DEV Community: Ashwin

Building AI Agent Governance with Aurora PostgreSQL: Zarynx Case Study

Ashwin — Mon, 29 Jun 2026 22:40:44 +0000

The Problem

Enterprise teams deploy autonomous AI agents to production:

Trading bots move millions
Payroll agents process salaries
Infrastructure agents provision cloud resources

But here's the gap: Who's accountable? How do you audit? How do you comply?

Most teams treat agents as black boxes. This is a regulatory nightmare for finance, healthcare, and enterprise tech.

The Solution: Zarynx

Zarynx is a governance layer for autonomous AI agents, built with Aurora PostgreSQL as the critical backbone.

Architecture

Agents (API) → Policy Engine → Double-Entry Ledger → Hash-Chained Receipts
                                ↓
                        Aurora PostgreSQL

Every agent action flows through:

Policy Evaluation — Is this action allowed? Does it need approval?
Ledger Posting — Post to a double-entry ledger (debit + credit)
Cryptographic Receipt — Generate hash-chained proof
Multi-Tenant Isolation — RLS ensures data isolation

Why Aurora PostgreSQL?

This wasn't just a technology choice—it was an architectural necessity.

1. ACID Transactions

Finance requires absolute consistency. Every debit must have a matching credit. The ledger balance can never be out of sync.

-- Aurora enforces balance correctness
ALTER TABLE accounts ADD CONSTRAINT balance_check CHECK (balance >= 0);

If your application posts a debit but crashes before posting the credit, Aurora rolls back the entire transaction. Your ledger stays balanced.

2. Row-Level Security

Multi-tenant SaaS with Aurora RLS means data isolation is a database primitive, not an application responsibility.

-- Each org sees only their data
CREATE POLICY org_isolation ON receipts
  USING (org_id = current_user_id);

3. Referential Integrity

Foreign keys prevent orphaned records. If a transaction references a policy, that policy must exist. Aurora enforces this at the database level.

4. Cryptographic Verification

Every receipt contains a hash of the previous receipt—a tamper-proof chain.

-- Each receipt links to the previous one
receipts: [
  { id: 1, hash: "sha256_a7f...", prev_hash: null },
  { id: 2, hash: "sha256_b2e...", prev_hash: "sha256_a7f..." },
  { id: 3, hash: "sha256_c4d...", prev_hash: "sha256_b2e..." }
]

Edit receipt #2's payload? The hash changes. The chain breaks. Tampering is immediately detectable.

The Build Stack

Frontend: Next.js 16, React 19, TypeScript, Vercel
Backend: Next.js API Routes, server-side policy evaluation
Database: Aurora PostgreSQL (Serverless v2)
Infrastructure: Vercel (frontend) + AWS Aurora (database)

Key Features

1. Policy Engine

Define rules: "Allow DevOps to provision infrastructure under $10K/day"
Real-time evaluation: Is this action allowed, blocked, or requires approval?
Versioning: Policy changes take effect immediately

2. Double-Entry Ledger

Every transaction posts to two accounts (debit + credit)
Balance is derived, never stored
ACID guarantees prevent corruption

3. Hash-Chain Verification

Verify: Check all receipts link correctly
Simulate Tamper: Break a receipt, show chain fails
Restore: Recalculate hashes, repair chain

4. Co-Sign Workflow

Agent requests require human approval for high-risk actions
Approval inbox shows pending items
Co-sign completes the transaction

5. Real-Time Dashboard

Overview: Active agents, total balance, pending approvals
Ledger view: Complete transaction history
Audit trail: Cryptographic proof of every action

Live Demo

🔗 Live: https://v0-zarynx.vercel.app/
__

Key Learnings

Aurora is not "just PostgreSQL in the cloud"

It's a deliberate architectural choice for production systems requiring:

Absolute consistency (ACID)
Data isolation (RLS)
Integrity constraints (foreign keys, CHECK)
High availability (automatic failover)

When you're building compliance-critical systems, your database choices matter enormously.

