π― The $50 Million Problem
"We have 40 years of ABAP code running our supply chain. The people who wrote it are retired. The documentation doesn't exist. Every consultant wants
$50 million and 3 years to modernize it. And there's no guarantee it won't break everything."
That's what one of the CIOs told me over coffee. He wasn't alone - 25,000 Fortune 500 companies face this exact nightmare. Legacy ABAP code from the
1980s, cryptic and undocumented, haunting their data centers. A $200 billion problem with no good solution.
When I saw the https://kiro.devpost.com with the Resurrection category, it clicked: What if we could literally bring dead code back to life?
Two weeks later, I built Resurrection Platform - an open-source alternative to SAP's proprietary Legacy AI. It transforms 40-year-old ABAP into
modern SAP CAP applications in 3 minutes instead of 3 years.
This is the story of how Kiro AI became my senior engineering partner and helped me build something that would have taken 6 months solo.
π§ The Frankenstein Twist
As I started building, I realized something extraordinary: I was stitching together 50 years of incompatible technologies.
- ABAP from 1983 (mainframe, procedural)
- SAP CAP from 2020 (cloud-native, event-driven)
- Fiori UI5 from 2024 (modern web)
- GPT-4 from 2024 (AI intelligence)
These technologies were never meant to work together. Different paradigms, different runtimes, different eras. That's when I realized this wasn't
just Resurrection - it was Frankenstein too. Building a chimera that somehow... lives.
And the "stitches" holding it together? Model Context Protocol (MCP).
π€ Kiro as Strategic Partner, Not Code Generator
Here's what I learned early: Don't treat Kiro like GitHub Copilot.
My old approach:
Me: "Build a function to parse ABAP code"
Kiro: generates basic parser
Me: "Add error handling"
Kiro: adds try/catch
Me: "Handle edge case X"
... 10 iterations later ...
My new approach:
Me: "I need to parse 40 years of ABAP variations. What should we consider?"
Kiro: "Here are 5 approaches. I recommend pattern-based parsing because:
1. ABAP syntax is inconsistent across versions
2. Strict grammar parsers fail on real-world code
3. Pattern matching is more forgiving
4. You can add patterns incrementally
Should I implement the pattern-based approach?"
Me: "Yes, that's exactly right."
Kiro: generates production-ready parser with 10+ patterns
The difference? I asked for recommendations, not implementations. Kiro became a strategic partner who understood the problem space.
π The 5 Kiro Features - Expert Level
The hackathon required using Kiro's features. Most projects use 1-2. I used all 5 at expert level. Here's how:
1οΈβ£ Specs: The Blueprint
I spent the first 3 days writing comprehensive specifications. Seemed like "wasted" time - I wanted to code!
But then magic happened.
Example - Custom Code Intelligence Spec (500 lines):
# Q&A Service Specification
## Requirements
FR-1: Use RAG (Retrieval Augmented Generation)
FR-2: Context window: 8,000 tokens
FR-3: Include source attribution with file/line numbers
FR-4: Calculate confidence scores (multi-factor)
FR-5: Prevent hallucination through grounding
## API Design
POST /api/intelligence/qa
Request: { question: string, context: {...} }
Response: { answer: string, sources: [...], confidence: number }
## Implementation Details
- Vector DB: Pinecone
- Embedding: text-embedding-ada-002
- LLM: GPT-4, temperature 0.1
- Similarity threshold: 0.7 ...
I gave this spec to Kiro and said: "Implement this."
Kiro's output: 3 complete services (500+ lines), 7 API endpoints, full TypeScript types, comprehensive error handling - production-ready on first
try.
Time: Spec took 2 hours. Implementation took 1 hour. Total: 3 hours.
Without spec: Would have taken 15-20 hours of iterative "try this, no that, fix this, add that..."
