AI Agentic Kiro- Empowering Intelligent Agents

What Is AI Agentic Kiro?

I built AI Agentic Kiro to let small, goal-driven programs called “agents”—handle cloud tasks automatically. Think of each agent like a little helper that:

• Understands what you want (for example, “fetch today’s sales data”).

• Does the work (runs code on AWS services).

• Keeps track of what it’s done (logs results and errors).

How It’s Organized
It is organized in three steering files. Before any code runs, Kiro needs clear instructions. I found it easiest to keep three simple files in every project:

• requirements.md
  Lists exactly what the agent should do, in plain English.

• design.md
  Sketches the overall flow—step 1 leads to step 2, and so on.

• tasks.md
  Breaks the work into bite-sized tasks (for example, “download CSV,” “clean data,” “save to S3”).

Keeping these plain-text files first means you stay organized and teammates know exactly what to expect.

The Agent’s Life: Hooks You Can Use
Each agent goes through a simple lifetime in Kiro. At certain moments, you can “hook” in your own code to customize behavior:

• on_init (when the agent starts):
  Good for loading secrets or setting up connections.

• before_task (right before a task runs):
  Let’s you tweak the inputs—for example, add a timestamp or feature-flag.

• after_task(right after completion):
  Perfect for sending logs, metrics, or kicking off another process.

• on_error (if something fails):
  You can send an alert or retry.

• on_shutdown (when all work is done):
  Great for cleanup or sending a summary email.

In my first project, I used before_task to check if a file was large—if so, I sent it through a special processor.

Simple Architecture
Under the hood, here’s what happens:

• You write your steering files.

• An AWS-hosted MCP server holds those files and serves them to agents.

• Kiro’s runtime (an AWS Lambda function) reads them.

• It pulls tasks from an SQS queue.

• Each task runs in Lambda or Step Functions.

• Results and logs go to S3, DynamoDB, and CloudWatch.

That may sound like a lot, but Kiro hides most of it, your job is just to focus on the steering files and any custom hook code.

The MCP Server: Your Control Hub
The Management Control Plane (MCP) server is a lightweight component you deploy once per project. It:

Hosts your steering files (requirements.md, design.md, tasks.md).

Authenticates agents before they fetch updates.

Manages versioning, so you can roll back to a previous workflow design.

I run mine in a small EC2 instance behind an Application Load Balancer. Agents use a short-lived IAM role to call the MCP’s HTTPS endpoint and pull the latest instructions.

Use Cases

• Daily Reports
  Let an agent grab yesterday’s sales numbers and email you a summary every morning.

• Auto-Cleanup
  Agents monitor a folder in S3 and delete old files you no longer need.

• Simple Chatbot
  An agent reads support requests from a queue, checks a FAQ table, and posts answers.

Each of these starts by writing a few lines in requirements.md
no heavy coding to get going.

Tips for Getting Started

• Write clear instructions first
  Spend time on requirements.md, it’s your blueprint.

• Keep hooks tiny
  Focus hooks on one job: logging, alerting, or modifying data.

• Use AWS free tier
  You can experiment with Lambda, S3, and DynamoDB at no cost.

• Test locally
  Kiro comes with a simple emulator so you can run agents on your laptop before you deploy.

Try It Yourself
Visit Kiro website

Click on download button based on the operating system you are using.