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Modernizing Legacy Systems with Amazon Kiro: A Quick Sample

Modernizing Legacy Systems with Amazon Kiro: A Quick Sample

What if modernizing your legacy mainframe system didn't require months of planning and armies of consultants? Here's how I used Amazon Kiro to prove it's possible in just a quick afternoon session.

Randall Potter

🚀 Introduction
Legacy system modernization presents significant challenges for organizations. The process typically involves complex code analysis, architecture redesign, data migration, and deployment infrastructure setup. This blog post demonstrates just a quick sample of how AI-assisted development tools can accelerate this journey while maintaining quality and operational continuity.
This is by no means a comprehensive guide—rather, it's a snapshot showing what's possible when you leverage the right AI development tools for legacy modernization projects.

⚡ The Challenge: Modernizing a C++ Mainframe System

Many organizations operate critical business systems built on legacy technologies. These systems often run on mainframe environments using older programming languages and lack modern deployment capabilities. While these systems function reliably, they present challenges:
  • Limited scalability and deployment options
  • Difficulty integrating with modern systems
  • High maintenance costs and specialized skill requirements
  • Lack of cloud-native deployment capabilities
This post examines how I completed a modernization project that transformed a C++ mainframe order management system into a modern .NET application with Docker containerization and AWS cloud deployment.

🤖 The AI-Assisted Development Approach with Amazon Kiro

Traditional modernization projects follow a waterfall approach with extensive upfront planning. AI-assisted development enables a different methodology. For this project, I used Amazon Kiro, an agentic AI-powered IDE that brings structure to AI coding with spec-driven development. You can learn more about Kiro at kiro.dev .
Kiro offers two distinct development modes: Vibe sessions for quick, conversational interactions and Spec sessions for structured, complex development tasks. For this modernization project, I primarily used Spec mode due to its systematic approach to complex development.

Understanding Kiro's Development Modes

Vibe Mode: Interactive Q&A-focused sessions designed for quick questions, explanations, and building projects through a more conversational approach. This mode is perfect for exploratory coding and simple tasks.
Spec Mode: Guides you through a structured approach to complex development tasks that formalizes the software development process, transforming high-level ideas into detailed implementation plans with systematic execution. This mode is ideal for comprehensive modernization projects like the one I tackled.

Kiro's Spec-Driven Development Workflow

Kiro follows a structured three-phase approach:
  1. Requirements Analysis: Kiro analyzes existing code to understand functionality and creates comprehensive requirements documentation
  2. Architecture Design: Modern architecture patterns are researched and applied, with detailed component design
  3. Implementation Planning: The design is broken down into actionable tasks with clear dependencies
Each phase requires human approval before proceeding, ensuring alignment with business objectives.

Autonomous Implementation with Human Oversight

Kiro handles implementation details while I focused on strategic decisions:
  • Kiro's Responsibilities: Code structure, best practices, testing, documentation, error resolution
  • My Responsibilities: Business requirements, architectural approval, integration validation

🏗️ Project Implementation: From Legacy to Cloud-Native

📋 Phase 1: Core Modernization

My initial interactions with Amazon Kiro were straightforward.
💬 Here's a sampling of the conversation...
👤 Me:
Create a modern .NET C# version of the legacy order management system
🤖 Amazon Kiro:
I'll help you modernize the legacy C++ order management system into a modern .NET application.
Let me first analyze the existing codebase to understand the current functionality, then create a comprehensive modernization plan.
[Analyzed mainframe/src/main.cpp and related files]
I'll create a spec to guide this modernization project. This will ensure we maintain all existing functionality while adding modern capabilities.
🤖 Kiro Generated Spec Document Snippet:
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# Generated Spec Document
name: "Legacy Order Management System Modernization"
description: "Convert C++ mainframe order management to modern .NET application"

requirements:
functional:
- name: "Order Management"
description: "Create, read, update, and delete orders"
acceptance_criteria:
- "Users can create new orders with customer, product, quantity, price"
- "System validates order data before saving"
- "Orders can be retrieved by ID or listed"

