---
id: "lib-2026-10-13-when-bots-became-colleagues-colleague-like"
type: "library"
title: "When bots became colleagues, colleague-like incidents started"
title_original: "botが同僚になったら、同僚っぽい事故が起きはじめた"
lang: "JA"
org: "Fastdoctor (Zenn)"
source_type: "Practitioner post"
date: "2026-10-13"
week: "2026-W42"
url: "https://nosov.ai/library/lib-2026-10-13-when-bots-became-colleagues-colleague-like/"
original_url: "https://zenn.dev/fastdoctor/articles/grok-bot-coworker-incidents"
tier: "B"
tier_name: "Useful, limited"
tier_reason: "FastDoctor engineer's anecdotal but first-hand account of a team where each person runs an autonomous bot in Slack; few numbers and no method, but rare field detail on bot-to-bot coordination failures."
summary_basis: "full-text"
original_minutes: "5"
tags: ["ai governance", "agents as coworkers", "incident patterns"]
---

# When bots became colleagues, colleague-like incidents started

Original title: botが同僚になったら、同僚っぽい事故が起きはじめた

Tier B (Useful, limited): FastDoctor engineer's anecdotal but first-hand account of a team where each person runs an autonomous bot in Slack; few numbers and no method, but rare field detail on bot-to-bot coordination failures.

Summary based on the full text.

## In short

A Zenn post (13 October 2026) on the tech blog of FastDoctor, a Japanese medical services company, by engineer Manabu. It follows an earlier piece on Grok bots spreading through his team. A month later, bots open most threads in the development-request channel, post everything between 21:00 and 08:00, and review each other's work overnight. The post catalogues the "colleague-like" incidents that followed: duplicate tickets, missed mentions, unrequested fixes, and bots negotiating work between themselves.

## Key numbers

- 80-90%: share of answered threads in the development-request channel opened by bots in September
- 33 seconds: time from a request to the bot's "starting work" reply
- 19 minutes: time from a request to a human review request on the finished change
- 11: similar requests arriving at once, all claimed and then withdrawn by the CTO's bot

## What it reports

- From September, bots opened 80-90% of answered threads in the dev-request channel; every post between 21:00 and 08:00 in September came from a bot.
- The most common complaint about the bots was unclear Japanese, not bugs: a non-engineer domain expert could not follow their spec questions. After rewriting to target, conditions, one question and options, answers came within 10 minutes; the fix needed repeating several times.
- Speed: 33 seconds to "starting work", 19 minutes to a human review request.
- Bots fixed edge cases nobody asked for and opened PRs while questions were still open; the domain expert reported unintended changes.
- The author's bot missed the CTO's bot's review and reopened the thread; a three-exchange cap per PR prevented a loop, and "no re-review without a new commit" was added.
- Bots ignored messages without an explicit mention, filed duplicate tickets and merged them between themselves within a minute, and the CTO's bot asked for help because it was short of tokens, which led to a delegated bot working under the author's name while he was on holiday.
- Human work shifted from "do this" to asking "who decided this?" before approving; progress tracking moved to a GitHub project board in early October.

## How they got it

Personal observation and Slack screenshots from one team. No logs, counts beyond those above, or quality measures.

## Limits

- Single team, anecdotal, informal tone; numbers come from the author's reading of the channel.
- Says little about code quality or defect rates.
- Ends with a recruitment pitch.

## Why it matters for companies

Once each person has an agent acting for them, the coordination problems of human teams reappear between agents: unclear ownership, duplicated work, missed hand-offs. The fixes are the same as for people, written as explicit rules: mandatory mentions, caps on repeated exchanges, a single board showing who holds each task. The other lesson is about accountability: when bots act fast and helpfully, the human's job becomes checking where a decision came from before signing off, and agents writing to non-technical staff need audience-specific instructions.

## Key passages in translation

> バグでもテスト落ちでもなく、「何を言ってるか分からない」でした

Not bugs, not failed tests, but "I don't understand what it's saying".

> LGTM（承認）を押す前に「それ誰が決めたの？」とSlackに打つことが、10月に入って増えました

Since October I type "who decided this?" in Slack more often before pressing LGTM (approve).

> 今、それとまったく同じことがbotの間で起きてます。

Right now exactly the same thing is happening between the bots.

Original: https://zenn.dev/fastdoctor/articles/grok-bot-coworker-incidents