Agile software development often faces challenges related to architectural uncertainty. ArchHypo addresses this by providing a hypothesis-driven architecture technique that helps teams formulate, test, and learn from architectural assumptions iteratively. However, studies have shown that the lack of tooling integrated into everyday development workflows is a significant barrier to its adoption. In this paper, we present ArchHypo.AI, a Trello plugin that integrates hypothesis-driven architectural reasoning directly into agile boards and validates the ArchHypo technique in real projects. The plugin uses an LLM and a RAG mechanism to help teams generate and classify architectural hypotheses, develop technical plans, and link actions to architecture decision patterns. By operating on top of an existing project management tool, ArchHypo.AI aims to lower the adoption cost of hypothesis engineering and to make architectural decision processes more observable. We evaluated ArchHypo.AI in a controlled study with software professionals working on a realistic architecture scenario. The results indicate that the plugin helps structure architectural discussions, reduces manual effort in documenting hypotheses and plans, clarifies procedural steps, and surfaces differences in risk perception within teams. Qualitative feedback suggests that AI-assisted support facilitates collaborative reasoning about architecture. Our findings show that LLM-based tools can effectively support hypothesis-driven architecture in agile settings and highlight design considerations for integrating such tools into existing workflows.