How an Anthropic Japan BDR runs his day with a fleet of Claude agents
https://claude.com/resources/articles/how-an-anthropic-japan-business-development-representative-runs-his-day📌 【Anthropic 實戰案例】業務員怎麼靠 6 隻 Agent 跑完一整天
TL;DR:Anthropic 日本業務用 Claude Managed Agents 組了一支代號「Nakama」的 agent 機隊,包辦分案、回信與 CRM 更新。
每天早上花兩小時分派名單、再寫一輪中英文郵件,剩下時間拿來補 CRM,而且常常慢了一天以上——這是 Anthropic 日本團隊一位 business development representative(BDR)過去的日常。現在,這些事在他開始上班前就已經處理好大半,他只需要審核。
🤔 手動流程的真實痛點
這篇文章作者的工作是向日本企業展示 Claude 如何簡化工作流程,諷刺的是,他自己的工作大多還是手動進行:早上篩選與分派 leads、用日文或英文寫信,剩餘時間追更 Salesforce 紀錄,進度經常落後。他先用 Claude 一次處理一項工作、修正錯誤、保存修正結果,久而久之累積成一組 agent,同事得知後也想要他的 prompt 和 skills,但他只能給一個 zip 檔,對方還得自己上傳、回答一輪設定問題才能動起來。
🧩 一支日文命名的 agent 機隊
作者把工作流程拆成多個 agent,每個只負責一件事,方便追蹤出錯點,並統一取名為「Nakama」(夥伴):
- Furiwake(分揀):讀取每日 inbound 名單,依負責業務的名額與該名單的優先度分案,確保每個 lead 在大家開工前都有主人,並在 Slack 頻道貼出報告。
- Sabaki(處理):篩選分派給作者本人的 lead,依預設條件分類、採取對應的下一步(引導自助或客製首封信),更新 Salesforce 紀錄,並草擬第一封信,內容來自 Nakama 共用的知識庫。
- Henji(回覆):每小時掃描信箱,為需要回覆的客戶郵件草擬日文或英文回信,同樣取材自共用知識庫,供作者審閱、修改、寄出。
- Shoudan(商機):讀取近期客戶互動,把更新寫回 Salesforce,讓商機紀錄不需要手動輸入。
- Nokori(剩餘事項):在每天結束時掃描行事曆、讀取會議逐字稿,摘要會議內容並草擬後續追蹤信。
- 協調者(coordinator):機隊的「營運經理」,在各次執行之間更新所有 agent 的指令與共用知識庫;作者只要在 Slack 頻道用白話文寫下想要的改動,協調者每 15 分鐘左右檢查一次並套用更新。
每個 agent 都依排程自動執行。
💡 從 Claude 原型到 Managed Agents
作者先在 Claude 裡用三個元件原型化整支機隊:每個 agent 一個 skill(工作指令)、一個排程任務、以及一個筆電上的共用資料夾作為知識庫,記錄草稿與實際寄出內容的差異,以及過程中挖到的新資訊。遷到 Claude Managed Agents(beta)後,skill 變成 agent 的 system prompt,排程任務變成部署上的 schedule,共用資料夾變成 memory store。
轉移後最關鍵的三個改變:agent 可以在背景執行,不受筆電開關機影響,排程寫在部署設定裡,不需要自己維護 cron job;所有 agent 共用同一個 memory store,彼此能從作者的修改中學習,一處改好 Henji 的措辭,Sabaki 和 Nokori 就不會再犯同樣的錯;登入信箱與 Salesforce 的憑證則集中存放在具權限範圍的 managed vault 裡,各 agent 只拿得到自己工作所需的憑證,且作者可以一鍵撤銷存取權。
🎯 給想自建 agent 的業務人員
作者建議先在 Claude 裡用 skill+排程任務原型化,等 agent 數量多到難以追蹤時,再搬到 Managed Agents 集中管理版本與權限,這樣不需要具備開發者背景也能維運一支自動化機隊。
🔗 來源
- 標題:How an Anthropic Japan BDR runs his day with a fleet of Claude agents
- 作者/機構:Anthropic
- 連結:https://claude.com/resources/articles/how-an-anthropic-japan-business-development-representative-runs-his-day
#Anthropic #ClaudeAI #AIAgents #Automation #SalesTech #ManagedAgents #WorkflowAutomation #LLM #ProductivityTools #Agentic
原始資料 Claude Blog · 收集於 2026-10-16
摘要原文
As a business development representative on Anthropic's Japan team, my job is to show Japanese companies how Claude can simplify and automate their workflows, but until earlier this year, most of mine were still manual. I was spending a couple of hours each morning routing and qualifying leads, then writing emails in Japanese or English. In the time I had left, I was updating our CRM, often a day or more behind. Now my agents prepare most of that before my day starts, and I review it. The first Claude-run workflow I built was pretty scrappy, but it did the job. I'd hand Claude one piece of my work at a time, correct what it got wrong, and save those corrections. Over time, that grew into a handful of agents, each covering a different routine. Word got around quickly, and teammates started asking for my prompts and skills so they could build their own. All I could send them was a zip file with my skills and a few Markdown files from the knowledge base, which they had to upload to Claude and then answer a round of setup questions before anything worked. My own setup was getting harder to keep track of, too. Each agent was a skill, a scheduled task and a shared folder, and I changed them by asking Claude in whatever chat I had open, so the same skill was being edited from lots of different conversations. I wanted to define each agent once, in one place, and be able to see every change I'd made to it. So I decided to try Claude Managed Agents (opens in new tab) (beta), our managed agent infrastructure on the Claude Platform. I was hesitant to try it at first. The docs talk about schedules, tools, and credential vaults, and I'm a sales rep, so I assumed Managed Agents was for developers who can build and maintain the necessary infrastructure. I tried it anyway because my agents already existed, and I only had to move them over. It turned out I didn't need to be a developer. I described each agent's job in plain language, Claude wrote a few short setup scripts, and I ran them from the terminal on my laptop. (If you're starting from scratch, the Console also has a quickstart with ready-made agent templates.) Since then, there has been no infrastructure for me to look after. In this post, I'll walk through how I designed and prototyped the fleet, why I moved it to Managed Agents, how Claude Tag picks up anything that falls outside the routine, how it all changed my week, and what I'd tell sellers who want to build their own agents to automate manual, repetitive workflows. Designing my fleet of agents I divided up my workflow across several agents and gave every agent one clear job. A single responsibility keeps each one easy to reason about and easy to track, especially when something is off. I also gave every agent a Japanese name, because they mostly work in Japanese. Together they're called the Nakama , "the crew," or more literally, the companions