Foundation-model agents in multi-step, open-ended environments frequently suffer from compounding errors, where early mistakes contaminate long-horizon trajectories. While Multi-Agent Debate (MAD) succeeds in deterministic domains with external validators (CITATION), agents in subjective, non-deterministic tasks such as persuasion experience problem drift and sycophantic conformity. Focusing on subjective argumentation, we identify semantic leakage in standard Retrieval-Augmented Generation (RAG) as a reproducible trigger for these failures: standard RAG prioritizes vocabulary overlap over logical structure.

To mitigate this leakage, we introduce Taxonomic Strategy RAG (TS-RAG), a systems intervention that routes strategy retrieval through a discrete categorical bottleneck to decouple argumentative structure from topical content. Zero-shot, cross-domain evaluations on r/ChangeMyView show that TS-RAG improves the transfer of abstract argumentative logic where standard semantic retrieval degrades (+14.5 win-rate points over an unaugmented persuader for Gemini Flash 3.0, p<0.05). In asymmetric deployments that mix lightweight and frontier models, TS-RAG raises the win rate of a lightweight persuader against a stronger opponent (70.5% → 78.5%, p<0.05) and reduces rounds to consensus when the persuader is already dominant. Finally, we introduce trace-level diagnostics via a turn-by-turn Debate State Representation (DSR), and show that strict stance-holding constraints on the opponent are necessary to prevent evaluation collapse caused by default agentic sycophancy.