🌐 The key to building strong AI agents may actually be designing the environments they operate in. This 63-page survey systematizes the view of "environment engineering." Title: Agentic Environment Engineering for Large Language Models: A Survey of Environment Modeling, Synthesis, Evaluation, and Application URL: 📝 Overview LLM agents don't act alone; they operate inside interactive environments. This survey organizes the research landscape through the lens of "environment engineering," the engineering design and construction of those environments themselves. ❓ Challenges Solved Until now, how to build environments was discussed only in fragments. Even though agent capability depends heavily on good environment design, there was no unified framework to organize it. 💡 Methodology & Proposed Approach It classifies environments along the development lifecycle in four pillars. ・Environment modeling: characterizing representative environments and assessing core capabilities ・Environment synthesis: two paradigms, symbolic and neural ・Environment evaluation: domain-specific assessment aligned with the synthesis paradigms ・Environment application: agent-environment co-evolution across four pathways, memory-centric, orchestration-centric, trajectory-centric, and exploration-centric 🎯 Use Cases It helps agent researchers locate their own work on a map and spot missing perspectives, and serves as a starting point when designing environment synthesis, evaluation, and self-evolution. 📊 Trends and Outlook ・It organizes evolution approaches into three families: neural-driven, difficulty-driven, and scaling-driven ・It analyzes across eight attributes and eight application domains ・It points to Environment-as-a-Service, multi-agent systems, and neural-symbolic integration as future directions #AIAgents# #LLM#