ROI: 5-7x productivity gain
2οΈβ£ Steering: The Domain Expert
I created two steering documents totaling 1,000+ lines:
.kiro/steering/sap-domain-knowledge.md - 40 years of SAP expertise
The breakthrough was the "before/after" pattern:
## Pricing Logic Pattern
β DON'T (Naive ABAP translation):
if (customer_type === 'PREMIUM') {
var discount = base_price * 0.15;
}
β
DO (Idiomatic modern code):
const DISCOUNT_RATES = {
PREMIUM: 0.15,
STANDARD: 0.10,
DEFAULT: 0.05
};
const calculateDiscount = (customerType, basePrice) => {
const rate = DISCOUNT_RATES[customerType] || DISCOUNT_RATES.DEFAULT;
return basePrice * rate;
};
Result: Kiro stopped generating literal ABAP translations. It started generating professional, idiomatic SAP CAP code following best practices.
The difference:
- Before steering: Naive translations with magic numbers
- After steering: Clean, maintainable code with constants, error handling, and modern patterns
3οΈβ£ Hooks: The Automated QA
Three hooks that saved my project:
Pre-commit Hook:
#!/bin/bash
npm run type-check || exit 1
npm run lint -- --max-warnings 0 || exit 1
npm test || exit 1
echo "β
All checks passed!"
Impact: Prevented 50+ bad commits during development. Caught errors in 30 seconds locally vs. 5 minutes in CI/CD.
4οΈβ£ MCP: The Frankenstein Achievement
This is where it gets wild.
Most hackathon projects use: 0-1 MCP servers
I used: 3 coordinated MCP servers
The Architecture:
// Server 1: Custom ABAP Analyzer (Python)
const analysis = await mcp.call('abap-analyzer', 'parse_abap', {
code: abapCode
});
// Server 2: Official SAP CAP MCP (@cap-js/mcp-server)
const cdsModels = await mcp.call('sap-cap', 'cap_generate_cds', {
dataModel: analysis.dataModel
});
// Server 3: Official SAP UI5 MCP (@ui5/mcp-server)
const fioriUI = await mcp.call('sap-ui5', 'ui5_get_fiori_template', {
service: cdsModels
});
The challenge: These are 3 different runtimes (Python, Node.js, Node.js), different protocols, different response formats. How do you coordinate
them?
Kiro's solution - the MCP Orchestrator:
class MCPOrchestrator {
async executeWorkflow(abapCode: string): Promise<Resurrection> {
// STEP 1: Parse ABAP (1983 tech)
const analysis = await this.callWithRetry(
'abap-analyzer', 'parse_abap', { code: abapCode }
);
this.emitProgress('analyze', 100);
// STEP 2: Generate backend (2020 tech)
const capBackend = await this.callWithRetry(
'sap-cap', 'cap_generate_cds', { entities: analysis.dataModel }
);
this.emitProgress('generate', 50);
// STEP 3: Generate frontend (2024 tech)
const fioriUI = await this.callWithRetry(
'sap-ui5', 'ui5_get_fiori_template', { service: capBackend }
);
this.emitProgress('generate', 100);
// STEP 4: Validate (ensure correctness)
const validation = await this.callWithRetry(
'abap-analyzer', 'validate_business_logic',
{ original: abapCode, generated: capBackend }
);
return { capBackend, fioriUI, validation };
}
private async callWithRetry(server, tool, params, maxRetries = 3) {
// Caching (50% cost reduction)
// Exponential backoff retry
// Connection recovery
// Error handling across MCP boundaries
}
}
What makes this impressive:
- Bridges 50 years: ABAP (1983) β CAP (2020) β UI5 (2024) - seamlessly
- Production reliability: Retry logic, caching, connection recovery
- Real-time progress: Streaming updates to frontend
- Smart optimization: Kiro added caching I didn't ask for (50% cost savings)
This orchestrator is the "stitches" in the Frankenstein monster. Without it, we'd have 3 separate tools. With it, we have a transformation pipeline.
5οΈβ£ Vibe Coding: The Documentation
I documented everything in KIRO_IN_ACTION_COMPLETE.md (1,400+ lines).
Key conversations:
- Session 1: Kiro audited my project, recommended adding 3rd MCP server
- Session 2: Strategic discussion about SAP Legacy AI β Kiro recognized $200B opportunity
- Session 3: Scope decisions - what to build for hackathon vs. post-launch
The insight: This documentation became proof of expert-level Kiro usage. Judges could see the strategic thinking, the iterations, the breakthroughs.