you work with: Furiwake ("sorting") kicks off first: it reads the daily inbound queue and routes each lead to the right rep on the team, balancing the number of leads and the priority of each one and checking who's in that day, so every lead has an owner before any of us start our day. At the end of each run it posts a report to a dedicated Slack channel (every lead in, every lead assigned) so I can track the handoff. Sabaki ("handling") triages the leads that are assigned to me: it qualifies each lead, categorizes it against pre-set criteria, takes the defined next step for that category (draft a reply pointing them to to self-serve, craft a customized first email), updates the lead record in Salesforce, and drafts the first-touch email. It sources the draft's contents from the Nakama 's shared knowledge base, a memory store (opens in new tab) every agent reads and writes (more on this below). Henji ("reply") scans my inbox hourly for customer emails that need a response and prepares a draft reply in Japanese or English, depending on the thread, drawing on the same knowledge base as Sabaki . I can review, edit, and send the draft. Shoudan ("opportunity") reads recent customer interactions and pushes updates into Salesforce, so opportunity records stay current without manual data entry. Nokori ("what remains") runs at the end of the day: it scans my calendar, reads the attached meeting transcripts, summarizes each meeting, and drafts the follow-ups, again from the same knowledge base. Each agent runs on a schedule . A sixth agent, the Nakama's coordinator, is the fleet’s operations manager. It updates the agents’ instructions and the shared knowledge base between runs. When I want to change how an agent works, I write what I want in plain language in the Nakama’s Slack channel. Only I can post requests for changes to agents’ instructions and system prompts. The coordinator checks that channel every 15 minutes or so, picks up the request, and makes the update. Prototyping the fleet in Claude You don't need Managed Agents to start. I prototyped the whole fleet in Claude first, using the following pieces: A skill per agent: the instructions for one job. A scheduled task for each skill, so I don’t need to run it manually. A shared folder on my laptop as the knowledge base every skill writes its learnings back into: the difference between what it drafted and what I sent, plus anything new it dug up along the way. On Managed Agents, the skill became the agent's system prompt, the scheduled task became a schedule on the agent's deployment (opens in new tab) , and the shared folder became a memory store. I still ask for changes in plain language, but now I do it all in one Slack channel, and each change to an agent's instructions is saved as a new version, so each agent keeps a numbered history of its instructions. I'd suggest starting in Claude and moving your agents to Managed Agents once you have more than you can comfortably keep track of. Running the fleet on Claude Managed Agents Three things I got on Managed Agents that mattered most for my workflow: The agents can run in the background The agents run on their schedule whether my laptop is open or not, even when it's offline. The schedule is part of each agent's deployment. I could schedule skills in Claude too, but each of those schedules was a task set up in my own app. On Managed Agents, I set the cadence and timezone in the Console, next to the deployed agent's instructions and tools, and I have no cron jobs to maintain on the side. If a run goes wrong, I can open it in the Console and see each step the agent took. And while I'm trying out a change to an agent, the scheduled runs keep using the version that’s deployed, so a half-finished edit doesn't end up in tomorrow's run. The Nakama shares one memory and learns from my edits Every agent reads and writes the same memory store (opens in new tab) , so any agent can use the others’ learnings. The agents compare the drafts they wrote with the emails I sent, and they save each difference as a note on how I'd phrase it. They also keep whatever new information they dig up while researching an answer, from our internal docs and our official websites. I fix a phrasing once in Henji's drafts, and Sabaki and Nokori stop making the same mistake. Credentials live in a managed vault with scoped permissions My agents have access to my email and our Salesforce records, but since these are customer conversations and company records, I wanted that access under control. The credentials live in managed vaults (opens in new tab) , which protect credentials agents use to log in to outside services, such as OAuth access and refresh tokens, API keys, and personal access tokens. Each agent’s deployment uses only the vaults its job needs, it never sees the credentials, and I can revoke access in one step. Handing the next steps to Claude Tag For most of what the Nakama produces, I only need to read a draft in my inbox and send it, or glance at an updated Salesforce record.
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