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Spec-Driven Development with Kiro
Duration
2 Days
Course
AWS-370
Available Formats
Classroom Training, Online Training
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Course Description
Overview
Spec-Driven Development with Kiro is a two-day, instructor-led training course designed for developers, software engineers, and technical leads adopting AI-assisted, agentic development practices. The course teaches the principles of agentic coding and how to apply them using Kiro, the agentic IDE for software development. Participants learn to plan and implement features through Kiro Feature Specs and Bugfix Specs, control agent behavior with steering files and execution modes, and extend agent capabilities with Model Context Protocol (MCP) servers and Kiro Powers. The course also covers automating workflows with agent hooks, packaging reusable expertise with agent skills and custom subagents, and configuring projects for team collaboration and enterprise governance. Hands on labs are interleaved with module content throughout each day. On Day 1, learners progressively build a weather application using each Kiro capability as it is taught. On Day 2, learners deploy, extend, and team-enable a pre-built micro-blogging application.Objectives
In this course, you learn to do the following:
- spec-driven development workflows using specifications to plan, design, and execute features and fixes
- and control agent behavior using steering files, context providers, execution modes (Autopilot and Supervised), and custom subagents
- agent capabilities using Model Context Protocol (MCP) servers and Kiro Powers to integrate external tools, services, and domain expertise
- and scale development workflows using agent hooks, agent skills, shared steering, and custom subagents
- and share reusable agent capabilities by creating custom Kiro Powers, agent skills, and custom subagents for team-specific workflows
- enterprise governance controls for MCP registry allowlisting, model governance, and extension management
Audience
This course is intended for:
- developers and engineers
- leads adopting AI-assisted development workflows
- teams adopting agentic coding practices
- engineers integrating AI agents into delivery pipelines
Prerequisites
-
We recommend that attendees of this course have:
- in at least one programming language
- knowledge of Git version control
- understanding of RESTful APIs and cloud application concepts
- knowledge of generative AI concepts (LLMs, tokens, inference)
Topics
Day 1
Module 1: Introduction to Agentic Development with Kiro
- agentic coding paradigm and Kiro core capabilities
- principles of effective agentic development
- IDE vs Kiro CLI
- sessions, Spec sessions, and model selection
- three-phase spec workflow (Requirements, Design, Tasks)
- workflow and EARS notation
- workflow (High-Level and Low-Level Design)
- with property-based testing
- files and foundational context
- steering files and inclusion modes (always, fileMatch, manual, auto)
- and global scope
- providers and session management
- and Supervised execution modes
- commands and trusted tools
- to use Bugfix Specs
- bugfix analysis phase: current, expected, and unchanged behavior
- cause analysis and design
- testing for regression prevention
- Context Protocol fundamentals and client-server architecture
- MCP servers at workspace and user scope
- servers, tools, and auto-approve
- MCP tools in chat and troubleshooting
- context overload problem and how Powers solve it
- directory structure (POWER.md, mcp.json, steering)
- and using Powers from the IDE and kiro.dev
- and sharing custom Powers
- hooks: triggers, actions, and creation
- skills: portable instruction packages with progressive disclosure
- subagents: scoped tool permissions and delegation
- vs Steering vs Powers
- workspace configuration through the .kiro directory
- file organization and spec review conventions for teams
- governance: MCP registry allowlisting, model governance, extension registry, web tools
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- Same in-demand topics as instructor-led public and private classes.
- Standalone learning or supplemental reinforcement.
- e-Learning content varies by course and technology.
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