π The Results
What I built in 2 weeks:
- β 15,000+ lines of TypeScript
- β 3 MCP servers coordinating 15+ AI tools
- β Complete ABAP β SAP CAP transformation pipeline
- β Custom Code Intelligence platform (docs, Q&A, search, graphs)
- β 100% build success, deployed to Vercel
- β 20,000+ words of documentation
Time comparison:
- With Kiro: 2 weeks
- Without Kiro: 6 months (estimated)
- Productivity multiplier: 12x
Cost to demo:
- OpenAI API: ~$15
- Everything else: Free tier
- Total: ~$15 vs. $50M manual modernization
π What I Learned
- Specs Are Worth Their Weight in Gold
I resisted writing specs initially. "That's waterfall thinking!"
Wrong. Specs gave Kiro the context to implement features autonomously. The ROI was 5-7x.
Key insight: Time spent on specs = time saved on iteration Γ 5
- Treat Kiro as a Senior Partner
The best prompt pattern I found:
"Here's the problem: [context]
Here's what we've tried: [history]
Here's what we need: [requirements]
What do you recommend?"
Kiro would analyze, propose multiple options, explain trade-offs, and recommend the best approach. Strategic thinking, not just code generation.
- Documentation IS the Product
For hackathons especially: judges need to understand your genius.
Without documentation, they see code.
With documentation, they see vision, journey, mastery.
I spent 30% of my time on docs. Worth every minute.
- MCP Enables the Impossible
Making 1983 mainframe code work with 2024 cloud infrastructure? Literally impossible without MCP.
MCP isn't just about connecting tools - it's about bridging incompatible worlds.
- Hooks Transform Quality
Before hooks: Manual testing, inconsistent quality, things slip through
After hooks: Automated validation, 100% coverage, sleep better at night
53 errors caught that would have reached production. That's the ROI.
π What's Next
This started as a hackathon project. But the more I built, the more I realized: this is a real business opportunity.
Immediate (30 days):
- Beta launch with 10 pilot customers
- Complete remaining intelligence features
- Production hardening
Short-term (90 days):
- Add third pillar: AI Fit-to-Standard (complete SAP Legacy AI parity)
- AWS Marketplace launch
- First paying customers
Long-term (12+ months):
- Become the open-source standard for SAP modernization
- 10,000+ GitHub stars
- 100 Fortune 500 customers
- $100M ARR
The vision: Just as GitHub democratized version control, Resurrection Platform will democratize legacy modernization.
π Final Thoughts
Two weeks ago, I didn't know about MCP. I'd never written a line of ABAP. I'd never built anything this complex this fast.
Kiro made it possible.
Not by writing all my code (though it wrote a lot). But by being a strategic engineering partner who:
- Challenged my assumptions
- Recommended better approaches
- Implemented features autonomously from specs
- Caught errors I would have missed
- Delivered production-quality code
The future of software development isn't human OR AI. It's human AND AI, working together.
I brought domain knowledge, strategic thinking, and product vision.
Kiro brought implementation speed, pattern recognition, and code generation.
Together, we built something neither could have built alone.
That's the magic.
π Links
- GitHub: https://github.com/vigneshbarani24/kiroween-hackathon
- Full Documentation: See repository README
- Kiro Features Documentation: See .kiro/ directory
π¬ Questions?
Drop a comment below! I'm happy to discuss:
- MCP architecture and orchestration
- Spec-driven development with AI
- SAP/ABAP modernization strategies
- Building with Kiro AI
- Turning hackathon projects into businesses
π Acknowledgments
- Kiro AI - For being an incredible development partner
- Anthropic - For building MCPs and the Kiroween Hackathon
- SAP Community - For 40 years of ABAP knowledge (encoded in steering docs)
Built with π and Kiro AI
If you found this helpful, give it a β€οΈ and follow me for more AI-powered development stories!
Tags
#kiro #ai #hackathon #sap #abap #mcp #opensource #typescript #nextjs #modernization
P.S. - This entire article structure was planned with Kiro. Meta, right? π


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