# Forward Deployed Engineering 101
**作者**: vas
**日期**: 2026-05-20T19:10:55.000Z
**来源**: [https://x.com/vasuman/status/2057177266984226892](https://x.com/vasuman/status/2057177266984226892)
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By the end of this article, you should understand why Anthropic, OpenAI, Google, and other AI companies are looking for FDEs, and how you can capitalize on this demand.
I've done the job myself, hired some of the best in the world to build out our FDE motion at Varick, and noticed there's no real definitive guide to the hottest role in tech right now. This is that guide.
Why do AI companies need FDEs
To become an FDE, the first step is to internalize why AI companies are in desperate need of them.
If you believe intelligence is becoming commoditized, it then follows that the only competitive edge is how and where you use it. In fact, I will go so far as to say that there is no competitive advantage in intelligence alone. Therefore, determining how and where companies use it becomes the most important role and that is the role of a forward deployed engineer.
Businesses hire an Applied AI company (like Varick) that deploys FDEs to help them get the most out of the technology. Doing this gives them access to a team that has already done large-scale AI transformations that make clients move much faster than their competitors, and by doing so, yields massive efficiency gains.
The FDE is a highly skilled engineer who can understand the customer's problems very deeply, write code into a code base they've potentially never seen before, and communicate the business impact to a non-technical decision maker to close the deal. This is a million-dollar hire.
What the role requires
Being an FDE requires you to be on-site with a customer. Palantir's CTO says that you cannot build products for an environment without actually being in the environment itself. We've seen the same thing internally.
The term FDE actually originated from Palantir, and they took being on-site very seriously. In 2010, they worked with the Special Forces group in Afghanistan. The Special Forces would go on the mission in the day, get feedback, and route it to the FDEs who would ship code during the night.
Being in the environment is as necessary for deploying military software as it is for deploying AI. In order to see real efficiency gains, a company needs to be rebuilt around AI from the ground up. And that is only possible through sitting with the customer and building custom agents that are engineered on company-specific data, with company-specific context.
About the role
In our view, there are three main parts of an Applied AI FDE's job: Audit, Evals, and Deployment. Let's break down each one.
Audit: You're onsite with a client, mapping processes/workflows in different teams within the company. For example: two weeks with rev ops, one week with procurement, and a full month with finance.
With each team you sit with, you learn a few things: what their job looks like, where the bottlenecks are, and where you might create agents that deliver value.
Along with understanding the workflows of each team in the company, an important part of the audit phase is determining what should be automated vs what shouldn't. There's a point where agents can create more problems than they solve.
Here are three general principles you can follow to help you decide.
If a workflow can be distilled into rules but the inputs are different (one input is an email, the next could be a PDF, the next is a scanned image), and the work involves calling tools, put an agent in. If the rules and inputs are both predictable, code is faster and cheaper. If the decision needs pattern recognition and domain expertise, leave it manual.
Your clients aren't going to get good ROI if the agent runs five times a month. Look for lengthy, high-volume automations. There needs to be enough volume to matter.
Don't overuse AI when building your agents. Most automation tasks can be done with a series of tool calls and just one call to an LLM as an orchestrating layer. Too much AI leads to unneeded token costs (which compound at scale) and often a lower-quality output.
The final part of the Audit phase is prototyping. See Agents 101 to learn how to build an agent, and Agents 102 to take that agent from demo to production.
Evals: If a customer is spending millions on an AI deployment, they need to know it's working. To do that, an FDE builds detailed evals.
A good eval doesn't just check if the final answer an agent gives you is correct, but also verifies the AI is thinking like a human would. In order to do that, do two things:
Trace the human's steps and grade the AI on each one: A human doesn't solve problems in one move. It's a multi-step process. Map out those steps and see if the AI is hitting the same checkpoints along the way.
Start small with great examples of the intended outcome, then measure everything against them: If you're building a customer support agent, sit with a human and figure out what the best possible answer to a user's query is. Repeat that a few times over a few tasks. Now you know what "great" looks like and can hold the agents to that standard.
Evals prove value to the customer. While everyone says they want AI in their company, there are still many who are skeptical of whether it works. A good agent evaluation is what an executive needs to trust the agent will provide ROI.
