面试题库

30 天从前端工程师到 Agent 工程师

D1 LLM API 基础：messages/roles、token、流式、temperature；Agent 到底是什么

• 用户切到后台或者直接关掉网页，回来后怎么恢复那条还在生成的回复？The user backgrounds the app or closes the tab. How do you restore a reply that was still being generated?

  国内高频海外高频深入#streaming#reliability#architecture

  分析过程 · 先想清楚再作答

  1. 先识别这题和「网络断了」不是同一个问题：客户端已经不存在了，任何写在前端的重试逻辑都不会执行。
  2. 由此推出唯一出路：生成过程必须能脱离这个客户端独立存活，也就是把流本身放到服务端持久化。
  3. 落到具体架构：发起请求时给这轮生成分配一个流 id，服务端一边把 token 推给当前连接，一边把同样的内容写进 Redis 之类的存储；会话记录里保存这个 activeStreamId。
  4. 恢复路径是另开一个 GET 端点：客户端带着会话 id 请求，服务端按 activeStreamId 找到那条流并接着推；找不到活跃流就返回 204，让前端知道没有需要恢复的东西。
  5. 说清代价，别只说方案：多了一份存储、一套过期清理、以及「同一条流可能被多个连接消费」的并发问题。
  6. 延伸：这套结构和普通聊天产品的「消息已持久化，重进会话直接读库」不同——区别在于回复还在生成中，需要的是可续的流而不是一条静态记录。

  How to reason about it · think before answering

  1. First separate this from a dropped connection: the client is gone, so no client-side retry will ever run.
  2. That leaves one option — the generation must outlive the client, which means persisting the stream server-side.
  3. Concretely: assign a stream id per generation; the server pushes tokens to the live connection while also writing them to storage such as Redis, and the chat record stores that activeStreamId.
  4. Recovery is a separate GET endpoint: the client asks with the chat id, the server locates the stream by activeStreamId and resumes; with no active stream it returns 204.
  5. Name the costs, not just the design: extra storage, expiry/cleanup, and concurrency when several connections consume the same stream.
  6. Extension: this differs from ordinary message persistence because the reply is still being produced — you need a resumable stream, not a static row.

  答题要点

  • 客户端已经不在了，前端重试无从谈起，必须让生成过程在服务端独立存活
  • 发起生成时分配流 id，服务端边推送边把内容写进 Redis，会话里记录 activeStreamId
  • 恢复走单独的 GET 端点：按会话 id 找到活跃流接着推，没有活跃流就返回 204
  • 代价：额外存储、过期清理，以及同一条流被多个连接消费的并发处理
  • 与「消息持久化后重新读库」的区别在于回复仍在生成中，需要的是可续的流

  Key points

  • The client is gone, so recovery must live server-side: the generation has to outlive the connection
  • Assign a stream id at start; the server writes tokens to Redis while streaming, and the chat stores activeStreamId
  • Resume through a dedicated GET endpoint that replays the active stream, returning 204 when there is none
  • Costs: extra storage, expiry and cleanup, and concurrent consumers of one stream
  • It differs from plain message persistence because the reply is still in flight, so you need a resumable stream

D3 Pi SDK 上手：三层架构、Agent Loop 对照（dg P01/P02/M02/M03）

• Pi SDK 的三层架构分别对应什么职责？这样分层解决了什么问题？What are the responsibilities of Pi SDK's three layers, and what does that layering buy you?

  国内高频海外高频基础#framework-design#architecture

  分析过程 · 先想清楚再作答

  1. 前半句是记忆题，后半句才有区分度。只背出三个包名而说不出「为什么这么切」，面试官会判断你只是照着文档看了一遍。
  2. 先把三层说准：最底层是统一的模型调用层，负责把各家 provider 的请求格式、鉴权、流式分包收敛成一套接口，还统计 token 与成本；中间是 Agent 内核层，构建在模型层之上，负责 Agent 循环、工具执行、状态管理和事件流；最上层是应用层，负责会话存取、扩展与资源装载、内置工具，以及交互式、打印、进程间调用、嵌入式 SDK 这几种运行模式。依赖方向严格单向向下。
  3. 然后回答「解决了什么」：分层的价值是让你能「只要一半」——只想要统一的模型调用层就停在最底层，想要完整循环但不要终端交互就停在中间层。这条判据可以用来评估任何框架，比复述包名有用得多。
  4. 补一个很实际的收益：排障时先判断问题落在哪一层。报错栈里出现模型层，多半是鉴权、模型 id 或请求格式；出现内核层，那是循环或工具执行；两者的排查方向完全不同。
  5. 可以预期的追问：这套分层跟你手写的版本怎么对应？答手写版把三层揉在了一个文件里——fetch 那几行是模型层，while 循环和工具分派是内核层，命令行交互是应用层。能当场做这个映射，比任何背诵都有说服力。

  How to reason about it · think before answering

  1. The first half is recall; the second half carries the signal. Reciting three package names without explaining the cut suggests you only skimmed the docs.
  2. State the layers precisely: the bottom is a unified model layer that normalizes each provider's request format, auth and streaming into one interface while tracking tokens and cost; the middle is the agent kernel built on top of it, owning the agent loop, tool execution, state and the event stream; the top is the application layer, owning session storage, extension and resource loading, built-in tools, and the interactive, print, RPC and embedded-SDK run modes. Dependencies point strictly downward.
  3. Then answer what it buys: layering lets you take only half. Want just a unified model layer and your own loop? Stop at the bottom. Want the full loop but none of the terminal UX? Stop in the middle. That test generalizes to any framework and is worth far more than the package names.
  4. Add the practical payoff: when something breaks, first place it in a layer. A stack trace through the model layer points at auth, a wrong model id or a malformed request; one through the kernel points at the loop or tool execution. The two investigations look nothing alike.
  5. Expect the follow-up: how does this map onto the loop you wrote by hand? All three layers were collapsed into one file — the fetch calls were the model layer, the while loop and tool dispatch were the kernel, and the CLI was the application layer. Making that mapping live is more convincing than any recitation.

  答题要点

  • 模型层：统一各家 provider 的请求格式、鉴权与流式，附带 token 与成本统计，只收录支持工具调用的模型
  • 内核层：Agent 循环、工具执行与结果回填、状态管理、事件流，构建在模型层之上
  • 应用层：会话存取、扩展与资源装载、内置工具，以及交互式、打印、进程间调用、嵌入式 SDK 几种运行模式
  • 依赖单向向下，好处是每层可单独替换、单独测试，也能「只要一半」
  • 排障时先定位问题落在哪一层，模型层和内核层的排查方向完全不同

  Key points

  • Model layer: normalizes provider request formats, auth and streaming, tracks tokens and cost, and only ships tool-calling models
  • Kernel layer: the agent loop, tool execution and result folding, state management and the event stream, built on the model layer
  • Application layer: session storage, extension and resource loading, built-in tools, and the interactive, print, RPC and embedded-SDK run modes
  • Dependencies point one way, so each layer is replaceable and testable on its own and you can adopt only part of the stack
  • For debugging, place the failure in a layer first — model-layer and kernel-layer investigations diverge immediately