Next-Gen Self-Evolving AI Agents (ATDP)

AI Agents That Rewrite Themselves, without any humans. Self-evolving agents require an enterprise-grade agentic data proxy that captures ATDP trajectories from real production workloads across models, tools, memory systems, retrieval systems, and human-feedback channels. Self-evolving agents require a unified agent evolution control plane that decides when, where, and how agent behavior should change. Self-evolution should not be equated with blindly updating model weights. A deployed agent is a composite policy consisting of a base LLM, an in-context harness, memory, tools, and guardrails; different failures require different intervention surfaces. all rights w/ authors: Next-Generation Agentic Reinforcement Learning Systems Enable Self-Evolving Agents Ran Yan1,2, Wei Fu1,3, Jiale Li1, Shusheng Xu1, Zhiyu Mei1, Jiaxuan Gao1,3, Jiarui Zhang1,2,3, Wentai Zhang1, Hao Dai1, Xujie Shen1, Chuyi He1, Zhen Pu1, Jun Mei1, Zhiyao Lin1, Haitao Wang1, Zhiqiang Ding1, Jiawei Zhang1, Huaijie Wang1,3, Ruida Xu1, Honghua Dong1, Youhe Jiang2, Yi Wu3, Tongkai Yang1, Binhang Yuan1,2 from 1 Ant Group, 2 HKUST, 3 Tsinghua University #ai #futuretechnology #aitechnology #aiexplained #aiagents