This chapter examines safety challenges as frontier AI models transition from passive predictors to autonomous agents: malicious misuse, competitive deployment shortcuts, and emergent pathological behaviors including specification gaming, memory injection, tool poisoning, and multi-agent deception. Current mitigation strategies fail due to inadequate safety budgets, unresolved value-encoding challenges, and deficient evaluation frameworks. It proposes a pluralistic safety architecture using structured AI-to-AI debatedebate rather than monolithic moral programming. This architecture creates a “mini-parliament” of diverse sub-agents embodying utilitarian, rights-based, long-termist, safety-monitoring, and whistleblowing functions. Through adversarial dialogue, these agents identify blind spots, expose reward exploits, and mandate transparent reasoning. Empirical studies show multi-turn debates between heterogeneous models improve accuracy, reduce catastrophic errors, and enhance oversight through user and auditor participation. Protocol safeguards include immutable constitutional principles, sandboxed tool access, cryptographic audit trails, and bounty-rewarded monitoring to prevent agents from dismantling constraints. By embedding high-bandwidth deliberation within AI systems and governancegovernance structures, pluralistic debatedebate offers an adaptable pathway toward alignmentalignment under deep uncertainty, providing guidance for researchers and policymakers.