Digital Employees in Production: 92% Decision Agreement at MINISO, +59% Livestream Sales at Winner
Two companies at the Feishu CLI Developer Day in Shenzhen shared numbers, not slideware: Winner Group (稳健) and MINISO (名创优品). Both run "digital employees" in production — agents with a defined job, real permissions, and measurable performance. This post pulls apart what they actually built, the standard MINISO uses to qualify a digital employee, and the lark-cli building blocks so you can stand up your own.
MINISO's five-part standard for a digital employee
Before the cases, the most useful takeaway: MINISO's definition of a qualified digital employee. It needs all five:
- A clear job definition — it has a position, not just a prompt.
- Company-specific skills — tuned to your processes, not generic.
- Quantifiable KPIs — you can grade its work.
- Standardized tool permissions — it can only touch what its role allows.
- Long-term memory that keeps iterating — it accumulates context over time.
That last three are where lark-cli does the heavy lifting: permissions, measurable output written to a Base, and a memory built from your own meetings and docs.
MINISO: 92% decision agreement, 70% less manager comms time
MINISO pulls the company's full set of group chats and meeting records through the CLI, distills the decision logic of core scenarios, then validates it with multi-model checking and multi-version "horse racing" to keep the digital employee's decisions aligned with a real manager's.
Result: the management-type digital employees now in production reach 92% decision agreement with real managers, auto-answer employees' routine questions, and cut managers' daily communication time by over 70%.
The "learn how we decide" loop:
# 1. Harvest real decisions from meetings & chats
lark minutes +search --keyword "审批" --as user
lark im +messages-search --keyword "同意" --as user
# 2. Distill decision rules, store as the employee's skill / memory
lark docs +create --content '<title>XX岗位决策手册</title>...'
The distillation prompt:
"你是管理者决策分析师。下面是过去一个月某管理者在会议和群聊里做审批/拍板的真实记录。请归纳出他做决策的稳定规则:什么情况下直接同意、什么情况退回、退回时常给的理由、对哪类风险最敏感。输出为 5-8 条可执行的判断规则,每条附一个原文里的真实例子。不要泛泛而谈。"
Winner Group: three production workflows
Winner Group shared three concrete builds, all on the same "CLI connects, LLM judges" split:
- 618 daily business report, automated — the CLI pulls sales, ad-spend, and complaint data from every channel; the model generates a structured daily report that drives the daily promo review. Ended with both sales and profit beating target.
- 24-hour livestream monitoring — built by a business person on the aily agent. Auto-generates a data report every hour and pushes real-time alerts on anomalies. Big-promo livestream sales hit 120% of target, +59% YoY.
- Agent-as-judge — in an internal data-analysis contest, the agent auto-joined the meetings and scored every team's roadshow against a preset rubric. Its scores matched the human judges' to a very high degree.
The livestream-monitor skeleton (the most copy-able):
# Hourly: pull the numbers, generate the report
lark base record list --app-token <app> --table-id <tbl> # sales feed
# Push anomalies to the ops group the moment they appear
lark im +messages-send --chat-id <ops_group> --msg-type text \
--content "⚠️ 在线人数较上小时跌 30%,请关注投流"
The one line that ties it together
Winner Group's own summary is the sentence worth keeping: Feishu CLI owns connection and execution; the LLM owns understanding and judgment. A digital employee is just that split, given a job description. Define the role, wire the pull-and-write-back loop with lark-cli, grade its output against a human, and iterate the memory. That's the whole playbook.