Multi-Agent Collaborative Code Generator

Abstract

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