Interview Bank

From Frontend Engineer to Agent Engineer in 30 Days

D7 Packaging It as a Service: Fastify + SSE + Docker (dg P07); Week One Retrospective

• When turning a local agent script into a production service, what does the interface layer have to get right?把一个本地跑的 Agent 脚本改造成生产服务，接口层要重点考虑哪些事？

  Common in ChinaCommon overseasIntermediate#api-design#service-architecture#streaming

  How to reason about it · think before answering

  1. This tests whether you can name the assumptions hidden in a script. A generic checklist (auth, logging, monitoring) scores nothing; name the assumptions that silently break.
  2. List them first: one user (so history can live in a module-level variable), serial execution (no two requests mutating the same state), trusted input (you typed the arguments yourself), and a process whose life equals the session's. All four break in a service, and the first is hardest to catch because single-user local testing looks perfect.
  3. Then give the four decisions: response shape (single JSON versus streamed events), session identity (client-supplied id versus server cookie, and where history is stored), authentication and rate limiting (who may call, how often, and the per-call token ceiling), and how errors are expressed.
  4. Expand the last one — it is where this question is actually won. Once a streaming endpoint has written 200 and the first byte, the status code is already on the wire, so a later timeout, out-of-credit or upstream 500 can only surface as an agreed error event inside the stream. Validate everything you can before the first byte, because that is your last chance to speak in status codes.
  5. Add a production note: ship a health endpoint. Without one, orchestrators and load balancers cannot tell whether an instance is ready, and rolling deploys send traffic to a process that has not finished booting.
  6. Expect the follow-up: why cap tokens per request at the interface layer? Because agent cost is triggered by the caller and paid by you — no cap means handing your wallet to the client. Rate limiting is about money per call, not just QPS.

  分析过程 · 先想清楚再作答

  1. 这题考的是「你知不知道脚本里有哪些隐含假设」。答成一份笼统的清单（鉴权、日志、监控）拿不到分，要说出脚本时代默认成立、服务里立刻不成立的那几条。
  2. 先把假设列出来，这是最能体现工程视角的一步：只有一个用户（历史可以放模块级变量）、串行执行（不会有两个请求同时改一份状态）、输入可信（参数是自己敲的）、进程和会话同生共死（Ctrl+C 之后不用交代）。四条在服务里全部不成立，而第一条最难查，因为它在本地单人测试时表现完美。
  3. 然后给出四个必须做的决定：接口形状（一次性 JSON 还是流式推送）、会话标识（客户端带 sessionId 还是服务端发 cookie，以及历史存哪里）、鉴权与限流（谁能调、多久能调一次、单次 token 上限）、错误怎么表达。
  4. 第四条要单独展开，它是这题真正的区分点：流式接口一旦写出 200 和第一个字节，状态码就已经发出去了，之后模型超时、余额不足、上游 500，都只能在流里补发一个约定好的 error 事件。所以推流之前必须把能校验的全部校验完，那是你最后一次能用状态码好好说话的机会。
  5. 再补一条生产视角：服务要有健康检查接口。没有它，编排系统和负载均衡就没法判断这个实例能不能接流量，滚动发布时会把请求打给一个还没起好的进程。
  6. 可以预期的追问：单次请求的 token 上限为什么要在接口层限制？因为 Agent 的成本是请求方触发、你来买单，不设上限就等于把钱包交给调用方——限流限的不只是 QPS，还有每次调用能烧多少钱。

  Key points

  • A script's four assumptions all break in a service: single user, serial execution, trusted input, and a process that dies with the session
  • Session state must be keyed by session id, and in-process storage means data is lost on restart and blocks horizontal scaling
  • Four interface decisions: response shape, session identity, auth and rate limiting including a per-call token ceiling, and error semantics
  • A streaming endpoint cannot report errors by status code after the first byte, so define an in-stream error event and move all validation ahead of it
  • Expose a health endpoint, or orchestrators cannot tell whether the instance is ready for traffic

  答题要点

  • 脚本的四个隐含假设在服务里全部不成立：单用户、串行、输入可信、进程与会话同生共死
  • 会话状态必须按 sessionId 隔离，且要意识到放进程内存意味着重启即丢、无法水平扩容
  • 四个接口决定：响应形状、会话标识、鉴权与限流（含单次 token 上限）、错误表达方式
  • 流式接口推流之后无法用状态码报错，必须约定一个流内的 error 事件，并把校验全部前置到第一个字节之前
  • 提供健康检查接口，否则编排系统无法判断实例能不能接流量