Agentic Environment Engineering for Large Language Models: A Survey of Environment Modeling, Synthesis, Evaluation, and Application
arXiv:2606.12191v1 Announce Type: new Abstract: Environments serve as interactive systems for large language model (LLM) based agents across diverse scenarios and play a crucial role in driving the continual evolution of model capabilities. Despite this importance, existing work lacks a systematic categorization and deep analysis. This paper systematically studies current researches on agentic environments from the perspective of the environment engineering lifecycle, covering their modeling, synthesis, evaluation and application. Specifically, the paper first introduces representative environ
The rapid advancement and deployment of Large Language Models necessitate robust methods for creating and evaluating the environments they operate in to ensure safety and effectiveness.
This research provides a systematic framework for developing and assessing LLM agentic environments, which is crucial for scaling AI agent capabilities and integrating them into complex systems.
The explicit focus on an 'environment engineering lifecycle' for LLMs suggests a more structured, reproducible, and verifiable approach to AI agent development will emerge.
- · AI platform developers
- · Researchers in AI safety
- · Developers of AI agent applications
- · AI development relying on ad-hoc environment design
Improved methodologies for creating and evaluating environments for LLM-based agents will accelerate their development and deployment.
More reliable and robust AI agents will enable automation of increasingly complex tasks across various industries.
The structured engineering of agent environments could lead to new standards and regulatory frameworks for AI system development and deployment.
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