Overview

Al Harris from Amazon presents Kiro, an agentic IDE that implements spec-driven development to improve AI coding quality and reliability. The core insight is that structured specification development creates more reliable and maintainable AI-generated code than traditional “vibe coding” approaches, using formal requirements, design artifacts, and property-based testing to ensure code correctness.

Key Takeaways

  • Replace ad-hoc prompting with structured workflows - Moving from “vibe coding” to spec-driven development provides guardrails and reproducible processes for AI agents
  • Use EARS format for requirements - Structured natural language (Easy Approach to Requirement Syntax) enables property-based testing and automated verification of code correctness
  • Leverage MCP servers throughout the development cycle - Integrate external data sources during requirements generation, design, and implementation phases to eliminate context gaps and reduce manual research
  • Customize artifacts to match your workflow - Add wireframes, test cases, or domain-specific requirements to specifications since natural language structure allows flexible adaptation without breaking the process
  • Iterate on the process itself, not just the output - Challenge initial assumptions and ask agents to research alternatives rather than accepting the first proposed solution

Topics Covered