Deployment: Avoid large-scale data migrations. Instead, build APIs over an existing data layer (SharePoint or databases) and place a model on top as an orchestrator to query through it. This saves time and money, and more importantly, saves you from the brutal nightmare of ripping out your existing systems. Our clients have spent millions of dollars and multiple years migrating to their latest ERP. The last thing they want to do is replace it again.
Once all of the above is completed, create an execution environment to test the agent safely. This is a sandbox directly in the company's infra so you can run, test, and debug the agent before hitting production.
When moving to production, start slow. Take a small workflow, get it to work, and then layer on additional capabilities. For example, start with an agent that catches bugs, investigates, and writes a ticket summarizing what it thinks went wrong. If that works, only then give it the capability of writing code and pushing a PR.
Start with the smallest unit of autonomy; only then give it the capability to take action.
That is how you go from an audit to deployment as an FDE. Learning these steps is the entire job in itself.
How to become an FDE in 30 days
There are typically three backgrounds that find the most success as an FDE: Consultants, Product Managers, and Software Engineers. Even if you don't fall into any of these, following the 30-day roadmap at the end of this section will exponentially increase your chances of getting the role. Do these in parallel with applying and interviewing.
Consultants/PMs
As a consultant or PM, you should already have the ability to translate data into ROI. That is half the job. But the biggest roadblock for someone with this background is a lack of engineering experience.
A high-quality portfolio can mitigate this. Pick two of these side projects and go all in:
- A production-ready AI agent that can execute an entire process that you used to do manually in your old job. It should be able to call APIs, log its thinking autonomously, and have a failure harness.
- A RAG pipeline built on top of a dataset (choose a custom dataset for whatever industry you're trying to break into: legal docs, medical records, financial filings, etc.).
- An eval framework you built yourself that scores an agent's outputs across multiple dimensions (correctness, format, cost, latency) for different business processes (procurement, accounts payable, etc.).
- An MCP where you can connect an LLM to legacy software that doesn't currently support AI integration.
Do not outsource your understanding to AI. If you take it step by step, these concepts should be quite understandable. There's a reason why this is titled 30 days, not 30 minutes.
Software Engineers
Arguably the most important part of being an FDE is communication. You need to translate what AI can and can't do into something that makes sense to a non-technical VP. If you can't do that, you can't be an FDE.
SWEs should build projects similar to those mentioned in the consulting/PM section, but explain every single component of what you just built. The tech stack, results, iterations you made, business outcomes. Most importantly, you need to have a reason for building those agents in the first place: what was the pain point you're solving for, and how might this go down in a real client interaction?
30-day outline regardless of role
For something more concrete, follow this 30-day plan that will prepare you for almost anything:
Checkpoint 1 (7 days in):
- What is an agent and how an agent loop works: read Anthropic's Building Effective Agents, then write a script that runs the loop: prompt → model → response → next step.
- How to make an agent call a tool: add two tool calls (an API call and a web search) using the Anthropic/OpenAI tool use tutorials.
- How to build proper guardrails: add input validation, a max-step limit, and output filtering before anything reaches the user.
- When to use context window vs external memory: default to context unless state needs to persist longer than the run.
- What is an audit trail and how to build one: log every prompt, tool call, and response with timestamps. This helps find and flag agent errors.
Checkpoint 2 (14 days in):
- How to enforce structured outputs: always return JSON. Read through OpenAI's developer page.
- How to take a demo to prod and what usually breaks: Read Agents 102.
- How to checkpoint: save agent state every n steps to a file so it can restart from the last checkpoint.
Checkpoint 3 (21 days in):
- How retry logic and exponential backoff work: every external call needs retries. On failure, wait 1s, 2s, 4s, 8s, cap at 16s.
- How to optimize cost when deploying agents: three things: cheaper models for cheap subtasks (Opus should be used only for reasoning), cache common prompts, cap max tokens. Track cost per query.
- How to build a golden dataset for evals: start with 20 real queries, label the perfect output yourself. Anthropic's "Demystifying evals for AI agents" covers everything.
- How multi-agent pipelines and parallel architectures work: split the job when one agent can't handle it. One plans, others execute, one synthesizes.
Checkpoint 4 (Final week):
Review the above and communicate everything out loud. Tie everything you can to business metrics.
TLDR
The FDE is the most in-demand role in tech right now. Every company needs AI, but nobody knows how to deploy it.
The job has three phases (audit, evals, deployment). Your job is to understand each phase and its purpose.
