Large Language Model (LLM) agents have established a promising paradigm for Knowledge Graph Question Answering (KGQA). However, their practical performance is severely constrained by the pervasive incompleteness of real-world knowledge graphs (KGs). To address this, Incomplete KGQA (IKGQA) has emerged as a vital task, yet existing multi-agent frameworks often suffer from a logic-misalignment effect, where passive collaboration leads to retrieval failure and hallucination propagation when encountering structural gaps. In this paper, we propose IDLC, an Intent-Driven Logical Compensation framework designed for faithful IKGQA. Unlike traditional reactive debate, IDLC formalizes reasoning as a proactive deliberation task within a Dec-POMDP. It introduces an Active KG Explorer that generates explicit Gap Descriptors to translate structural gaps into actionable retrieval goals, alongside a Logical Consistency Verifier that performs bi-directional anchoring to intercept step-wise semantic drift. The entire multi-agent policy is jointly optimized via reinforcement learning with a specialized logic-fidelity reward, aligning agent behaviors toward faithful reasoning paths. Extensive experiments on BRINK and IKG-CWQ/WebQSP benchmarks demonstrate that IDLC outperforms state-of-the-art baselines in both reasoning performance and step-wise logical fidelity. Furthermore, IDLC exhibits robust zero-shot transferability across various incompleteness levels while maintaining computational efficiency, confirming the effectiveness of intent-driven coordination in repairing fractured reasoning chains.