AI Coding Agent for Enterprise Migration: 60% Faster Legacy Java to Kotlin Conversion

Background

The subject of this case study is a 60-person mid-market fintech providing payment processing APIs for small businesses across North America. The company serves roughly 4,000 merchant accounts and processes several million dollars in daily transaction volume through a single core platform. Engineering is organized into three pods—platform, client SDKs, and fraud/risk—with the platform pod carrying the largest on-call burden.

The core platform is a 180,000-line Java Spring Boot monolith that has been in continuous development for seven years. It has accumulated the usual artifacts of a long-lived codebase: layered annotations, handwritten DTOs, multiple generations of DAO patterns, and a pervasive use of null that makes refactoring hazardous. The Android SDK team needed Kotlin interop for modern client libraries, developer candidates increasingly expected Kotlin in the stack, and the Java codebase carried years of boilerplate that slowed feature velocity. Build times had crept past 12 minutes for a full compile, and the team was shipping roughly 30% fewer features per quarter compared to two years earlier.

Challenge

Three senior engineers scoped the migration and estimated 20 weeks of full-time work for a manual conversion. That estimate assumed best-case conditions: no major refactoring surprises, no production incidents pulling engineers away, and no new feature work during the migration window. Realistically, leadership expected it would stretch to 6–7 months with normal interruptions.

The risk profile made the timeline worse. Every week the migration ran, the team maintained two parallel codebases—Java modules not yet converted and Kotlin modules already migrated. This dual-maintenance increased integration bug risk and forced developers to context-switch between languages constantly. The QA team flagged that their existing test suite covered only 62% of the Java codebase, meaning any automated conversion would need additional test coverage to catch regressions.

The company couldn't freeze feature development for five months. Their largest enterprise client had a contract renewal in four months contingent on API performance improvements that required the Kotlin migration. The team needed to compress the timeline without sacrificing code quality or introducing production risk to a system processing millions of dollars in daily transactions.

Solution

The company deployed an AI coding agent as the primary conversion engine, with three senior engineers shifting from manual conversion to review, architecture decisions, and edge-case handling. The implementation ran through the existing GitHub-based workflow and CI/CD pipeline, keeping all quality gates intact.

The AI agent operated in a structured three-phase process. It first performed a codebase analysis—mapping dependencies, identifying conversion patterns, and flagging modules by complexity. It categorized the 180K lines into three tiers: straightforward conversion (data classes, utility functions, simple services—roughly 60% of the code), moderate complexity (classes with complex inheritance, custom annotations, and framework-specific patterns—30%), and high complexity (transaction handling, concurrency logic, and security layers—10%) requiring human-led conversion with AI assistance.

The agent then converted modules in dependency order, starting with leaf nodes and working inward. For each module, it generated the Kotlin equivalent, created unit tests targeting the converted code, ran the existing Java test suite to verify behavioral parity, and submitted a pull request with detailed conversion notes explaining each non-trivial transformation. GitHub Copilot handled inline suggestions during human review sessions, while SonarQube quality gates ran automatically on every PR to enforce code smell thresholds, duplication limits, and security rules.

The final phase focused on the high-complexity 10%—the transaction processing core and authentication layers. Here the AI agent generated initial conversions, but senior engineers reviewed every line, often rewriting sections to take advantage of Kotlin coroutines and sealed classes for better error handling. The agent's value in this phase was generating comprehensive test coverage: it produced integration tests based on production API call patterns extracted from logging data.

The human-AI workflow was tightly structured. Engineers spent mornings reviewing the previous day's AI-generated PRs (averaging 12–15 PRs daily during peak conversion) and afternoons on the complex modules. A shared Slack channel received automated summaries of each PR's conversion decisions, letting the team spot systemic issues early rather than discovering them in late-stage review.

Implementation timeline

• Weeks 1–2: Codebase analysis, complexity tiering, and CI hardening. The team added mutation tests to sensitive modules to create a behavioral baseline.
• Weeks 2–6: Bulk conversion of the straightforward and moderate-complexity tiers, running at 12–15 PRs per day.
• Weeks 6–8: Hand-supervised conversion of transaction processing, authentication, and concurrency code. Kotlin-idiomatic rewrites replaced direct Java translations where they clearly improved clarity.
• Weeks 8–10: Canary deployment on 5% of production traffic, with automated rollback triggers tied to latency and error-rate SLOs.

Results

Key metrics table

Metric	Manual estimate	AI-assisted actual	Change
Timeline	20 weeks	8 weeks	-60%
First-pass PR approval rate	N/A	94%	—
Test coverage	62%	89%	+27pt
Full-build time	12.1 min	10.3 min	-15%
Production incidents during migration	Unknown	0	—
New unit tests generated	~0 (manual)	1,247	—

Of the AI-generated conversions, 94% passed senior engineer code review on the first submission; the remaining 6% required minor adjustments rather than full rewrites. Idiomatic Kotlin patterns (data classes replacing verbose Java POJOs, extension functions reducing utility class overhead) cut full build times from 12.1 minutes to 10.3 minutes.

The lead engineer summarized the outcome plainly: "We expected the agent to save us time on the boring 60 percent of the work. What we didn't expect was that it would also lift our test coverage by nearly thirty points, which is a capability upgrade we now rely on daily."

Lessons learned

Tier your codebase before you start. The complexity tiering done in week one drove every subsequent decision, including how aggressively humans reviewed and where to concentrate rewriting effort. Without it, the team would have reviewed every PR the same way and drowned in the volume.

Treat AI-generated tests as a first-class deliverable. The 1,247 new unit tests were the most durable artifact of the migration. They caught regressions months after the conversion finished and set a new baseline coverage standard.

Canary everything. Despite high first-pass approval rates, the team refused to cut over without a 5% traffic canary. That discipline let them catch two subtle serialization differences early enough to patch without customer impact.

Takeaway

The migration proved that AI coding agents deliver the most value not on the easiest code or the hardest code, but on the vast middle tier—the 60–70% of a codebase that is tedious but predictable. The agent converted boilerplate at machine speed while humans focused on the architectural decisions that actually required judgment. The test generation capability turned out to be an unexpected long-term win: the 89% coverage threshold became the team's new baseline standard, improving confidence in all future deployments. For teams considering large-scale migrations, the key is structuring the work so the AI handles volume and humans handle risk. For more on AI coding agent capabilities and tools, see AI Coding Agent. To evaluate platforms for your engineering workflow, visit Solutions.