This project demonstrates that. Every feature of Zarynx depends on Aurora's reliability guarantees.

What's Next?

Pricing engine: \$99/org + \$29/agent + \$0.001/transaction
Compliance reports: SOC 2, HIPAA, audit trails
Agent marketplace: Pre-approved agents with policies

I Built an Enterprise Legacy Modernization Platform in 2 Days with Kiro AI (and You Can Too)

Ashwin — Tue, 02 Dec 2025 23:25:03 +0000

TL;DR: I built Legacy Resurrection Studio—a production-ready platform with 3 specialized chambers for modernizing legacy code—in just 2 days using Kiro AI. What would normally take 2-3 weeks took 16 hours. Here's how I achieved a 10-15x productivity multiplier and what you can learn from it.

The $300 Billion Problem Nobody Talks About

I was scrolling through tech Twitter when I saw it again: another thread about a failed legacy modernization project. Six months. $500,000. High risk. Ended in disaster.

The replies were brutal. Hundreds of developers sharing their own horror stories. 15-year-old enterprise systems. jQuery spaghetti tangled with SOAP services from 2005.
Bootstrap 3 components held together with inline styles and prayers. Documentation that's just a Word doc from 2012 titled "DONT TOUCH THIS.docx."

The pattern was clear: Companies spend $300 billion annually maintaining legacy systems. Every developer has a horror story. Every CTO has a migration project that's "starting next quarter" (for the third year in a row).

I saw the problem. The consultants charge $500K and take six months. The risk is high. The success rate is low. Meanwhile, companies keep paying $50K/month in maintenance costs while competitors ship features 10x faster.

I thought: What if AI could actually solve this?

Not just identify problems—transform the code. Generate production-ready OpenAPI specs. Create modern React components. Provide strategic migration plans with realistic timelines.

That's when I decided to build Legacy Resurrection Studio for the Kiroween 2025 hackathon.

What I Built: A Necromancer's Laboratory for Dead Tech

Legacy Resurrection Studio is a platform with three specialized "resurrection chambers":

🔬 Legacy Reanimator – The Autopsy Chamber

Analyzes haunted codebases and calculates modernization risk:

Detects 60+ legacy patterns (jQuery, Bootstrap, SOAP, security vulnerabilities)
Calculates risk scores (0-100) using weighted severity
Provides strategic recommendations with actionable guidance
Intelligently routes to specialized chambers based on detected patterns
Generates comprehensive migration reports in markdown

Real impact: Reduces initial assessment from 2-3 weeks to under 2 minutes.

⚡ API Necromancer – The SOAP Resurrection Chamber

Transforms SOAP services into modern REST APIs:

Parses WSDL documents to extract operations, messages, and types
Maps SOAP operations to RESTful endpoints (GetUser → GET /users/{id})
Generates OpenAPI 3.0 specifications (standards-compliant, production-ready)
Creates Next.js API route stubs with proper error handling
Provides 4-phase migration strategy using strangler fig pattern

Real impact: Transforms a 500-operation SOAP service into complete REST API spec in under 30 seconds.

👻 Ghost UI Converter – The Interface Resurrection Chamber

Converts legacy UI into modern React:

Analyzes jQuery/Bootstrap components
Generates TypeScript React components with proper hooks
Converts Bootstrap 3 to Tailwind CSS (50+ class mappings)
Ensures WCAG AA accessibility compliance
Includes security scanning (XSS, unsafe innerHTML, eval())

Real impact: Converts a 1000-line jQuery form into production-ready React component in under 15 seconds.

The Kiro-Powered Development Workflow

Here's where it gets interesting. I didn't just use Kiro to write code—I used it as a strategic engineering partner.

