This work explores large-language-model-based agents for architectural design, focusing on autonomous search for patterns and tactics and reflection on the trade-offs of candidate decisions. It introduces ReArch, a proof of concept that adapts ideas from ReAct and LATS to architecture reasoning, and reports initial case-study results showing how reflective agents can support iterative exploration and assessment of design alternatives.