  Recent progress with LLM-based agents has shown promising results across
various tasks. However, their use in answering questions from knowledge bases
remains largely unexplored. Implementing a KBQA system using traditional
methods is challenging due to the shortage of task-specific training data and
the complexity of creating task-focused model structures. In this paper, we
present Triad, a unified framework that utilizes an LLM-based agent with three
roles for KBQA tasks. The agent is assigned three roles to tackle different
KBQA subtasks: agent as a generalist for mastering various subtasks, as a
decision maker for the selection of candidates, and as an advisor for answering
questions with knowledge. Our KBQA framework is executed in four phases,
involving the collaboration of the agent's multiple roles. We evaluated the
performance of our framework using three benchmark datasets, and the results
show that our framework outperforms state-of-the-art systems on the LC-QuAD and
YAGO-QA benchmarks, yielding F1 scores of 11.8% and 20.7%, respectively.