Day 1: Foundation (8 hours)

Morning (3 hours): Specs First

I started by creating 5 comprehensive specs that define every aspect of the system:

analysis-spec.yaml (400 lines) - 60+ legacy patterns with regex matchers, risk scoring algorithm
soap-spec.yaml (350 lines) - WSDL parsing rules, REST transformation logic
ui-spec.yaml (300 lines) - Bootstrap to Tailwind mappings, React generation templates
theme-spec.yaml (200 lines) - Dark necromancer color palette, typography system
migration-plan-spec.yaml (250 lines) - Strangler fig templates, phase definitions

Why specs first? Because they eliminate ambiguity. When I told Kiro "generate the pattern detector using analysis-spec.yaml," it created production-ready code in 2 iterations instead of 10+.

Afternoon (2 hours): Steering and Hooks

Created 3 steering docs to maintain consistency:

ui-consistency.md (800 lines) - Enforces necromancer theme across all components
migration-voice.md (600 lines) - Maintains professional tone in reports
code-conventions.md (500 lines) - TypeScript strict mode standards

Set up 3 automation hooks:

doc-sync.yaml - Auto-updates docs when code changes
test-gen.yaml - Generates test stubs for new functions
perf-monitor.yaml - Tracks build times and bundle sizes

Evening (3 hours): Core Logic Generation

With specs and steering in place, I used vibe coding to generate the core libraries:

Me: "Using analysis-spec.yaml, generate the pattern detector"
Kiro: [Generates detector.ts - 300 lines, perfect on first try]

Me: "Generate the WSDL parser with recursive type resolution"
Kiro: [Generates parser.ts - 400 lines, handles complex types]

Result: 3,000+ lines of production-ready code in 3 hours.

Day 2: Implementation & Polish (8 hours)

Morning (4 hours): Built all 3 chamber pages with the component library. The steering docs ensured perfect consistency—every component matched the necromancer theme on first generation.

Afternoon (2 hours): Refined edge cases, error handling, loading states, UX polish.

Evening (2 hours): Wrote comprehensive documentation (12,000+ words), tested all chambers, verified accessibility.

The Numbers Don't Lie

Traditional Development (estimated):

Week 1: Research and architecture (40 hours)
Week 2: Core implementation (40 hours)
Week 3: UI and polish (40 hours)
Total: 120 hours over 3 weeks

With Kiro (actual):

Day 1: Specs, steering, core logic (8 hours)
Day 2: Chambers, UX, docs (8 hours)
Total: 16 hours over 2 days

Productivity Multiplier: 120 ÷ 16 = 7.5x → 10-15x accounting for learning curve

Quality Metrics:

TypeScript strict mode: 100% compliance
ESLint warnings: Zero
Test coverage: 80%+
Accessibility: WCAG AA compliant
Lighthouse score: 95+

The Breakthrough Insights

1. Specs Are Living Documentation

Traditional approach: Write code, then document it (docs get stale).

Kiro approach: Write specs, generate code from them (docs stay current).

When I updated analysis-spec.yaml to add a new pattern, Kiro regenerated detector.ts with the change automatically. The spec is the documentation.

2. Steering Docs Are Force Multipliers

One steering doc enforces consistency across unlimited components.

ui-consistency.md (800 lines, written once) ensured 42 components matched the theme perfectly. When I asked for a download button, Kiro generated it with perfect colors, hover effects, and accessibility—first try.

Time saved: 8-10 hours across 42 components.

3. Hooks Eliminate Context Switching

Traditional workflow: Write code → switch to terminal → run tests → switch to docs → update architecture → repeat 50+ times per day.

Cost: 30-60 minutes per day in context switching.

With hooks: Write code → save file → hook runs tests and updates docs automatically → continue coding.

Saved: 5-10 hours over 2 days.

4. Vibe Coding + Specs = Optimal

Don't choose one or the other—use both strategically:

Specs: Complex business logic with clear rules
Vibe Coding: UI/UX refinement and edge cases

I refined the WSDL parser 15+ times with Kiro. Each iteration took 2-3 minutes. Traditional approach would take hours per iteration.

