A single verify-payment call sounds simple — until it sits on the critical path of billions of payment checkouts. Behind that one call lived six different encryption schemes, hundreds of merchant and payment-gateway overrides, and a decade of implicit business logic buried inside a PHP monolith. Verify v1.0 powers one of the most payment-critical workflows at PayU: determining whether money actually moved.
The challenge was not replacing a failing system. The monolith worked. The challenge was modernizing a business-critical platform into a scalable, observable, and extensible Java-based UPI service — without breaking years of undocumented runtime behavior. It was a payment-grade behavior migration not just a language migration.
We paired Agentic AI tools like Cursor and Claude with engineering discipline and human-in-the-loop validation. This post is how PayU used AI to accelerate the modernization of a real production payment system — not a sandbox experiment.
The Problem Nobody Puts on a Slide Deck
Most migration stories sound clean in presentations: “We migrated from a monolith to microservices.” What rarely gets discussed is the invisible complexity hidden inside legacy systems.
Our legacy repository had years of deeply coupled runtime-driven behavior. At first glance, the execution paths looked straightforward — but in reality, most of the logic was dynamically controlled through injected runtime objects, configurations, feature flags, and gateway / merchant-specific implementations.
As developers new to this ecosystem, we werenʼt just migrating code. We first had to discover how the system actually behaved in production.
The Breakthrough – Understanding the Unknown & Runtime Discovery Before Migration:
Before writing a single line of migration code, we spent time reverse-engineering the execution flow.
We started by analyzing production logs from a few unique merchants to understand how requests traversed through the system. Those logs revealed only part of the picture.
To uncover the hidden runtime paths, we:
• Added deeper observability and targeted instrumentation into the legacy repository
• Deployed the enhanced version into pre-production
• Ran controlled test suites across multiple merchant scenarios
• Traced runtime-driven flows that previously had no visibility
This process helped us identify execution branches that were entirely configuration-driven and impossible to infer statically from the codebase alone.
Only after building this runtime map did we feel confident enough to begin the migration.
Architecture First, AI Second:
Once the legacy flow was understood, the next challenge was designing a modern architecture that could scale with future onboarding and evolving PG integrations or to any evolutions.
We collaborated closely with senior engineers and engineering managers to define a clean and extensible Spring Boot–based UPI service architecture.
The key design principles were:
• Clear orchestration boundaries
• Gateway-specific abstractions
• Reusable utilities and encryption layers
• Strict ownership separation between orchestration and core transaction persistence paths
• Extensibility for future PG integrations without service rewrites
What looked like “one payment flow” from the outside was actually dozens of unique behaviors hidden behind gateway-specific implementations.
• Every payment gateway spoke its own dialect
• Hundreds of merchant parameters and feature flags
• Different request/response contracts
• Different checksum and signature validation rules
• AES, RSA, and custom encryption strategies
• Nested success/failure semantics
• Unique timeout and retry behaviors
The complexity wasnʼt in the API calls. It was in preserving years of edge-case behavior without breaking reconciliation and settlements.
The Agentic Migration Loop – Where AI Became a Force Multiplier:
After finalizing the architecture skeleton, we onboarded both the legacy monolith and the new microservice into a shared workspace and fed the latest runtime logs into our development workflow.
This is where AI dramatically accelerated execution.
Instead of manually porting thousands of lines of logic gateway by gateway, we leveraged AI-assisted development to:
• Understand legacy method-level intent
• Convert language and framework patterns
• Map implementations into the correct architectural layers
• Identify abstractions and orchestration boundaries
• Generate migration scaffolding aligned with the new design
Because the architecture skeleton was already well-defined, the AI systems were intelligent enough to infer where code belonged:
• orchestration layers
• utility modules
• gateway implementations
• abstractions and interfaces
AI was highly effective for structured scaffolding and repetitive transformation, while engineers retained ownership of correctness, edge cases, and production readiness.
What would traditionally take weeks of repetitive engineering effort was reduced to days of focused validation and refinement.
We completed the core migration activity in under a week.
