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💫 #LangChain Academy 新课程：构建可靠的智能代理 💫

将智能代理部署到生产环境并非易事。传统软件具有确定性——当出现故障时，只需检查日志并修复代码即可。但智能代理依赖于非确定性模型。

再加上多步骤推理、工具使用以及真实用户流量等因素，构建可靠的智能代理比传统系统设计复杂得多。

本课程旨在教你如何通过迭代优化，将代理从首次运行逐步打造成生产就绪的系统。

你将学习如何使用 #LangSmith 实现这一目标——这是我们专为观察、评估和部署代理而打造的代理工程平台。

https://x.com/LangChain/status/2033269622103793917?s=20

中文 English

0:00 我很高兴宣布我们最新的Langshane学院课程——构建可靠的

0:03 Langsmith代理。

0:04 我们正在解决代理工程团队如今面临的最大挑战之一，

0:09 无论你是搭建系统的工程师还是负责交付的产品经理，

0:10 将代理从原型推向生产环境都是一项艰巨的任务。

0:13 可靠性并不新鲜，工程师们

0:14 数十年来一直致力于提升系统的正常运行时间、准确性和性能。那么为什么代理这么难处理呢？

0:20 传统软件有明确的规则和确定性的逻辑。

0:21 出现问题时，你查看日志，找到代码行。代理的代码只是骨架，

0:27 它定义了工具和

0:29 提示。真正的决策发生在一个非确定性的模型内。你无法仅从代码库中

0:34 阅读模型的

0:35 推理，但你可以追踪从初始输入到最终输出的决策过程。

0:39 这种可观测性是构建可靠代理的基础。

0:41 本课程的目标是通过持续迭代，将代理从首次运行打磨成生产就绪系统。

0:47 你将学习如何使用

0:48 我们的代理工程平台Langsmith来观察、评估和部署代理。

0:53 你会从第一次分析周期开始，运行代理，找出问题，

0:55 并通过追踪而非日志进行调试。修复失败后，还要确保它们不再出现。

1:00 你会构建评估来捕捉回归并随着时间追踪质量。

1:04 最后进入生产阶段，每天成千上万用户使用你的代理，

1:05 你需要一个既能随业务增长又成本高效的测试系统。

1:09 到课程结束，

1:10 你将拥有完整的工具包来构建、测试和运营任意规模的可靠代理。

1:14 我非常期待你投入学习，掌握用Langsmith构建可靠代理的方法。

1:20 你可以今天就报名参加。

0:00 I'm excited to announce our latest Langshane Academy course, Building Reliable

0:03 Agents with

0:04 Langsmith. We're tackling one of the biggest challenges Agent Engineering teams

0:09 face today,

0:10 whether you're an engineer building the system or a product manager responsible

0:13 for shipping it,

0:14 bringing agents from prototype to production is difficult. Reliability is not

0:20 new. Engineers

0:21 have spent decades refining uptime, accuracy, and performance. So why are

0:27 agents so hard to get right?

0:29 Traditional software has clear rules and deterministic logic. Something breaks,

0:34 you check the logs,

0:35 you find the line of code. With agents, the code is just scaffolding. It

0:39 defines the tools and the

0:41 prompt. The real decisions happen inside a non-deterministic model. You can't

0:47 read a model's

0:48 reasoning in your code base, but you can trace its decisions from first input

0:53 to final output.

0:55 This observability is the foundation of building reliable agents.

1:00 The goal of this course is to take an agent from first run to production-ready

1:04 system

1:05 through iterative cycles of improvement. You'll learn how to do this with Lang

1:09 smith,

1:10 our Agent Engineering platform for observing, evaluating, and deploying agents.

1:14 You'll start with your first analysis cycle, running your agent, finding what

1:20 breaks,

1:20 and debugging with traces, not logs. Once you fix those failures, you'll make

1:25 sure they don't

1:25 come back. You'll build evals that catch regressions and track quality over

1:30 time.

1:30 Finally, production. Thousands of users hitting your agent daily. You'll need a

1:36 testing system

1:37 that scales the grow while staying cost-efficient. By the end, you'll have the

1:42 complete toolkit

1:43 to build, test, and operate reliable agents at any scale.

1:48 I'm really excited for you to dive in and learn how to build reliable agents

1:51 with Langsmith.

1:53 You can enroll today.