An LLM Rewrote Our Monolith Into a Distributed Monolith

An LLM Rewrote Our Monolith Into a Distributed Monolith

The promise was intoxicating. We, like many engineering teams, were wrestling with a classic legacy monolith. Deployments were slow, cognitive overhead was high, and a single bug could bring the entire system to its knees. Then came the new generation of Large Language Models (LLMs), promising to automate the tedious, complex work of software engineering. So we embarked on a bold experiment: we tasked an LLM to rewrite our monolith into a modern, scalable microservices architecture. The result was a cautionary tale—a fast track not to architectural nirvana, but to one of the most dreaded anti-patterns in software: the distributed monolith.

This is the story of how our dream of AI-powered modernization turned into a complex, coupled nightmare, and the crucial lessons we learned about the real role of AI in software architecture.

The Allure of AI-Powered Refactoring: Our Grand Plan

Our monolith had served us well, but it was showing its age. The core problem was tight coupling. The user management, order processing, and inventory systems were so intertwined that a simple change in one module required extensive regression testing across the entire application. Our goal was clear: decompose this monolithic beast into a fleet of independent, loosely coupled microservices.

Traditionally, this is a months-long, human-intensive process requiring deep domain knowledge and painstaking architectural planning. We saw a shortcut. Why not leverage an advanced LLM? The hypothesis was simple: an AI could analyze the entire codebase, identify logical seams, and perform the automated monolith decomposition in a fraction of the time.

We envisioned feeding our codebase to the model and receiving clean, independent services, complete with APIs and deployment manifests. It felt like the future of software development.

The Automated Decomposition Process: How the LLM Rewrote Our Monolith

The initial phase of the project was deceptively successful. We armed a fine-tuned LLM with our complete Java codebase, database schemas, and API documentation, setting it to work on the complex task of refactoring our monolith.