- name: "Data Persistence"
description: "Store orders in database with ACID compliance"
acceptance_criteria:
- "Orders persist between application restarts"
- "Database operations are transactional"
👤 Me:
This looks good, but I also need data export capabilities to migrate from the old system
🤖 Amazon Kiro:
Great point! Data migration is crucial for legacy modernization. I'll expand the spec to include comprehensive data interoperability features.
Let me add:
- Export utilities for the legacy C++ system (JSON, CSV, SQL formats)
- Import capabilities in the .NET application
- Data validation and error reporting
- Shared test data for cross-system validation
🧠 Throughout the process, Kiro continued to help me resolve technical challenges:
👤 Me:
The Docker build is failing with dependency issues
🤖 Amazon Kiro:
Let me check the Dockerfile and fix the dependency resolution...
[Fixed multi-stage build with proper .NET SDK and runtime images]
The issue was in the multi-stage build - I've updated it to use the correct base images and copy patterns. The build should work now.
👤 Me:
Can you add monitoring and logging to the CloudFormation template?
🤖 Amazon Kiro:
Absolutely! I'll add CloudWatch log groups, monitoring dashboards, and alerting... [Added comprehensive CloudWatch configuration]
I've added:
- CloudWatch log groups for container logs
- Custom metrics for application performance
- Alarms for error rates and resource utilization
- Cost monitoring and optimization recommendations
👤 Me:
The data import is working great! Can you add validation reporting?
🤖 Amazon Kiro:
I'll add comprehensive validation with detailed error reporting...
[Implemented validation framework with structured error messages]
Added validation features:
- Pre-import data validation with detailed error reports
- Field-level validation messages
- Summary statistics for successful/failed imports
- Rollback capability for failed imports

📇 Code Samples

Example Code Before and After
C++ Legacy Code:
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// Legacy C++ order management
class Order {
private:
int orderId;
char customerName[100];
char productName[100];
int quantity;
double price;
char status[20];

public:
void createOrder(int id, const char* customer, const char* product,
int qty, double prc) {
orderId = id;
strcpy(customerName, customer);
strcpy(productName, product);
quantity = qty;
price = prc;
strcpy(status, "Pending");
}

void processOrder() {
// Basic validation
if (quantity <= 0 || price <= 0) {
strcpy(status, "Invalid");
return;
}
strcpy(status, "Processed");
}
};
Kiro-Generated .NET C# Code:
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// Modern .NET with Clean Architecture
public class Order
{
public int Id { get; private set; }
public string CustomerName { get; private set; }
public string ProductName { get; private set; }
public int Quantity { get; private set; }
public decimal Price { get; private set; }
public OrderStatus Status { get; private set; }
public DateTime CreatedAt { get; private set; }

public static Order Create(string customerName, string productName,
int quantity, decimal price)
{
if (string.IsNullOrWhiteSpace(customerName))
throw new ArgumentException("Customer name is required");

if (quantity <= 0)
throw new ArgumentException("Quantity must be positive");

if (price <= 0)
throw new ArgumentException("Price must be positive");

return new Order
{
CustomerName = customerName,
ProductName = productName,
Quantity = quantity,
Price = price,
Status = OrderStatus.Pending,
CreatedAt = DateTime.UtcNow
};
}

public void Process()
{
if (Status != OrderStatus.Pending)
throw new InvalidOperationException("Order is not in pending status");

Status = OrderStatus.Processed;
}
}
Dockerfile Generation:
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# Use the official .NET 5 runtime as base image
FROM mcr.microsoft.com/dotnet/runtime:5.0 AS base
WORKDIR /app

# Use the .NET 5 SDK for building
FROM mcr.microsoft.com/dotnet/sdk:5.0 AS build
WORKDIR /src

# Copy project file and restore dependencies
COPY ["OrderManagement.csproj", "./"]
RUN dotnet restore "OrderManagement.csproj"

# Copy source code and build
COPY . .
RUN dotnet build "OrderManagement.csproj" -c Release -o /app/build

# Publish the application
FROM build AS publish
RUN dotnet publish "OrderManagement.csproj" -c Release -o /app/publish

# Final stage - runtime image
FROM base AS final
WORKDIR /app

Results from Phase 1

Kiro successfully:
  • Analyzed the existing C++ codebase to understand business logic
  • Created a comprehensive requirements document with user stories and acceptance criteria
  • Designed a Clean Architecture solution using modern .NET patterns
  • Implemented the complete application with Entity Framework Core and SQLite
Result: A functionally equivalent .NET application with improved maintainability and modern architecture patterns.