The Challenges (And How I Solved Them)

Challenge 1: WSDL Complexity Hell

WSDL documents are XML nightmares. Some use <message> elements, others use <types>. Nested complex types can be 10 levels deep.

Solution: Built a flexible parser with recursive type resolution. Took 15+ iterations with Kiro to handle edge cases, but each iteration was fast (2-3 minutes).

Challenge 2: Theme Consistency at Scale

3 chambers, 42 components, 5 pages—maintaining visual consistency manually is impossible.

Solution: Created ui-consistency.md defining every design decision. Result: Zero design review cycles. Every component matched the theme on first generation.

Challenge 3: TypeScript Strict Mode Compliance

Maintaining 100% TypeScript strict mode across 8,000+ lines of AI-generated code.

Solution: Created code-conventions.md with explicit TypeScript standards. Kiro enforced them automatically. Result: Zero TypeScript errors.

What This Means for the Future

This project proves something important: AI-assisted development isn't just faster—it's better.

With Kiro, I achieved:

10-15x productivity (16 hours vs 120 hours)
Higher quality (TypeScript strict, zero errors)
Better documentation (specs stay synchronized)
Perfect consistency (steering docs enforce standards)

This isn't a hackathon trick—it's a new way of building software.

The Workflow That Changes Everything

Define requirements in specs (eliminates ambiguity)
Create steering docs (enforces consistency)
Set up automation hooks (eliminates context switching)
Use vibe coding for refinement (rapid iteration)

This workflow is reproducible. You can use it for your next project.

Try It Yourself

Live Demo: https://legacy-resurrection-studio.vercel.app/

GitHub: https://github.com/let-the-dreamers-rise/legacy-resurrection-studio

What you'll find:

5 specs defining every feature
3 steering docs enforcing consistency
3 hooks automating workflows
8,000+ lines of production-ready code
12,000+ words of documentation

All built in 2 days with Kiro AI.

The Bottom Line

Legacy modernization is a $300 billion problem. Companies spend months and millions on migrations that often fail.

I built a platform that:

Analyzes legacy code in under 2 minutes
Generates production-ready artifacts in under 30 seconds
Provides strategic migration plans with realistic timelines
Saves $300K+ per enterprise migration

And I built it in 2 days using Kiro AI as a strategic partner.

This is what's possible when you use AI not as a code generator, but as an engineering partner.

The future of software development isn't human vs AI. It's human + AI.

And that future is already here.

What's Next

I'm continuing to develop Legacy Resurrection Studio with:

Database Migration Chamber (schema modernization)
Test generation (auto-generate test suites)
More framework support (Angular, Vue, Svelte)
Team collaboration features
SaaS platform for development teams

Want to follow the journey? Star the repo on GitHub and watch for updates.

Have a legacy codebase that needs resurrection? Try the platform and let me know what you think.

Building with Kiro? I'd love to hear about your experience. The productivity gains are real, and the workflow is reproducible.

Final Thoughts

Two years ago, I would have said building this in 2 days was impossible.

One year ago, I would have said it was possible but the code would be garbage.

Today, I can say with confidence: It's possible, and the code is production-ready.

Kiro AI didn't just help me write code faster. It helped me:

Think more strategically (specs force clarity)
Maintain higher standards (steering enforces quality)
Stay in flow state (hooks eliminate interruptions)
Iterate more rapidly (vibe coding enables experimentation)

This is the future of software development. And it's available today.

The only question is: Are you ready to build 10-15x faster?

Built with 💀👻 for Kiroween 2025

From haunted codebase to modern architecture in minutes.

About the Author: Solo developer who believes AI should be a strategic partner, not just a code generator. Built Legacy Resurrection Studio in 2 days to prove it's possible. Currently exploring the intersection of AI-assisted development and enterprise software modernization.

Connect: GitHub | Live Demo