AWS Kiro: Build AI Applications & Cloud Workflows

Install Kiro, accept the license, and set up a project folder and account with Metabrains or GitHub; review AWS account setup, credits, and model options in a collaborative workspace.

Discover Kiro settings and system settings, including sessions, autopilot, model context, and MCP tools, plus extensions and tab autocomplete for building AI workflows.

Discover how to use the OpenVSX registry to install and manage VS Code extensions—python extension pack, Git Lens, IntelliJ, and Prettier—enable live testing with go live, live server, preview.

Install python 3.14.3, node.js, and npm (for html and react work), then verify versions in the terminal to set up the Kiro environment and manage dependencies via npm and package.json.

Create and manage multiple profiles, including python, react, and frontend environments, with icons, extensions, and snippets. Preview, import, export, and set default profiles to support multi-language production and production-ready workflows.

Learn vibe and spec based building in AWS Kiro, using rapid prompts to generate code, plan requirements, and iterate from a simple calculator to production ready tooling.

Learn prompt engineering by defining intent, requirement, desired output, and a negative prompt to improve response quality, demonstrated with building a ChatGPT clone and structured prompts.

Navigate Kiro plan options, credits, and pay-per-use; compare high-latency and low-latency models, understand 1 million token context windows, and manage code base sign-in workflows and autopilot.

Explore the agentic workflow from prompt to production-ready outcomes by automating AI model selection, API interactions, and response handling within an AWS-based, Excalidraw-guided environment.

Explore debugging and log analysis inside the IDE to inspect execution, manage breakpoints and watchpoints, stream logs in real time, and leverage structured logging and stack traces for rapid troubleshooting.

Take on a one-go project to build AI applications and cloud workflows with AWS Kiro.

Monitor live application performance to detect bottlenecks and maintain reliability, using latency, throughput, memory profiling, cpu usage, error rates, and real-time dashboards to proactively optimize scalable systems.

Explore MCP integrations within AWS Kiro, enabling direct communication with external tools and services through MCP servers and local project context.

Understand how MCPs connect AI agents with external services via a standardized tool interface, using GitHub and file system MCP servers to perform read and write operations securely.

Understand specs as a structured blueprint that defines goals, requirements, inputs, and outputs to guide AI-generated code, documentation, and infrastructure templates, enabling repeatable, collaborative, and automated cloud development.

Create and manage agent hooks in AWS Kiro to automate tasks on IDE events, choosing AI-driven prompts or shell commands to update tests, docs, and formatting.

Master version control concepts with GitHub repositories, commits, branches, and pull requests, and learn how version history and merging enable reliable collaboration and safe reverts.

Master infrastructure versioning with git-based control, remote Terraform state, versioned deployment templates, and immutable infrastructure to ensure reproducibility, rollback, and auditable cloud environments.

Set up a git environment in Windows, install and configure git with GitHub, connect to the remote prj01 repository, and practice cloning and managing branches for version control.

Understand how to set up a local git repository, connect to GitHub, diagnose and fix syntax errors, commit and push changes, and use AWS Kiro to auto-generate a README.

Learn best practices for agent and GitHub access, including secure authentication with PAT or SSH keys, permission levels, branch workflows, pull requests, code reviews, automated tests, and secure version control.

Install a power in AWS Kiro to give the AI agent domain-specific expertise on demand, packaging tools, workflows, MCP integrations, and validation logic.

Contrast the MCP protocol layer with the power orchestration layer: MCP provides access and limits, while powers supply guided workflows and domain knowledge for deployment and security.

Learn how steering in QIRO provides persistent guidance across projects by using workspace and global steering files, including markdown standards, inclusion modes, and example AWS patterns.

Showcase best usage practices for MCP, steering, and powers as a cohesive production workflow, illustrating MCP connections to external tools like Figma and GitHub with secure, context-driven activations.

Map Git branches to deployment environments to enable development, staging, and production workflows. Implement feature branch deployments, staging validations, and pull request environments to maintain velocity with stability.

Master IAM controls and permission management in AWS Kiro, focusing on roles, policies, and least-privilege access. Use profiles, external identities, and access analyzer to enforce minimum access.

Apply Kiro's secret mechanism for sandbox and autonomous agent workflows, encrypt secrets at rest and load them as environment variables, using .env, .env.example, and .gitignore to separate and protect keys.

Explore secure coding practices in cloud projects, including input validation, secrets management with .env, parameterized SQL, least-privilege IAM, secure HTTP with encryption, and safe error handling in AI-driven workflows.

Learn to monitor cloud environments with AWS CloudTrail, tracking API calls, IAM activity, and security events to support compliance, incident response, and audit readiness.

Learn to implement cost governance in cloud environments through budget alerts, policy enforcement, FinOps monitoring, resource tagging, and automated cost reporting to control spend while enabling innovation.

Showcases full stack cloud development by generating a complete front-end and back-end for a customer survey, including server, database, routes, and authentication with role-based access control.

Learn how the Kiro agent runs terminal commands inside a workspace, installs libraries like pandas and matplotlib, and follows a plan with steering, iteration, and safety checks.

Understand the maximum usage limit for Kiro and how credits drive website generation. Compare free and pro plans, enable overages, and estimate credits needed for full-fledged front-end and back-end projects.

Explore how AWS Kiro uses fallbacks to keep workflows running, switching to project context, reduced scope, and safe reasoning to degrade gracefully rather than fail.

Profile and debug performance by combining IDE debugging with AI-assisted investigation, using breakpoints, diagnostics, and code intelligence to identify slow calls and optimize the front-end.

Optimize Kiro for development by configuring prerequisites like Git, Node.js, and Python. Set up frontend profiles, autopilot mode, extensions, and steering to align workspaces and product stacks.

Scale high-end projects by using modular workflows and context partitioning with spec-driven contracts, aligning teams and reducing noise. Enforce governance, standards, automation, and AWS infrastructure to ensure reliable AI applications.

Enable only required extensions per project and per task, scope the context, and use performance-aware extensions to reduce latency and keep AWS Kiro's development workflow fast.

Design and run load tests to simulate real-world traffic against cloud applications, observe performance under concurrent users, assess latency, throughput, and bottlenecks, and verify auto-scaling and resilience before production.

Explore load testing for cloud applications, including concurrent user simulation and stress testing. Measure throughput, latency, and auto-scaling responses to build resilient architectures.