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May 18, 2025 06:53 AM
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reddit上的一个热门的帖子,我还是挺认同的:
以下是2025年AI智能体领域真正在发生却无人讨论的事实:
1. 集群模式正在胜出 真正取得成果的客户不是在构建单一"超级智能体",而是创建协同工作的专业智能体系统。想象不同任务的专业智能体相互通信:一个处理客户数据,一个负责日程安排,一个处理创意任务——协同工作。
2. "引擎盖下"的革命 最有价值的AI智能体不是花哨的客户交互界面。优化后端运营的供应商侧智能体正在创造真正的投资回报率。这些系统将运营成本削减高达40%。如果只关注表面,就会错过真正的价值所在。
3. 人类监督不会消失 尽管宣传甚嚣尘上,成功落地的系统仍需要人类参与。真正获益的企业没有完全自动化,而是在增强团队能力。
4. 多智能体系统>单一智能体 未来属于协同合作的智能体系统,而非"全能型"单一智能体。
5. 主动型>反应型 获得最佳效果的客户正从"问答式"智能体转向主动监控业务事件并采取行动的系统。到2025年底,AI智能体将能针对供应链中断等事件"自动准备决策流程"。
并非要否定AI智能体的潜力,只是需要保持清醒。构建真正有用的智能体系统是艰难而复杂的工作,需要真正理解要解决的问题。
Here's what's ACTUALLY happening with AI agents in 2025 that nobody is talking about:
The constellation approach is winning The clients getting real results aren't building one "super agent" - they're creating systems of specialized agents that work together. Think specialized agents for different tasks that communicate with each other. One handles customer data, another does scheduling, another handles creative tasks - working TOGETHER.
The "under the hood" revolution The most valuable AI agents aren't the flashy customer-facing ones. Provider-side agents that optimize backend operations are delivering the real ROI. These things are cutting operational costs by up to 40%. If your focusing only on the visible stuff, your missing where the real value is.
Human oversight isn't going away Despite what the hype says, successful implementations still have humans in the loop. The companies getting value aren't fully automating - they're amplifying their teams.
Multi-agent systems > single agents The future is about systems of agents collaborating rather than a single "do everything" agent.
Proactive > reactive The clients seeing the best results are moving from "ask and respond" agents to proactive systems that monitor business events and take initiative. By the end of 2025, AI agents will "automatically prepare decision workflows" in response to things like supply disruptions.
I'm not saying don't get excited about AI agents - just be realistic. Building truly useful agent systems is hard, messy work that requires understanding the problem you're actually trying to solve.
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