LLM-Based Multi-Agent Systems for Code Generation: A Multi-Vocal Literature Review

Developing LLM-based Multi-Agent Systems in Software Engineering: A Mixed-Method Experience Report

A comprehensive overview of the existing tools and frameworks for implementing MAS in software engineering and a set of lessons learned and challenges that can help researchers and practitioners to select a suitable MAS framework according to their needs are provided.

A Systematic Survey of LLM-Based Agentic AI Frameworks for Multi-Agent Coordination and Interoperability

The analysis indicates that the promise of LLM-based agents for scalable automation, collaborative reasoning, and complex workflow execution comes with significant challenges in long-horizon reliability, evaluation standardization, communication security, cost-efficient orchestration, governance, and the interpretability of emergent multi-agent behavior.

A Multi-Agent Benchmarking Framework for Evaluating the Performance of Large Language Models in Logic Programming

A configurable multi-agent framework for benchmarking LLMs in Prolog code generation that combines a Code Generator Agent, a deterministic execution layer using SWI-Prolog, and an evaluator based on the LLM-as-a-Judge paradigm that supports model-agnostic experimentation and evaluates outputs across functional correctness, efficiency, structural properties and evaluator reliability.

Multi-Agent Collaborative Code Generator

Developments in artificial intelligence have made it possible for autonomous systems to compose, test, and refine code, but all current methods are based on single-agent designs or weak multi-agent systems with little cooperation and poor validation procedures. Single-agent designs lack internal diversity and poor reasoning, whereas small multi-agent designs are mostly competitive rather than cooperative and lack strong role specialization to facilitate complicated iterative improvement. The work presented here provides a five-agent collaborative architecture to provide continuous and verifiable code optimization by controlled specialization and iterative refinement. The architecture consists of five specialized agents, including the planner agent, the coder agent, the tester agent, the reviewer agent, and the explainer agent. Each agent plays a differentiated role in an integrated process of feedback where code is conceived, developed, tested, examined, and explained in an iterative process until optimality is reached. The improvement process continues until an internally optimal solution is obtained, and the reviewer agent then checks its validity individually and passes it on to the explainer agent for explainability and documentation, else the system goes through the improvement cycle again. This composition unifies independent thinking, responsibility, and transparency in an interdependent environment with lesser bias and more consistency of choice. The five-agent architecture realizes maximum heterogeneous viewpoints and distributed knowledge, producing better-quality, understandable, and verifiably optimal code than current single-agent or smaller multiagent approaches. Experiments show that continuous validation and specialization-based cooperation result in better results in automatically generated code and provides a scalable platform for intelligent, self-adjusting development environments with a 92

Integration of Multi-agent Reinforcement Learning (MARL) and Large Language Models (LLMs) in Agentic AI Systems: A Narrative Review

Multi-Agent LLM Pipeline for Code Writing: An Experimental Study of Writer-Reviewer Architecture

A multi-agent pipeline-based approach for solving competitive programming problems via Large Language Models (LLMs) is developed and the Writer-Reviewer pipeline where the Writer Agent produces Python solutions and the Reviewer Agent gives static natural language feedback is analyzed.