Amazon Quick & Kiro Fiesta #2
Summary Report: “Amazon Quick & Kiro Fiesta #2”
Event Objectives
- Deliver a full-day, hands-on AI workshop hosted by AWS, split into two sessions for two audiences
- Morning – Amazon Quick: show business and finance leaders how an AI-powered executive assistant streamlines decision-making, from morning briefs to strategic analysis, with no technical skills required
- Afternoon – Kiro: show infrastructure teams how Kiro, AWS’s AI-native IDE, brings AI productivity together with enterprise-grade guardrails for Security, DevOps, and FinOps
- Give attendees hands-on time to configure their own Amazon Quick workspace and set up Kiro with AWS Solutions Architect guidance
Hosts
- Morning Session (Amazon Quick): AWS team from Singapore
- Afternoon Session (Kiro): AWS Vietnam team & AWS Solutions Architects
- AWS GenAI Builders Club
Agenda Timeline
Morning Session: Amazon Quick — “A Day in the Life of a CxO”
| Time | Session |
|---|---|
| 9:00 – 9:30 | Morning Brief — AI pulls overnight updates, flags exceptions, prepares your daily snapshot |
| 9:30 – 10:30 | Financial Deep-Dive — Budget vs actual, cash flow forecasting, case studies (Zurich, Jabil $400K savings) |
| 10:30 – 11:15 | Strategic Analysis — Quick Research: complex questions answered in minutes from your own data |
| 11:15 – 12:00 | Hands-On — Configure your own Amazon Quick workspace with AWS SA guidance |
| 12:00 – 1:30 | 🍽️ Lunch & Networking |
Afternoon Session: Kiro — “KiroOps”
| Time | Session |
|---|---|
| 1:30 – 1:45 | Introduction — GenAI for Infrastructure: use cases for DevOps, Security, FinOps, SRE |
| 1:45 – 2:15 | Getting Started — Set up Kiro IDE/CLI, explore Supervised vs Autopilot modes |
| 2:15 – 3:00 | Empowering Your Kiro — Agent Hooks (auto cfn-lint, block destructive commands), Steering (security baselines, tagging), AWS MCP Server |
| 3:00 – 3:15 | ☕ Break |
| 3:15 – 3:45 | Enterprise Governance — SCPs + user-agent markers, cost management, CloudTrail audit |
| 3:45 – 4:15 | Troubleshoot Lab — Diagnose a real infrastructure issue with Kiro (network, IAM, cost) |
| 4:15 – 4:30 | Wrap-up — Next steps, Assisted Trial Program, Q&A |
Key Highlights
Morning – Amazon Quick: “Your 24/7 AI-Powered Executive Assistant”
- The framing problem: executives lose 40+ hours monthly switching between apps, compiling data, and waiting for answers
- Walked through a real CxO’s day to show how Amazon Quick acts as an AI Chief of Staff that already knows your data, people, and priorities
- Morning Brief: the assistant pulls overnight updates, flags exceptions, and prepares a daily snapshot
- Financial Deep-Dive: budget vs actual, cash flow forecasting, with real case studies (Zurich, Jabil’s $400K savings)
- Strategic Analysis: Quick Research answers complex questions in minutes directly from your own data
- Hands-On: attendees configured their own Amazon Quick workspace with AWS SA guidance — no technical skills required
Afternoon – Kiro: “KiroOps for Infrastructure Teams”
- The framing problem: infrastructure teams are adopting AI coding tools and velocity is up, but who ensures the AI follows security baselines, enforces tagging, and controls what it can do in your AWS accounts?
- Getting Started: set up the Kiro IDE/CLI and explored Supervised vs Autopilot modes
- Empowering Your Kiro: Agent Hooks (auto cfn-lint, block destructive commands), Steering (security baselines, tagging policies), and the AWS MCP Server
- Enterprise Governance: SCPs combined with user-agent markers, cost management, and CloudTrail audit
- Troubleshoot Lab: diagnosed a real infrastructure issue with Kiro across network, IAM, and cost
- Wrap-up: next steps, the Assisted Trial Program, and Q&A
Key Takeaways
- AI assistants like Amazon Quick can remove repetitive executive workload — data gathering, reporting, and ad-hoc research — and return hours back to leaders
- Self-service analytics built on your own data shortens the path from question to decision
- For engineering teams, AI productivity must come with guardrails: Agent Hooks, Steering, and governance keep AI-assisted work safe and compliant
- Kiro’s Supervised vs Autopilot modes let teams choose the right level of control for each task
- Enterprise governance — SCPs, user-agent markers, cost controls, and CloudTrail audit — is what makes AI tooling safe to adopt at scale
Applying to Work
- Use Amazon Quick patterns to automate recurring reporting and research instead of manual app-switching
- Adopt Kiro Agent Hooks to auto-run cfn-lint and block destructive commands in my own workflow
- Apply Steering files to encode security baselines and tagging policies so AI output stays consistent
- Connect the AWS MCP Server to give Kiro safe, scoped access to AWS context
- Default to Supervised mode for high-risk infrastructure changes and Autopilot for routine tasks
Who Should Attend
- Morning (Amazon Quick): CxOs, VPs, Finance Directors, CFOs, FP&A Analysts, Controllers, Heads of Operations, and IT Directors seeking self-service analytics
- Afternoon (Kiro): Platform Engineers, DevOps Engineers, Cloud Architects, Security Engineers, Compliance Officers, FinOps Practitioners, SRE / Operations Engineers, and Engineering Managers overseeing infrastructure teams
Event Experience
Attending Amazon Quick & Kiro Fiesta #2 on June 19, 2026 at the AWS Vietnam Office was a packed, full-day experience covering both the business and engineering sides of AI adoption. Key moments included:
A leadership lens in the morning
- The Amazon Quick session reframed AI around an executive’s real day, making the value tangible: less time compiling data, more time deciding.
- The financial case studies (Zurich, Jabil’s $400K savings) showed concrete outcomes rather than abstract promises.
- The hands-on workspace setup made the tool feel approachable even without technical background.
An engineering lens in the afternoon
- The Kiro session spoke directly to the concern of every infrastructure team: how to gain AI velocity without losing control.
- Seeing Agent Hooks auto-run cfn-lint and block destructive commands made the guardrails concept concrete.
- Steering for security baselines and tagging, plus the AWS MCP Server, showed how to keep AI output aligned with policy.
- The Troubleshoot Lab was the highlight — diagnosing a real network/IAM/cost issue with Kiro turned theory into practice.
What I left with
- A clear picture of how AI helps both leaders and engineers, each in their own workflow
- Practical guardrail patterns I can apply with Kiro right away
- Awareness of the Assisted Trial Program for continued learning
Some event photos
Overall, Amazon Quick & Kiro Fiesta #2 connected two audiences in one day — showing leaders how AI returns hours to their schedule, and showing engineers how to adopt AI with enterprise-grade guardrails built in.