Your portfolio and your ability to speak about it are the deciding factors. Build agents, RAG pipelines, eval frameworks, MCPs, etc, and, most importantly, be able to confidently articulate the business use case behind everything you're building.
Lack of communication ability is a deal-breaker for the FDE role. If you can't explain what AI can and can't do to a non-technical decision maker, there won't be a deployment.
Know when AI isn't the answer; this builds trust with the client and more importantly, ROI for the agents in production.
Do these steps and your chances of breaking in will be exponentially higher.
If you want to make the jump to the fastest-growing Applied AI company in SF, Varick is hiring. We're building out the most elite FDE team in Silicon Valley, led by a former COO of Citadel Securities. Apply directly at https://www.varickagents.com/careers.
## 相关链接
- [vas](https://x.com/vasuman)
- [@vasuman](https://x.com/vasuman)
- [32K](https://x.com/vasuman/status/2057177266984226892/analytics)
- [Varick](https://www.varickagents.com/)
- [Agents 101](https://x.com/vasuman/status/2011923440769659132)
- [Agents 102](https://x.com/vasuman/status/2013742184772550851)
- [Building Effective Agents](https://www.anthropic.com/research/building-effective-agents)
- [Anthropic](https://platform.claude.com/docs/en/agents-and-tools/tool-use/overview)
- [OpenAI](https://developers.openai.com/api/docs/guides/tools)
- [developer page](https://developers.openai.com/api/docs/guides/structured-outputs)
- [Demystifying evals for AI agents](https://www.anthropic.com/engineering/demystifying-evals-for-ai-agents)
- [https://www.varickagents.com/careers](https://www.varickagents.com/careers)
- [Upgrade to Premium](https://x.com/i/premium_sign_up)
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---
*导出时间: 2026/5/21 10:15:03*
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## 中文翻译
# 前线部署工程 101
**作者**： vas
**日期**： 2026-05-20T19:10:55.000Z
**来源**： [https://x.com/vasuman/status/2057177266984226892](https://x.com/vasuman/status/2057177266984226892)
---
读完本文后，你应该能理解为什么 Anthropic、OpenAI、Google 以及其他 AI 公司都在招聘前线部署工程师（FDE），以及你如何抓住这一需求带来的机会。
