Migrating Legacy Systems with AI: A Modernization Playbook for COBOL and Java

Every enterprise has "The Monolith." A massive, mission-critical application written in 2005 (or 1985) that processes $100M a year. It works, but nobody dares to touch it because the original author retired, and there are no tests.
Traditionally, migrating this meant hiring expensive consultants for 3 years of manual reverse engineering.
AI Agents have accelerated this process by 10x.

Phase 1: Automated Archaeology (Documentation)

The first step is understanding what the heck the code does. Reading 500,000 lines of spaghetti code is mind-numbing for humans. It is trivial for AI.

The Strategy: Use a model with a massive context window (Gemini 1.5 Pro).
The Prompt: "Analyze this entire folder of COBOL scripts. Map the data flow. Generate a Mermaid.js sequence diagram showing how Order_Process interacts with Inventory_Db. Write a plain English summary of the business rules for the 'Discount' logic."

The Result: Instant, queryable documentation. You can now ask: "Where in the codebase is the tax rate hardcoded?" and get line numbers.

Phase 2: Synthetic Test Generation

You cannot refactor functionality you cannot test. Legacy code rarely has unit tests.

The Strategy:

1. Isolate a function (e.g., calculateInterest).
2. Use AI to generate 100 input/output pairs based on reading the code logic.
3. Create a "Golden Master" test suite in the original language.
4. Verify that the old code passes these tests.

Phase 3: The Transpilation (Pattern Matching)

Do not use simple "Translation" tools. They produce "Java code written with COBOL syntax," which is the worst of both worlds.
Use Generative Refactoring.

The Prompt: "Rewrite this Java Spring controller logic as a Python FastAPI route. Do NOT translate line-by-line. Use the Pydantic validation pattern we use in our new microservices."

The AI acts as a senior engineer who understands idioms of both languages.

Phase 4: Validation (The Double-Run)

During the migration window, run both systems in parallel.

• Route 1% of traffic to the new AI-generated system.
• Compare the output JSON against the old system.
• If New_Output == Old_Output, increase traffic.

AI turns the "black box" of legacy code into a glass box. It removes the fear factor, allowing you to pay down technical debt that has been accumulating for decades.