📋 Phase 2: Data Interoperability

Legacy modernization requires seamless data migration. The project expanded to include comprehensive data exchange capabilities:
  • Export Utilities: Enhanced the mainframe system with JSON, CSV, and SQL export capabilities
  • Import Framework: Built comprehensive data import/export services in the .NET application
  • Shared Test Data: Created cross-system test data frameworks for validation
  • Data Validation: Implemented pre-import validation with detailed error reporting
Result: Bidirectional data exchange enabling gradual migration and parallel operation.

📋 Phase 3: Production Deployment

Modern applications require cloud-native deployment capabilities. The final phase added:
  • Docker Containerization: Multi-stage Dockerfile with optimized image size and persistent storage
  • AWS Infrastructure: CloudFormation templates for ECS Fargate deployment with auto-scaling
  • Operational Excellence: Comprehensive logging, monitoring, and automated deployment scripts
Result: Production-ready deployment with enterprise-grade scalability and monitoring.

☁️ Technical Architecture and AWS Services

The modernized solution leverages several AWS services for production deployment and Amazon Kiro generated the AWS CloudFormation YAML easily.
AWS CloudFormation Example
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# ECR Repository with Lifecycle Management
ECRRepository:
Type: AWS::ECR::Repository
Properties:
ImageScanningConfiguration:
ScanOnPush: true
LifecyclePolicy:
LifecyclePolicyText: |
{
"rules": [{
"rulePriority": 1,
"description": "Keep last 10 production images",
"selection": {
"tagStatus": "tagged",
"countType": "imageCountMoreThan",
"countNumber": 10
},
"action": {"type": "expire"}
}]
}

Monitoring with Amazon CloudWatch

  • Container logs with structured output
  • Performance metrics and alerting
  • Cost monitoring and optimization
  • Operational dashboards

📊 Results and Benefits

The AI-assisted modernization delivered significant improvements:

Technical Benefits

  • Architecture: Clean Architecture with proper separation of concerns
  • Scalability: Container orchestration with auto-scaling capabilities
  • Reliability: Comprehensive error handling and logging
  • Maintainability: Modern .NET patterns with dependency injection

Operational Benefits

  • Deployment: Infrastructure as Code with automated deployment scripts
  • Monitoring: CloudWatch integration for operational visibility
  • Security: VPC isolation, encryption at rest and in transit
  • Cost Optimization: Right-sized resources with pay-per-use pricing

Business Benefits

  • Risk Mitigation: Parallel operation during migration reduces business risk
  • Data Portability: Seamless data exchange enables gradual transition
  • Future Readiness: Modern platform supports future enhancements
  • Reduced Complexity: Automated deployment and monitoring reduce operational overhead]