我自己做过这份工作，也招聘了一些世界上最优秀的人才来构建 Varick 的 FDE 团队，但我发现目前没有任何一份真正权威的指南来介绍这个当下科技行业最热门的岗位。本文就是那份指南。
为什么 AI 公司需要 FDE
要成为 FDE，第一步是深入理解为什么 AI 公司急需这类人才。
如果你相信智能正在变得商品化，那么随之而来的结论就是：唯一的竞争优势在于你如何以及在何处使用它。事实上，我甚至要更进一步说，单靠智能本身没有任何竞争优势。因此，决定公司如何以及在何处使用智能就成了最重要的角色，而这个角色正是前线部署工程师。
企业会聘请应用 AI 公司（如 Varick），由它们派出 FDE 帮助企业从这项技术中获得最大价值。这样做能让企业获得一个已经完成过大规模 AI 转型的团队，使客户的行动速度远超竞争对手，从而带来巨大的效率提升。
FDE 是一名高技能工程师，能够非常深入地理解客户的问题，在一个他们可能从未见过的代码库中编写代码，并向非技术背景的决策者传达业务影响以促成交易。这是一个价值百万美元的招聘岗位。
这个岗位需要什么
担任 FDE 需要你在客户现场工作。Palantir 的 CTO 说过，如果你不真正身处某个环境之中，就无法为那个环境构建产品。我们在内部也看到了同样的事实。
FDE 这个术语实际上起源于 Palantir，他们对驻场工作非常重视。2010 年，他们与阿富汗的特种部队合作。特种部队白天执行任务，收集反馈，并将其传递给 FDE，FDE 会在夜间交付代码。
身处现场环境之中，对于部署军事软件和部署 AI 同样必不可少。要获得真正的效率提升，一家公司需要围绕 AI 从底层彻底重构。而这只有通过与客户坐在一起、基于公司特定的数据和公司特定的情境构建定制化智能体（agent）才能实现。
关于这个岗位
在我们看来，应用 AI FDE 的工作主要有三个部分：审计（Audit）、评估（Evals）和部署（Deployment）。让我们逐一拆解。
审计：你在客户现场，梳理公司内不同团队的流程/工作流。例如：与销售运营团队待两周，与采购团队待一周，与财务团队待整整一个月。
与每个团队合作时，你会了解几件事：他们的工作是什么样的，瓶颈在哪里，以及你可以在哪里创建能创造价值的智能体。
除了理解公司每个团队的工作流之外，审计阶段的一个重要部分是确定哪些应该自动化、哪些不应该。存在一个临界点，超过这个点，智能体制造的问题会比解决的问题更多。
以下是三个可以帮助你决策的一般性原则。
如果一个工作流可以提炼成规则，但输入各不相同（一个输入是电子邮件，下一个可能是 PDF，再下一个是扫描图像），并且工作涉及调用工具，那就部署智能体。如果规则和输入都可预测，代码更快也更便宜。如果决策需要模式识别和领域专业知识，就保持人工操作。
如果智能体一个月只运行五次，客户是无法获得良好投资回报的。要寻找耗时长、高吞吐量的自动化任务。需要有足够大的量级才有意义。
构建智能体时不要过度使用 AI。大多数自动化任务可以通过一系列工具调用，再加上仅仅一次调用 LLM 作为编排层来完成。过多的 AI 会导致不必要的 token 成本（而且会在规模上复利增长），并且通常产出质量更低。
审计阶段的最后一部分是原型开发。参见《智能体 101》学习如何构建智能体，参见《智能体 102》了解如何将智能体从演示推进到生产环境。
评估：如果客户在一项 AI 部署上花费数百万美元，他们需要知道它是否有效。为此，FDE 需要构建详尽的评估体系。
一个好的评估不仅要检查智能体给出的最终答案是否正确，还要验证 AI 是否像人类一样思考。为此，要做两件事：
追踪人类的步骤并对 AI 的每一步打分：人类解决问题不是一步到位的，而是一个多步骤的过程。把这些步骤梳理出来，看看 AI 是否在过程中达到了相同的检查点。
从少量高质量的预期结果示例开始，然后以此衡量一切：如果你在构建一个客服智能体，和真人坐在一起，弄清楚针对用户提问的最佳可能答案是什么。在几个任务上重复几次。这样你就知道“优秀”是什么样子，并能以此标准要求智能体。
评估向客户证明了价值。虽然每个人都说他们想让公司用上 AI，但仍有许多人怀疑它是否真的有效。一份好的智能体评估，正是高管信任智能体能带来投资回报所需要的依据。
部署：避免大规模数据迁移。相反，在现有数据层（SharePoint 或数据库）之上构建 API，并在其上放置一个模型作为编排器来查询数据。这既节省时间和金钱，更重要的是，让你免于拆除现有系统的可怕噩梦。我们的客户曾花费数百万美元和数年时间迁移到他们最新的 ERP。他们最不想做的就是再次更换它。
完成上述所有步骤后，创建一个执行环境以安全地测试智能体。这是公司基础设施中的沙盒，因此你可以在进入生产环境之前运行、测试和调试智能体。
进入生产环境时，起步要慢。选一个小型工作流，让它先跑起来，然后再逐步叠加其他能力。例如，先从一个能捕获 bug、进行调查并撰写工单（总结它认为哪里出了问题）的智能体开始。如果效果不错，再赋予它编写代码和提交 PR 的能力。
从最小的自主单元开始；之后再赋予它采取行动的能力。
