The VAL plan validator and symbolic landmarks now drive an agentic feedback loop to refine planning domains. This approach addresses the failure of standard LLMs to produce deployable, high-quality domains from natural language. Researchers found that minimal symbolic information significantly improves accuracy. Practitioners can use this to automate complex environment modeling.