TAI Labs
TAI Lightning Lesson · Free

n8n for
product
teams

Automate workflows, prototype faster. Stop waiting on engineering.
Session
Live · one sitting
You bring
A stuck workflow you'd love to automate
Led by
Aki WijesundaraAki Wijesundara
Manu JayawardanaManu Jayawardana
TAI Labs
Overview · what we'll cover

Seven moves. One live demo.

01

What is n8n?

And why product teams should care.

02

Where n8n fits

In your product workflow.

03

High-leverage use cases

Onboarding, internal tools, feedback loops, ops.

04

Avoiding product debt

Designing automations the right way.

05

Collaborating with engineers

Using n8n as a communication tool.

06

Testing & evolving safely

Iterate without breaking things.

Then · live demo: a multi-agent system in n8n · AI-powered lead qualification and routing, with an orchestrator that delegates to specialist sub-agents.
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Foundations · what is n8n?

Open-source workflow automation. Think Zapier, but you own it.

01

Self-hosted or cloud

Full control over your infrastructure. Run it on your own server or use the hosted cloud.

Yours
02

Visual drag & drop

No code required for simple flows. Drop nodes, wire them up, execute.

Visual
03

400+ integrations

Slack, Notion, Sheets, CRMs, databases, LLMs, webhooks. Out of the box.

Batteries in
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The compare

The same shape as Zapier or Make. With none of the ceiling.

Zapier · Make
  • Platform limits on complexity.
  • Cannot self-host.
  • Opaque execution logs.
  • Pay per action, per user, per zap.
n8n
  • Full control: host on your own infrastructure.
  • Inspect every execution · every node's inputs and outputs.
  • Complex branching logic. No node cap. No workflow cap.
  • Drop into JavaScript or Python when the UI hits its limit.
The upgrade: visual for the 80% of a workflow that's obvious, code for the 20% that isn't. Zapier stops at obvious.
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The problem

Simple workflows should not require engineering time.

The pain today
Typeform → Slack → Sheet
↓
Write a ticket
↓
Wait a sprint
↓
Delivered late
With n8n
PM builds it in n8n
↓
Test with real data
↓
Ship the same afternoon
Not about replacing engineers. About unblocking product iteration for the stuff that does not need to live in your codebase.
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Architecture · three layers

Your product work lives on three layers. n8n owns two.

01Engineering
Core product. User-facing features, data models, APIs, core business logic. This is the codebase. Do not put n8n here.
02n8n
Product operations. Onboarding sequences, internal alerts, reporting, feedback routing, cross-tool syncs. The plumbing that keeps the product running.
03Also n8n
Experimentation. Testing new flows, prototyping before committing eng resources, quick validation. Fail cheap and fast without a PR.
The support: n8n covers discovery, delivery, and iteration. It does not replace your engineers. It stops you from needing them for every small operational task.
The rule that keeps you safe

If it breaks, no customer notices. That's the n8n line.

The biggest risk is not building too little. It is building too much in the wrong place. If a broken workflow would leak into a user's experience, it belongs in your codebase. If it would only frustrate your team, n8n is the right place. That one question · "who notices when this breaks?" · draws the line.

TAI Labs
Use cases · where the leverage is

Four categories that pay the most, every time.

01

Onboarding flows

Trigger welcome sequences on signup. Auto-assign onboarding tasks. Notify CS when a high-value user activates.

User lifecycle
02

Internal tools

Connect Slack, Notion, Sheets, CRMs. A Slack post becomes a Jira ticket plus a roadmap row. Unified notification systems.

Cross-tool
03

Feedback loops

Route NPS responses by product area. Flag churn signals from usage drops. Aggregate feedback into a single view.

Product signal
04

Ops automation

Weekly reporting from multiple sources. Data syncs between CRM and analytics. Auto-post sprint summaries and alerts.

Ops
All of it without touching your product codebase. No PR, no deploy, no waiting.
TAI Labs
Best practices · avoid product debt

Automations without product debt.

Belongs in n8n
  • Internal workflows.
  • Ops automation.
  • Experimental flows.
  • If it breaks, no customer notices.
Belongs in your codebase
  • Anything user-facing.
  • Anything touching your data model.
  • Anything that needs to scale.
  • Anything with SLA requirements.
The danger zone: when a "quick n8n fix" becomes a permanent workaround that nobody documents, nobody owns, and everybody depends on. Treat n8n workflows like code · name them clearly, document what they do, assign ownership, review periodically.
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Collaboration · n8n as a communication tool

Show working logic. Not a spec.