这就是作为 FDE 从审计走向部署的全过程。学习这些步骤本身就是整份工作的核心。
如何在 30 天内成为 FDE
通常有三类背景的人做 FDE 最容易成功：顾问、产品经理和软件工程师。即使你不属于其中任何一类，按照本节末尾的 30 天路线图行事，也会极大地提高你获得这个职位的机会。在做这些的同时同步进行求职申请和面试。
顾问/产品经理
作为顾问或 PM，你应该已经具备将数据转化为 ROI 的能力。这占了这份工作的一半。但拥有这种背景的人最大的障碍是缺乏工程经验。
一个高质量的作品集可以弥补这一点。从以下副项目中选两个并全力以赴：
- 一个可投入生产环境的 AI 智能体，能够执行你以前工作中手动完成的整个流程。它应该能够调用 API、自主记录其思考过程，并具备故障处理机制。
- 一个基于数据集构建的 RAG 管道（为你想进入的行业选择一个自定义数据集：法律文件、医疗记录、财务申报文件等）。
- 一个你自己构建的评估框架，可以针对不同业务流程（采购、应付账款等）从多个维度（正确性、格式、成本、延迟）对智能体的输出进行评分。
- 一个 MCP，可以将 LLM 连接到目前不支持 AI 集成的遗留软件。
不要把你的理解外包给 AI。如果你一步一步来，这些概念应该是相当容易理解的。之所以标题是 30 天而不是 30 分钟，是有原因的。
软件工程师
可以说，作为 FDE 最重要的部分是沟通。你需要把 AI 能做什么和不能做什么翻译成非技术背景的副总裁能听懂的东西。如果你做不到这一点，你就当不了 FDE。
SWE 应该构建类似于顾问/PM 部分提到的项目，但要解释你构建的每一个组件。技术栈、结果、你做的迭代、业务成果。最重要的是，你要有一个最初构建这些智能体的理由：你要解决的痛点是什么，这在真实的客户互动中会如何展开？
无论何种角色都适用的 30 天大纲
如果你想要更具体的内容，请遵循这个 30 天计划，它将让你为几乎所有事情做好准备：
检查点 1（第 7 天）：
- 什么是智能体以及智能体循环如何工作：阅读 Anthropic 的 Building Effective Agents，然后编写一个运行该循环的脚本：提示词 → 模型 → 响应 → 下一步。
- 如何让智能体调用工具：使用 Anthropic/OpenAI 的工具使用教程添加两个工具调用（一个 API 调用和一个网络搜索）。
- 如何构建合适的防护栏：在任何内容到达用户之前，添加输入验证、最大步数限制和输出过滤。
- 何时使用上下文窗口 vs 外部记忆：默认使用上下文，除非状态需要在运行结束后持续存在。
- 什么是审计追踪以及如何构建：为每个提示词、工具调用和响应记录时间戳日志。这有助于发现和标记智能体错误。
检查点 2（第 14 天）：
- 如何强制结构化输出：始终返回 JSON。通读 OpenAI 的开发者页面。
- 如何将演示推向生产环境以及通常什么会出问题：阅读 Agents 102。
- 如何设置检查点：每 n 步将智能体状态保存到文件，以便它可以从上一个检查点重新启动。
检查点 3（第 21 天）：
- 重试逻辑和指数退避如何工作：每个外部调用都需要重试。失败时，等待 1 秒、2 秒、4 秒、8 秒，上限 16 秒。
- 部署智能体时如何优化成本：三件事：对廉价的子任务使用更便宜的模型（Opus 应仅用于推理），缓存常用提示词，设置最大 token 上限。跟踪每次查询的成本。
- 如何为评估构建黄金数据集：从 20 个真实查询开始，自己标注完美输出。Anthropic 的 "Demystifying evals for AI agents" 涵盖了所有内容。
- 多智能体管道和并行架构如何工作：当一个智能体无法处理时拆分任务。一个负责规划，其他执行，一个负责综合。
检查点 4（最后一周）：
复习上述内容，并把所有内容大声讲出来。尽可能把一切与业务指标联系起来。
TLDR
FDE 是目前科技行业最抢手的角色。每家公司都需要 AI，但没人知道如何部署它。
这份工作有三个阶段（审计、评估、部署）。你的任务是理解每个阶段及其目的。
你的作品集以及谈论作品集的能力是决定性因素。构建智能体、RAG 管道、评估框架、MCP 等，最重要的是，能够自信地阐述你所构建的一切背后的业务用例。
缺乏沟通能力是 FDE 角色的硬伤。如果你无法向非技术背景的决策者解释 AI 能做什么和不能做什么，就不会有部署落地。
知道什么时候 AI 不是答案；这能建立客户的信任，更重要的是，为生产环境中的智能体带来 ROI。
完成这些步骤，你打入这个领域的机会将呈指数级提升。
如果你想在旧金山发展最快的应用型 AI 公司实现职业飞跃，Varick 正在招聘。我们正在打造硅谷最精英的 FDE 团队，由 Citadel Securities 前首席运营官领导。请直接在 https://www.varickagents.com/careers 申请。
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*导出时间： 2026/5/21 10:15:03*