🎯 Empowering Teams with Mainframe Monoliths

The approach demonstrated in this sample project has profound implications for organizations struggling with legacy mainframe systems. The biggest barrier to modernization isn't technical complexity—it's the overwhelming effort required to even begin. Teams often feel paralyzed by the scope of modernization projects, unsure where to start or how to break down monolithic systems into manageable pieces.
AI-assisted development tools like Kiro fundamentally change this dynamic by lowering the effort threshold and providing teams with:
A Clear Starting Point: Instead of staring at decades-old code wondering where to begin, Kiro analyzes existing systems and automatically generates comprehensive requirements documentation. This gives teams immediate clarity on what their legacy system actually does and what needs to be preserved.
Structured Problem Breakdown: Legacy monoliths feel impossible to tackle because they're viewed as single, massive entities. Kiro's spec-driven approach automatically decomposes complex systems into discrete, manageable tasks with clear dependencies. What once seemed like an insurmountable project becomes a series of achievable milestones.
Reduced Risk Implementation Process: Traditional modernization is an all-or-nothing proposition that terrifies stakeholders. This approach enables parallel operation and gradual migration, allowing teams to validate each component before fully committing to the new system.
Built-in Knowledge Transfer: When mainframe experts retire or leave, their institutional knowledge disappears. Kiro's documentation-first approach captures not just what the system does, but why it was built that way, creating lasting organizational memory.
By providing these capabilities, AI-assisted development doesn't just make modernization faster—it makes it approachable. Teams that have postponed modernization for years due to perceived complexity can now envision a clear path forward, complete with automated assistance for the most challenging technical aspects.

🎯 Key Success Factors

Several factors contributed to this project's success:

Structured Workflow

The spec-driven development approach provided clear phases with approval gates, ensuring alignment between technical implementation and business requirements.

Iterative Enhancement

Starting with basic modernization and iteratively adding capabilities allowed for natural scope evolution while maintaining a working system at each stage.

Comprehensive Testing

Cross-system data compatibility testing and shared test frameworks ensured functional parity between legacy and modern systems.

Production-First Thinking

Including deployment and operational considerations from the beginning resulted in a production-ready solution rather than a proof of concept.

⏱️ Implementation Timeline

I accomplished the complete modernization in phases:
  • Phase 1 (10 minutes): Core .NET application with functional parity (It was a small codebase.)
  • Phase 2 (15 minutes): Data interoperability and migration utilities
  • Phase 3 (5 minutes): Docker containerization and AWS deployment
Even with a small codebase, this accelerated timeline demonstrates the efficiency gains possible with AI-assisted development while maintaining comprehensive documentation and testing.

💡 Lessons Learned

Effective AI Collaboration

  • Be Specific: Clear, contextual requests produce better results than vague instructions
  • Embrace Iteration: Allow scope to evolve naturally based on results and learning
  • Trust Autonomous Capabilities: Let AI handle implementation details while focusing on strategic decisions
  • Maintain Documentation: Comprehensive documentation throughout the process supports long-term maintenance

Technical Considerations

  • Plan for Data Migration: Include data interoperability requirements from the beginning
  • Consider Operations Early: Deployment and monitoring requirements should influence architecture decisions
  • Implement Comprehensive Testing: Cross-system compatibility testing prevents integration issues
  • Use Infrastructure as Code: CloudFormation templates enable reproducible deployments

🎉 Conclusion

This modernization project demonstrates how AI-assisted development can accelerate legacy system transformation while maintaining quality and operational continuity. The combination of structured workflows, autonomous implementation capabilities, and modern cloud deployment patterns enables organizations to modernize critical systems efficiently.
The key outcomes include:
  • Complete functional parity between legacy and modern systems
  • Comprehensive data interoperability enabling gradual migration
  • Production-ready deployment with enterprise-grade scalability and monitoring
  • Significant reduction in development time while maintaining quality standards
AI-assisted development represents a paradigm shift in how organizations approach complex modernization projects. By leveraging AI for implementation details while maintaining human oversight for strategic decisions, teams can achieve remarkable results in significantly less time than traditional approaches.
This is just a quick sample of what's possible with the right tools and approach. The real power lies in making previously daunting modernization projects not just achievable, but approachable for teams that have been putting off these critical upgrades.
Thank you!
-Randall
Further reading...
Amazon Kiro
AWS Transform:
  • https://aws.amazon.com/transform/
  • https://aws.amazon.com/blogs/migration-and-modernization/aws-transform-generally-available/
  • https://aws.amazon.com/blogs/aws/accelerate-the-modernization-of-mainframe-and-vmware-workloads-with-aws-transform/
# kiro# aws-mainframe-modernization# programming# agentic-ide# agentic-ai
Any opinions in this article are those of the individual author and may not reflect the opinions of AWS.
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