MicroAgent: Context-Augmented Multi-Agent Framework for Automatic Microservice Decomposition

· Source: cs.SE updates on arXiv.org · Field: Technology & Digital — Software Development & Engineering, Artificial Intelligence & Machine Learning · Depth: Expert, extended

Summary

MicroAgent is a Context-Augmented Multi-Agent Framework designed for automatic microservice decomposition, addressing challenges in partitioning monolithic applications. It tackles issues like time-consuming manual processes, semantic insight gaps in existing automated methods, and LLM limitations regarding context and design principles. The framework segments decomposition into five distinct subtasks, each handled by a specialized agent. It provides tailored, multi-granularity context to agents and integrates analytical tools to ensure adherence to established design principles. Experimental evaluations on 10 Java Web applications show MicroAgent achieves an 89.2% average decomposition accuracy, outperforming the state-of-the-art method by 24.6%. Furthermore, it achieves a 93.4% F1 score for common class identification and assignment, exceeding the best baseline by 41.1%.

Key takeaway

For software architects and AI engineers tasked with migrating complex Java monoliths to microservices, MicroAgent provides a robust, automated solution. Its multi-agent framework, tailored context, and specialized tools significantly improve decomposition accuracy and common class assignment compared to existing methods. You should explore agentic LLM frameworks to streamline your decomposition process, ensuring higher quality, more practical microservice partitions, and reducing the manual effort typically associated with such migrations.

Key insights

MicroAgent uses a multi-agent LLM framework with tailored context and tools for accurate microservice decomposition.

Principles

Method

MicroAgent divides decomposition into Domain Identification, Domain Clustering, Domain Merging, Common Class Assignment, and Decomposition Refinement, with specialized agents and tools for each stage.

In practice

Topics

Code references

Best for: AI Architect, Research Scientist, AI Engineer, Software Engineer, AI Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.SE updates on arXiv.org.