AI智能体与工作流自动化
甄选并验证：Leena Shah, Applied AI Engineer, Zoom一个给出错误答案的聊天机器人浪费一分钟。一个采取错误行动的智能体可能会发起退款、清空数据库或发送邮件——能够*行动*的软件与只会说话的软件遵循不同的标准。 本课程旨在缩小这一差距：将智能体演示转变为可针对真实业务系统持续运行的自动化。您将从每个严肃自动化项目的起点开始——映射实际流程，并诚实地决定哪些需要自动化，哪些保持原样。然后，您将正确地将每个任务置于从简单脚本到完整智能体的谱系中，组合涵盖大多数真实案例的几种工作流模式，并通过清洁、最小权限、幂等的接口将智能体与工具和数据连接起来。您将在需要权衡和可逆性的地方精确地将人纳入循环，并通过追踪、评估、成本上限和服务水平目标监控一切——同时深入审视提示注入和致命的三角问题。 贯穿始终的是责任。当智能体行动时，人类仍需负责——从加拿大航空的聊天机器人到被清空的生产数据库等有据可查的灾难，都被视为教训而非恐怖故事。无需编码，但您会偶尔阅读真实工件：工具模式、权限门、追踪。通过37节课，您将获得一份基于持久部分而非月度框架的操作者手册。
课程目录
关于课程作者
Leena Shah’s portfolio reads less like a catalog of models than a record of bottlenecks removed: customer-support teams searching thousands of documents, analysts waiting on manual summaries, and product managers unsure whether an AI answer can be trusted. She has addressed those problems with retrieval-augmented assistants, multimodal extraction, voice agents, and tool-using workflows, pairing Python and FastAPI services with vector databases, cloud infrastructure, and evaluation suites that measure grounding, latency, safety, and cost. Leena has also fine-tuned open models for private environments, introduced human-review loops and production guardrails, and worked alongside product, security, and domain specialists to turn rough prototypes into monitored features. Her strength lies in recognizing where AI can genuinely improve a workflow—and engineering the surrounding system so that improvement survives contact with real users.
评价 (16)
- cozy_hare
🔥
- golden_seahorse
super clair, merci!
- gallant_macaw
挺好的，讲得很清楚。
- adept_weaver
love it
- giddy_ferret
thx