Achieving an 8× Speedup: Building a Flink Monitoring System with Kiro AI IDE

This article introduces how to use Kiro AI IDE to develop an Amazon EMR Flink intelligent monitoring system, focusing on the development practice based on Spec‑driven development and the integration of Strands Agents MCP and Amazon Data Processing MCP .

Spec‑driven development is presented as a structured way to describe requirements. The workflow consists of four steps:

Describe the requirement in natural language → generate requirements.md.

AI understands the requirement and generates a design → generate design.md.

Decompose the design into concrete tasks → generate tasks.md.

Implement each task, generating code automatically.

Steering is Kiro’s knowledge‑management system that injects project‑level rules into the AI context, ensuring generated code complies with the defined specifications.

MCP integration extends AI capabilities:

Strands Agents MCP provides documentation and examples for Strands Agents.

Amazon Data Processing MCP offers operations for Glue, EMR, Athena, etc.

Custom tools and commands can be added.

The chosen technology stack includes:

Backend: Python 3.10+, FastAPI, Strands Agents, boto3.

Frontend: React 18, TypeScript, Ant Design, Vite.

AI: Amazon Bedrock.

The project architecture (see image) connects the FastAPI backend, the multi‑agent system, and the React frontend via Server‑Sent Events for real‑time updates.

Configuration examples for the MCP servers are shown below:

{
  "mcpServers": {
    "strands-agents": {
      "command": "uvx",
      "args": ["strands-mcp-server@latest"],
      "env": {"FASTMCP_LOG_LEVEL": "ERROR"},
      "disabled": false,
      "autoApprove": ["search_docs", "fetch_doc"]
    }
  }
}

Using the Strands Agents MCP, a user can query documentation, e.g., "How to implement Agents as Tools?". The AI responds by generating the appropriate code snippets.

Similarly, the Amazon Data Processing MCP can be used to query EMR cluster information, returning status, instance types, and VPC details.

The core feature development includes:

Multi‑agent system using the "Agents as Tools" pattern.

AI analyzer with graceful fallback to rule‑based analysis.

Streaming output via stream_async() on the backend and EventSource on the frontend.

# Orchestrator registers sub‑agents as tools
self.tools = [
    self.flink_agent.as_tool(),
    self.general_agent.as_tool()
]

# LLM selects appropriate agent
async for event in bedrock_stream(
    model="us.anthropic.claude-haiku-4-5-20251001-v1:0",
    messages=[{"role": "user", "content": message}],
    tools=self.tools):
    yield event

Best practices highlighted are:

Define Steering rules at project start (code comments in Chinese, logs in English, keep proper nouns in English).

Iterative optimization: generate baseline, then add error handling, performance improvements, tests, and documentation across multiple rounds.

The article presents a concrete case study from requirement gathering to production:

Day 1 – Requirement analysis and architecture design (2 h).

Day 2‑3 – Core feature development (5 h) with tasks such as metric collector (30 min), AI analyzer (45 min), multi‑agent system (1 h), FastAPI API (20 min), React frontend (2 h).

Day 4 – Testing and optimization (3 h) including unit tests, end‑to‑end tests, and code review.

Total effort: ~10 hours from requirement to launch, compared with an estimated 60‑80 hours using traditional methods, yielding a 6‑8× efficiency improvement.

The final summary emphasizes the value of AI‑assisted development:

Efficiency : 6‑8× faster development.

Quality : 100 % code conformity, 85 % test coverage.

Learning acceleration : developers acquire new technologies through AI‑generated code.

Decision support : AI helps choose the right technical stack.

Key highlights are Spec‑driven development, MCP integration, Steering rules, and iterative optimization.