize code performance using background agent hooks
• Diagnose and explain syntax, type, and semantic errors
• Generate Git commit messages from the source control panel
• Build UI layouts from a screenshot or whiteboard photo
• Execute large multi-step tasks in autopilot mode
• Persist agent context and knowledge across sessions
• Import VS Code settings, themes, and plugins into the IDE

Real-World Applications

Software engineers working on complex production systems can use Kiro to break large feature requests into structured specs before writing a single line of code. Instead of prompting an AI and hoping the output matches the intent, they define requirements and architecture first. Parallel AI agents then implement the tasks, which cuts down the back-and-forth that typically slows down feature delivery.

Teams building on large, established codebases may find the steering files especially useful. A dev team can encode house rules, naming conventions, and architectural constraints into a project-level steering file. Every agent interaction then follows those rules by default, which reduces the number of review cycles and keeps output consistent across contributors.

Solo developers prototyping new products can use this AI coding assistant to move through the full cycle quickly. Quick plan mode lets them sketch intent before committing to a full spec. Autopilot mode handles the build while they review changes in real time through the diff view. Event-driven hooks keep documentation and unit tests up to date as files change.

QA-focused engineers might use Kiro to close gaps that standard unit tests miss. Property-based code verification checks correctness against the original intent, not just against a fixed set of expected outputs. Combined with automatic test generation on file save, engineers can maintain higher test coverage without writing every test by hand.