Escape the Monolith: Modernize Legacy Code Without the "Big Bang" Rewrite
Every mature software company has "that" repository. The one written five (or fifteen) years ago. The one with no documentation, brittle dependencies, and variable names that only the original developer—who left three years ago—understood.
We call it the "Monolith Nightmare."
Engineering leaders face a paralyzed choice:
Rewrite it from scratch: A massive, multi-year "Big Bang" project that halts feature development and carries huge risk.
Leave it alone: Continue to pay the "interest" on technical debt, where simple bug fixes take weeks because developers are terrified of breaking hidden dependencies.
Most "AI coding assistants" don't solve this. They are great at autocompleting the next line of code, but they lack the context to understand the entire architecture.
At Evonence, we are changing the equation with the Legacy Code Refactoring Agent.
The Solution: A "Refactoring Factory" That Understands Context
We don't just give your developers a smarter chatbot. We deploy an autonomous agent designed to act as a Senior Architect.
This agent doesn't just write code; it investigates it.
It crawls your entire legacy repository.
It builds a dependency graph of how Module A talks to Database B.
It generates the missing unit tests required to validate that the code works before it tries to change it.
Instead of a developer spending three days trying to understand a spaghetti-code function, they simply ask the agent: "Refactor the authentication module in auth.py to use OAuth2, and generate the test cases to prove it still works."
The agent delivers a pull request, not just a snippet.
Under the Hood: The "Context Caching" Revolution
Why is this use case unique to Google Cloud? The secret sauce is the latest Gemini 3 architecture.
Refactoring requires the AI to "keep" the entire codebase in its head. On other platforms, sending 500,000 lines of code with every prompt is prohibitively expensive and slow.
1. Gemini 3 Flash with Context Caching We utilize the newest Gemini 3 Flash model, which features a massive 1-million-token context window. With Google's Context Caching (now including Implicit Caching), we upload your codebase once into the model's short-term memory. Subsequent queries are processed instantly. You effectively query your entire repo for free after the first load, drastically reducing latency and cost.
2. Vertex AI Code Interpreter We don't let the AI guess. We use the Vertex AI Code Interpreter to sandbox code execution. The agent can write a test, run it, see it fail, fix the code, run it again, and only present the solution to your human developer once it passes.
The Competitive Edge: Transformation vs. Autocomplete
Many engineering teams are already using tools like GitHub Copilot or Amazon CodeWhisperer. These are excellent "Autocomplete" tools—they help you type faster.
Our Refactoring Agent is a "Transformation" tool.
Competitors: "Here is the next five lines of code based on the last ten lines."
Evonence Agent: "I have read all 50 files. Here is a plan to decouple your billing logic from your user logic, including the necessary unit tests."
If you have tried using standard LLMs for this and hit token limits or massive bills, Google’s massive 1M+ token window and Context Caching are the missing keys to making this viable.
The Evonence Approach: 8 Weeks to Modernization
We treat refactoring as a structured pipeline, not a hackathon. Our Legacy Modernization Accelerator typically runs 8–10 weeks:
Ingestion: securely map your private repositories.
Mapping: Generate the "Theory of Operation" docs that should have been written years ago.
Testing: Auto-generate high-coverage unit tests for the legacy code.
Refactoring: Pilot the conversion of key modules (e.g., Python 2 to 3, or Monolith to Microservice).
The Goal: Pay down your technical debt without bankrupting your roadmap.
Stop fearing your own code. Contact Evonence to turn your legacy monolith into a modern asset.
