Breaking Down Monoliths Intelligently

Decomposing a monolithic application into microservices is a monumental task, filled with architectural risks and strategic challenges. For decades, this process relied on the painstaking work of senior engineers and architects manually mapping dependencies and business domains. Today, AI, particularly Large Language Models (LLMs), offers a powerful new way to analyze monolithic codebases, identify optimal service boundaries, and chart a data-driven path to a more scalable and flexible architecture.

This guide explains how AI-assisted decomposition works, why it’s becoming a critical strategy for modernization, and how you can use it to break down monoliths more intelligently.

Microservices decomposition is the process of breaking a large, single-unit (monolithic) application into a collection of smaller, independently deployable services. Each service is organized around a specific business capability, has its own database, and communicates with other services over well-defined APIs.

The goal is to move from a tightly coupled system, where a small change can require redeploying the entire application, to a loosely coupled one where services can be developed, deployed, and scaled independently. The hardest part of this process is deciding where to draw the lines—how to define the service boundaries.

AI-assisted decomposition uses algorithms and machine learning models to analyze a monolith’s source code, runtime data, and version control history to recommend logical service boundaries. It transforms a task that was once based on intuition and manual effort into a more scientific, evidence-based process.

Here’s a breakdown of how it works:

• Static Code Analysis: AI tools can parse the entire codebase to identify logical clusters. They analyze function calls, class relationships, and data structure access to find modules that are highly cohesive (work closely together) and loosely coupled with the rest of the code. These clusters are strong candidates for new microservices.
• Dynamic Log Analysis: By analyzing production logs, AI can observe how the application actually behaves. It can trace user requests through the system to understand real-world transaction flows and identify which parts of the code are exercised together, providing a dynamic view of potential service boundaries.
• Database Schema Analysis: AI can examine the database schema to see which tables are frequently accessed together. Groups of tables that are primarily used by a specific set of functions are a strong indicator of a bounded context that could be encapsulated within a microservice.
• Version Control Mining: By analyzing Git history, AI can identify parts of the codebase that are frequently changed together by the same team. This sociotechnical analysis often reveals hidden domain boundaries, as teams tend to be organized around specific business capabilities.

These different analysis techniques help build a comprehensive map of the monolith’s internal structure, revealing the natural “seams” along which the application can be split.

While specialized tools are emerging, you can begin exploring decomposition with general-purpose LLMs today. The key is to provide the right context and ask precise questions.

• Domain Identification: Start broad to find business capabilities.

  “Analyze the file structure and class names in this repository and group them into potential business domains. Suggest a name for each domain, like ‘User Management’ or ‘Inventory Control’.”

• Dependency Mapping: Zoom in on specific modules to understand their coupling.

  “Given the source code for the OrderProcessor class, list all other classes it directly depends on and all database tables it accesses. Categorize dependencies as high or low coupling.”

• API Contract Generation: Once you’ve identified a candidate service, ask the AI to draft its public interface.

  “Based on the public methods in the ProductService class, generate a draft OpenAPI 3.0 specification for a new ‘Product Microservice’. Include endpoints for creating, reading, updating, and deleting products.”

• Migration Roadmap: Use the AI’s analysis to plan the sequence of extraction.

  “Given the identified domains (‘Users’, ‘Products’, ‘Orders’) and their dependencies, propose a migration sequence. Recommend which domain to extract first, explaining why it has the best balance of business value and low risk.”

Adopting an AI-driven approach provides several key advantages over purely manual methods.

• Reduces Human Bias and Error: It provides an objective, data-driven view of the codebase, free from the cognitive biases and incomplete knowledge of individual architects.
• Accelerates the Discovery Phase: AI can perform a comprehensive analysis in hours or days, a task that could take a team of engineers weeks or months.
• Minimizes Migration Risk: By identifying the most loosely coupled parts of the system, AI helps create a migration plan that starts with the lowest-risk changes, allowing teams to deliver value incrementally.
• Uncovers Hidden Dependencies: It can surface non-obvious relationships between different parts of the code that might otherwise be missed, preventing costly architectural mistakes down the line.

This combination of speed, objectivity, and depth allows teams to approach modernization with greater confidence and a higher probability of success.

While powerful, AI is an assistant, not an oracle. Human expertise remains critical.

• It’s a starting point, not a final answer: AI provides recommendations that must be validated and refined by experienced architects who understand the broader business context.
• Code quality is a factor: An AI’s ability to make sense of a codebase depends on its quality. A heavily tangled “ball of mud” monolith will be difficult for both humans and machines to analyze effectively.
• Context is king: The AI doesn’t understand your business strategy, team structure, or long-term goals. This context must be applied by the engineering team to interpret the AI’s suggestions.

Once you begin extracting new services from your monolith, you immediately face a new challenge: securing a distributed system. A monolith often has a single, built-in security model. A microservices architecture requires a robust, centralized solution for authentication and authorization.

This is where Kinde becomes essential. Instead of building a complex and critical authentication service yourself, you can secure each new microservice using Kinde’s powerful, developer-friendly APIs.

When you decompose a monolith, Kinde helps you:

• Centralize User Management: Each new microservice can connect to Kinde as the single source of truth for user identity, ensuring a consistent and secure user experience across your entire application portfolio.
• Implement Token-Based Security: Kinde issues industry-standard JSON Web Tokens (JWTs) that each microservice can independently validate. This is the bedrock of modern microservice security, allowing services to communicate securely without sharing secrets.
• Decouple Authorization from Code: You can define fine-grained permissions (e.g., orders:read, users:delete) within Kinde and assign them to users or roles. Your services simply check for the presence of a permission in the user’s token, removing complex authorization logic from your application code.

By providing a ready-made solution for these critical security concerns, Kinde allows your team to focus on what it does best: building great products and successfully migrating your architecture.

• Integrate Kinde auth into .NET-based APIs