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.

Feeding the Beast: Prompt Engineering and Code Analysis

Our process involved a series of sophisticated prompts designed to guide the AI's architectural decisions. We started with high-level directives:

• "Analyze this codebase and identify distinct bounded contexts based on data models and service interactions."
• "Separate the 'Order Processing' logic into a new, independent microservice."
• "Generate a REST API contract for the new 'Inventory Service' based on its current internal methods."

The LLM churned through the code with incredible speed. Within hours, it had produced new project structures, moved thousands of lines of code, and even generated boilerplate Dockerfiles and gRPC stubs. From a purely syntactic perspective, the output was impressive. The code compiled, and the new services started up.

The First Cracks Appear: Syntactically Correct, Semantically Flawed

The initial euphoria quickly faded as we began integration testing. We discovered that while the LLM was a master of code manipulation, it lacked genuine understanding. It could refactor code, but it couldn't reason about the underlying business logic.

A classic example emerged between our newly created OrderService and ProductService. The LLM dutifully separated them but implemented the interaction as a series of synchronous, blocking API calls. To calculate the final price of an order with ten items, the OrderService would make ten separate, sequential network calls to the ProductService.

// Simplified example of LLM-generated code
public class OrderService {
    public Price calculateTotalPrice(Order order) {
        BigDecimal total = BigDecimal.ZERO;
        // PROBLEM: N+1 network calls inside a loop
        for (Item item : order.getItems()) {
            // Each call is a synchronous, blocking HTTP request
            Price itemPrice = productService.getPrice(item.getProductId());
            total = total.add(itemPrice.multiply(item.getQuantity()));
        }
        return total;
    }
}

This pattern, replicated across dozens of service interactions, was a performance disaster waiting to happen. The AI had correctly identified that the services should be separate, but it failed to design a resilient and efficient communication pattern. It simply replicated the old, in-process method calls with high-latency network calls.

Diagnosis: The Birth of a Distributed Monolith

What we had built wasn't a microservices architecture. It was a perfect specimen of a distributed monolith. We had achieved the operational complexity of a distributed system without gaining any of the benefits of loose coupling and autonomy.

The symptoms were textbook:

• Chatty, Synchronous Communication: Services were pathologically codependent. A single user request would trigger a long, brittle chain of calls across multiple services, dramatically increasing latency.
• Shared Database Patterns: The LLM struggled to untangle our data layer. In many cases, it simply pointed multiple new services at the same database tables, recreating the tight data coupling we were trying to escape.
• Lock-Step Deployments: The "independent" services were anything but. A minor change to the ProductService API contract required a coordinated, simultaneous deployment of the OrderService and ShippingService. We had traded a single deployment pipeline for three coupled ones.
• Cascading Failures: The system was incredibly fragile. A failure in a downstream service like InventoryService could now cause a cascading failure that took down the entire checkout process.

The AI refactoring had given us the worst of both worlds: the development friction of a monolith combined with the operational complexity of microservices.

Why AI Refactoring Failed: The Missing Ingredient

The root of the failure was clear: an LLM excels at syntactic transformation but is incapable of semantic understanding. It can restructure code based on patterns it has seen, but it cannot grasp the implicit business rules, historical context, and nuanced trade-offs that inform good architectural decisions.

A successful monolith decomposition relies heavily on principles like Domain-Driven Design (DDD), a collaborative process where developers and domain experts work together to model a complex system. An LLM cannot interview a product manager to understand why the checkout process has a specific, non-obvious business rule. It cannot debate the merits of eventual consistency versus strong consistency for a given feature.

This LLM-driven monolith migration failed because it lacked the one thing that architecture requires most: context.

Our Path Forward: Using LLMs as Co-pilots, Not Pilots

Our failed experiment wasn't a total loss. It taught us a valuable lesson about the proper role of AI in software development. We’ve now adopted a new, more effective strategy that treats the LLM as a powerful co-pilot, not an autonomous pilot.

Our new workflow looks like this:

1. Human-Led Strategy: Senior engineers and architects take the lead. We conduct DDD workshops and manually map out the service boundaries, data ownership, and communication patterns. The high-level architectural decisions remain a deeply human process.
2. AI-Assisted Implementation: Once a boundary is clearly defined, we use the LLM for targeted, well-defined tasks to accelerate development. We ask it to:
   • "Refactor this 300-line god method into smaller, pure functions with corresponding unit tests."
   • "Generate the gRPC client and server boilerplate for the new NotificationService based on this ProtoBuf definition."
   • "Convert this data access object from using JDBC to JPA."

This approach leverages the LLM's strengths—speed, boilerplate reduction, and pattern recognition—while keeping strategic control in the hands of experienced engineers who hold the necessary business and system context.

Conclusion: The Future of AI in Software Architecture

The temptation to have an AI solve our most complex architectural problems is immense. But our experience shows that blindly letting an LLM rewrite your monolith is a direct path to the distributed monolith anti-pattern. These powerful tools lack the critical context, domain knowledge, and reasoning ability required for sound architectural design.

The future of AI in software engineering is not about replacing the architect; it's about augmenting them. By using LLMs as intelligent co-pilots for focused implementation tasks, we can eliminate drudgery and accelerate development without abdicating the strategic thinking that defines great engineering.

What are your experiences with AI-assisted refactoring? Share your stories—and your scars—in the comments below. Let's learn from each other.