Sep 2, 2099
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3 min to read
Discomfort Is Your Best Teacher
Finding comfort through continuous discomfort
Imagine assigning AI agents to real work — just like a teammate.
No new tools. No training required.
Just faster execution, fewer mistakes, and a team that never sleeps.
That’s what monday.com did.
They built a Digital Workforce — powered by GenAI agents — to manage over 1 billion work tasks per year.
And the best part? It’s not just a concept.
It’s shipping. It’s working. And they shared exactly how they built it.
Here’s what they did — and how you can do the same.
The problem:
monday.com powers workflows for thousands of teams — sales, marketing, ops, dev, support.
But scale introduced friction:
→ “Can AI actually do work — not just suggest?”
→ “Can I trust it with real boards and real data?”
→ “Will it break things?”
Early usage was high — but only in read-only mode.
The moment AI tried to change something, users froze.
The blocker wasn’t tech. It was trust.
So they designed for it — and adoption exploded.
The Digital Workforce
What they built:
A modular, multi-agent AI system
Embedded directly in monday’s Work OS
That works across:
→ Boards
→ Docs
→ Tasks
→ External sources
Users can:
→ Assign agents to tasks like teammates
→ Preview changes before anything updates
→ Undo or revise easily
→ Ask questions or get work done conversationally
Result?
100%+ month-over-month AI usage growth since launch.🛠️ How They Built It (Step-by-Step)

Step 1: Start with trust, not autonomy
→ They didn’t launch fully autonomous agents
→ Instead, built preview + undo as first-class features
→ Users could explore safely → adoption followed
Step 2: Use existing flows, not new UX
→ Agents work inside monday’s current workflows
→ No side panels. No AI tab.
→ Just assign an agent like a team member
Step 3: Modular agent architecture
Supervisor Agent: Routes tasks + manages flow
Data Retrieval Agent: Fetches from boards, docs, KB, web
Board Actions Agent: Executes updates and changes
Answer Composer Agent: Writes in the user’s preferred style
Each agent does one thing well.
Easier to scale. Easier to debug.
Step 4: Add fallbacks early
→ Most real user requests are unhandled at first
→ They built smart fallback flows:
Search help docs
Suggest self-serve steps
→ Avoids dead ends = better UX
Step 5: Eval is the IP
→ They built an internal evaluation framework
→ Tracks:
Accuracy
Hallucination rates
Undo usage
Conversion from preview → commit
→ This is their edge — not the model
Step 6: Control agent sprawl
→ Too many agents = compound hallucination
→ 90% x 90% x 90% = 73% accuracy
→ They tune agent chaining carefully to maintain output quality
Step 7: Build reusable workflows
→ One-off automation (e.g., earnings reports) aren’t scalable
→ They built dynamic orchestration
→ Reuse finite agents across infinite tasks
→ Just like human teams
What it Looks Like in Action: Example Workflow
Let’s say a user wants to update a board and generate a summary.
Here’s what happens under the hood:
User asks:
“Update the Q3 Marketing board with new leads and send me a summary for execs.”
Supervisor Agent:
→ Understands request
→ Splits into subtasks
→ Routes to right agents
Data Retrieval Agent:
→ Pulls latest lead data
→ Gets board status
→ Fetches docs if needed
Board Actions Agent:
→ Updates board
→ Assigns tasks
→ Logs the action
Answer Composer Agent:
→ Writes exec-friendly summary
→ Adapts tone to past user style
Preview Mode:
→ User sees full changes
→ Can approve, cancel, or revise
→ Built-in Undo option available
Memory Layer:
→ Stores preferences
→ Tracks user context for next time
→ Logs changes for traceability
All in one flow. All inside monday.
Feels like a teammate. Works like a machine.

TL;DR
Start small, but build trust
Let users preview. Build confidence before pushing automation.Use preview, undo, and fallback
Guardrails matter more than the model.Don’t add new UX — build into existing flows
Adoption is easier when AI lives where the user already works.Modular agents scale better
One job per agent. Easier to improve.Eval = the foundation
You can’t improve what you don’t measure.Personalize output by user type
Executives don’t want the same answer as analysts.Use supervisor agents to orchestrate
Think: traffic control, not just automation.Limit agent chaining to avoid hallucination
Too many hops = risk.Dynamic > Static — reuse your logic
Build general agents that plug into dynamic flows.HITL isn’t optional — it’s your failsafe
Especially in high-stakes workflows.Build memory from past tasks
Session-to-session memory increases usefulness over time.
🎯 Want to Build a GenAI Workforce Like This?
We help companies:
Identify high-impact use cases
Build multi-agent GenAI workflows in production
Improve existing tools with preview, eval, and control layers
🚀 Get a Free GenAI Strategy Audit
👉 Book your call
🎥 Watch the monday.com x LangGraph talk:
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FOOTNOTE
This article was generated by AI and should not be considered an original work. It may contain inaccuracies or hallucinated information. Please use it as an example only and replace the content with your writing.