Legacy Service Modernization with AI

The Challenge

Legacy services often hold critical business logic but have high coupling, sparse tests, and outdated patterns. Teams avoid making changes because small edits can trigger regressions in distant modules. Full rewrites are frequently proposed and then delayed because migration risk is too high.

Primary issues:

• Low confidence in current behavior because characterization tests are missing.
• Tight coupling between transport, domain logic, and persistence.
• Slow onboarding because system knowledge is concentrated in a few engineers.

This use case outlines a modernization path that keeps feature delivery moving.

Suggested Workflow

Use an AI-guided phased modernization model:

1. Map architecture and dependency hotspots.
2. Generate characterization tests before structural refactors.
3. Split large modules incrementally behind stable interfaces.
4. Run dual-path validation on critical flows where possible.
5. Remove old paths only after stability windows with measured parity.

AI should assist analysis and scaffolding; human engineers should approve all boundary and migration decisions.

Implementation Blueprint

Phase plan:

Phase 1: Baseline and safety rails
- inventory modules and dependency graph
- generate characterization tests for critical behaviors

Phase 2: Boundary extraction
- isolate domain services from transport/persistence concerns
- introduce adapter interfaces

Phase 3: Controlled migration
- route selected paths to new modules behind feature flags
- monitor parity and performance

Prompt used for each module:

Analyze this legacy module and propose:
1) current responsibilities
2) highest-risk couplings
3) minimal boundary extraction plan
4) characterization tests required before refactor
5) rollback plan for each change step

Operational safeguards:

• No refactor is merged without tests for touched behavior.
• Every phase has exit criteria and rollback instructions.
• Local model runs (Ollama) are used for sensitive code.
• Weekly architecture review checks AI proposals against domain constraints.

Potential Results & Impact

Likely outcomes when executed with discipline:

• Lower change-failure rate in refactored areas.
• Faster onboarding due to clearer boundaries and better docs.
• Fewer incidents in modules with characterization tests.
• Continuous feature delivery during modernization.

Track outcomes using change-failure rate, onboarding time, incident count in migrated modules, and feature throughput during migration.

Risks & Guardrails

Common risks:

• Large “improve everything” prompts that produce vague plans.
• Refactors without baseline telemetry.
• Migration steps without rollback clarity.

Guardrails:

• Keep prompt scope to a single module or boundary.
• Establish baseline performance and error telemetry before first code move.
• Record architecture decisions and explicit rollback triggers per phase.

Modernization succeeds when risk is managed continuously, not only at release time.

Tools & Models Referenced

• Claude Code (claude-code): Repository-scale refactor assistance and impact mapping.
• Cursor (cursor): Fast implementation loops for extraction and cleanup.
• Ollama (ollama): Local-first analysis for sensitive legacy code sections.
• GPT-5 Codex (gpt-5-codex): Refactor planning and targeted transformation support.
• Claude Opus 4.6 (claude-opus-4-6): Architecture-level critique and edge-case coverage.
• DeepSeek Reasoner (deepseek-reasoner): Optional extra reasoning pass for tradeoff analysis.