Validation for Payment-Grade Trust and Production Readiness:
The migration itself was only half the challenge.
In payment systems, even a minor behavioral deviation can impact reconciliation, settlements, and downstream financial operations. So our biggest focus became validation.
We designed a bidirectional validation strategy:
• Process the first half of a transaction flow through the legacy system
• Feed the intermediate processed state into the modernized service
• Validate whether the final outcome matched expected production behavior
Then we reversed the flow:
• Process the initial stages in the new system
• Hand over execution into the legacy platform
• Compare outputs and downstream effects
This cross-system execution model gave us confidence that the migrated service preserved legacy behavior while remaining compatible with existing operational flows.
Six PG integrations reached production readiness in about four weeks, with the core migration scaffold completed much earlier and the majority of effort spent on validation.
Outcomes and Lessons:
The hardest part of modernization is rarely rewriting code.
Itʼs discovering the undocumented assumptions accumulated over years of production evolution.
What made this migration successful was not just AI-assisted code generation. It was the combination of:
• Runtime observability
• Architectural clarity
• Controlled validation strategies
• Deep understanding of payment edge cases
AI didnʼt magically solve the migration. But it significantly compressed the time required to understand, transform, and modernize a highly complex legacy ecosystem.
Our discovery: Agentic AI shines when the work is structured, not when itʼs judgment
We stopped treating AI as a magic translator and started treating it as a loop. That consistency didnʼt come from the model. We designed the template; AI filled the slots.
Our team still had to:
• Human-in-the-loop reviews for every AI-generated code path, ensuring protocol-level accuracy before merge.
• Run real transactions and diff logs: Legacy repo vs new microservice, same transaction identifiers.
• Paste production-like failures back into Cursor — “ gateway returned a 400 with an empty parameter set; what did the legacy system do differently?””
• Catch implicit legacy behavior AI invented or skipped (status pre-checks, transaction cancel counts, auth-leg field merges — the kind of logic that never appears in a README)
> Our rule: No generated verify path merged without a human who could explain it to an on-call at 2 a.m.
Prompt quality mattered more than model choice. Vague prompts gave us code snippet that failed parity. Specific prompts — “mirror the legacy verification response validator for status U.”
— gave us mergeable code.
Before vs after: What Improved — Without the Hype :
| Aspect | Without Agentic AI (estimate) | With Agentic AI + strict review |
| Time per PG integration | High — full manual rewrite | Significantly lower |
| Understanding legacy PHP | Months of archaeology | Days with AI-assisted walkthroughs |
| Boilerplate (DTOs, clients, tests) | Manual | Mostly generated, human-edited (Only wherever needed) |
| Risk of missing implicit branches | High | Lower — but only with log-driven iteration |
| Cross-PG consistency | Variable | Standardized gateway template |
Standardized gateway template
Weʼre not claiming “90% less code” or “zero bugs.” Weʼre claiming something more honest: we shipped six PG integrations with a small team, a repeatable pattern, and Verify v1.0 now in Production — without recreating monolithic complexity separately for every gateway.
The work wasnʼt done by engineers who had maintained legacy repo for years. Our squad was a Lead, an ASE, and an intern — none of us had prior exposure to the monolith repo when we started. Agentic AI compressed the legacy ramp; the Lead anchored design, parity, and what we were allowed to ship to production.
Closing thought
The hard part isnʼt syntax, itʼs trust. Trust that a captured UPI transaction in Bengaluru behaves exactly like it did when legacy system owned the thread.
AI proposes; engineers dispose. We used Agentic AI to reduce friction — not to outsource judgment. Our team discovered that the winning combo is a boring modern architecture, exciting iteration speed, and engineers who still read hex dumps when the model says “looks good to me.”
PayU did not simply use Cursor/Claude to port code. PayU designed a payment-grade, agentic migration operating model: runtime discovery, architecture-first modernization, AI-assisted implementation, human review, and parity validation before production.
If youʼre mid-flight on a legacy migration, start with one provider, one stage, one log diff. Let AI draft the RequestProcessor.x. You keep the keys to production.
