EASy: A Trainable Agentic Framework for Efficient LLM-Based Systems

A recent paper published on arXiv presents EASy, a framework that can be trained to enhance both task execution and computational efficiency within LLM-based agentic systems. Identified by ID 2608.04588v1, this study highlights a deficiency in current systems that mainly prioritize task completion while overlooking execution efficiency, particularly regarding executor abilities and computational expenses. EASy utilizes reinforcement learning to optimize these aspects collectively, providing an LLM-based orchestrator with detailed insights into the capabilities and costs associated with various executors. This allows for context-aware coordination that transcends mere performance-based routing. Additionally, the framework features a milestone-plan-act approach, breaking down complex tasks into simpler steps to boost efficiency. This research holds significance for the expanding domain of AI agents and offers insights for implementing LLM systems in environments with limited resources.