问HN：我们是否在强迫大型语言模型成为状态机？
我正在构建一个客户服务平台，但遇到了很大的挫折。“AI代理”的演示和教程总是看起来很华丽，但将混乱的、非结构化的用户意图与严格的、事务性的内部流程结合起来的现实却充满了棘手的边缘案例。
我感觉我花费80%的工程精力在建立防护措施，以防止幻觉或灾难性的逻辑失败，真正用于推出功能的时间却只有20%。
我想问问那些在实际项目中有经验的人（请只分享生产级的经验）：
你们是否发现了在严格的业务确定性与大型语言模型的概率特性之间的真正“甜蜜点”？
还是说我们只是在强行将一个随机的令牌预测器逼迫成有限状态机，而实际上这只是对关键任务工作流程的不可持续的炒作？
我希望听到的是实战故事和现实检验，而不是理论上的推介。
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I'm building a customer service platform and I've hit a wall of frustration. The "AI Agent" demos and tutorials are always sleek, but the reality of bridging messy, unstructured user intent with rigid, transactional internal processes has been a nightmare of edge cases.<p>It feels like I spend 80% of my engineering effort building guardrails to prevent hallucinations or catastrophic logic failures, and only 20% actually shipping features.<p>My question for those with actual skin in the game (production-grade only, please):<p>Have you found a legitimate architectural "sweet spot" between strict business determinism and the probabilistic nature of LLMs?<p>Or are we just trying to shoehorn a stochastic token predictor into acting like a Finite State Machine, and deep down, this is just unsustainable hype for mission-critical workflows?<p>I’m looking for war stories and reality checks, not theoretical pitches.