Before n8n
PM writes a spec
↓
Engineer interprets
↓
Back-and-forth on edge cases
↓
Build · Test · Ship
After n8n
PM builds the workflow
↓
Shows working logic to engineering
↓
Hands off a clean, testable spec
Show

Don't tell

Working logic instead of written specs.

Focus

Engineers stay on product code

Not ops tasks.

Ship

Shorter conversations

Logic is already visible.

TAI Labs
Safety · evolve without breaking

Treat workflows with the same discipline as product features.

1

Start with manual triggers

Do not automate until you have tested with real data.

2

Use execution logs

Every run is inspectable. See exactly what data flowed through each node.

3

Version your workflows

Export JSON, track changes in Git. You need rollback ability.

4

Iterate gradually

Manual trigger → scheduled trigger → webhook trigger as confidence grows.

5

Set up error handling

n8n has error workflows. Route failures to Slack so someone knows when things break.

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Live demo · multi-agent system in n8n

One orchestrator agent. Two specialist sub-agents.

AI Agent · Orchestrator
  • Receives chat messages from users.
  • Decides which sub-agent to delegate to.
  • Powered by OpenAI Chat Model + Simple Memory.
  • Routes tasks to the right specialist.
Two sub-agents
  • Deep Research Agent. OpenAI Chat Model with web search + Structured Output Parser. Returns clean, structured research.
  • Google Sheet Agent. OpenAI Chat Model + Google Sheets tool integration. Loads research results into a database.
Why multi-agent? Each agent has a single responsibility. Swap out the research logic without touching the storage agent. Add new agents without rebuilding the whole flow.
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Live demo · architecture

The orchestrator delegates. Each sub-agent has its own model, memory, and tools.

💬 Chat message received
↓
🤖 AI Agent · Orchestrator
Chat Model Simple Memory Tools
↙↘
🔍 Deep Research Agent
OpenAI + Web Search Structured Output
📄 Google Sheet Agent
OpenAI Chat Google Sheets Tool
↓
✅ Research stored in Google Sheets
TAI Labs
Summary · what to remember

Five rules for n8n discipline.

01
n8n is a product tool, not just an ops tool. Treat it that way.
02
Know what belongs in n8n vs your codebase. The line matters. "If it breaks, no customer notices" is the test.
03
Use it to prototype, automate, and close feedback loops fast. Then decide what earns a codebase entry.
04
Multi-agent architectures in n8n let you build sophisticated AI workflows without custom code.
05
Ship faster without creating long-term complexity · the whole point.
TAI Labs
What you do this week

Pick one workflow. Ship it in n8n. Skip the ticket.

01
Name one recurring workflow you dread. The one that eats your week. Support triage. NPS routing. Feedback aggregation. One line. Facts only.
02
Ask the "who notices when this breaks?" test. If nobody outside your team, it belongs in n8n. If a real user does, it belongs in the codebase. Do not blur the line. This is the rule that keeps n8n safe.
03
Build the first version with a manual trigger. No schedule, no webhook. Run it by hand ten times. Watch the execution log for each node. Only automate what you've watched behave.
04
Ship it. Then version it in Git. Export the workflow JSON. Give it an owner, a name, a description. Treat the .json like code. Nameless workflows die in the danger zone.
The receipt: a screenshot of the workflow + the "who notices when this breaks?" answer + a note on what it saved. Email us. We open the AI PM Bootcamp by reviewing three of these live.
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The habit to take with you

Before you file a ticket, ask if n8n can do it.

Half of what PMs put on the eng backlog is plumbing. Data syncs, alerts, cross-tool routing, reporting. The kind of work engineers hate to do and PMs hate to wait for. n8n is where that work stops piling up. The PMs whose products ship on time are the PMs who never file the plumbing ticket in the first place.

Next

AI PM Bootcamp and Certificate

Nine weeks. Ship AI-native product operations end to end. Cohorts start monthly.

Where this leads
Now

Questions in chat

Post the workflow you'd love to hand off to n8n. Aki or Manu will name the shape and how to build it.

Open floor
Later

The workflow

Ship one this week. Email the screenshot and the "who notices?" answer. We reply with a short audio review.

The receipt
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