AdaCoder introduces an adaptive planning framework that dynamically coordinates multiple AI agents to generate high-quality function-level code from natural language descriptions.

• Employs dynamic planning strategies that adapt based on task complexity
• Leverages multi-agent collaboration between specialized LLM-based agents for planning, coding, testing, and debugging
• Demonstrates improved performance over existing code generation frameworks
• Enhances software development productivity through intelligent automation

This research represents a significant advancement for engineering teams by reducing development time, improving code quality, and enabling more efficient allocation of human engineering resources.

AdaCoder: An Adaptive Planning and Multi-Agent Framework for Function-Level Code Generation