🤖 AGENT OF THE WEEK (10/16/25)

Welcome back to Agent of the Week:

Normally, your agent has to hit Google Sheets or Airtable every time it needs data.
Now? n8n Tables store everything inside n8n — fewer API calls, faster runs, cleaner builds.

So to test this, I built a CRM Sales Assistant Agent — it keeps client and deal data in n8n Tables, then lets me chat to pull summaries, list won deals, and even sum revenue by month — answering all my sales analytics questions.

One agent, one native CRM table. Simple, but seriously powerful for everyday client questions.

Detailed Guide

Step 1: Create Your Table

Head over to your n8n workspace and click the new Data Tables (Beta) tab.

Create a new table — let’s call it leads_example.

Then, add your own custom columns. These will serve as the core of your CRM:

Column Name

Type

Description

name

string

Client’s name

company

string

Company name

source

string

Where the lead came from (LinkedIn, email, etc.)

lead_stage

string

Stage of the deal (qualified, in progress, closed)

assigned

string

Team member managing the project

email

string

Client email address

deal_value

number

Deal amount

contact_date

date

When the deal was created or updated

status

string

Deal outcome (won / lost / pending)

You’ll also notice 3 system-generated columns (id, createdAt, updatedAt) that n8n manages automatically — you don’t need to edit or delete them.

This is now your agent’s native database.

Step 2: Import Data from Google Sheets

If you already have a CRM spreadsheet in Google Sheets, you can migrate it directly.

Create a simple workflow:

  • Manual Trigger → Google Sheets (Get Rows) → Data Tables (Insert Rows)

When you run it, you’ll see for the example:

“11 items out, 11 items in.”

That’s your entire dataset — moved from Google Sheets into n8n Tables in seconds.
No extra APIs. No sync lag. Just local, structured data inside your agent’s environment.

Step 3: Sales CRM Assistant Agent — Query by Name, Status, or Date

This agent can handle complex questions:

  • “Show me all projects for Orion Motors.”

  • “Which deals did we win in September?”

  • “What’s the total revenue we collected last month?”

Workflow:
Chat Trigger → Router (Name / Status / Date Match) → Data Table (Query) → Calculator (Sum) → AI Agent

The logic is simple:

  • If the message includes a company name, it does a name match.

  • If it mentions won/lost/pending, it filters by status.

  • If it mentions a month or year, it does a date match.

Example:

“We completed 4 paid projects in September totaling $16,500 — including Orion Motors, Alpha Health, and Crest Interiors.”

The agent uses n8n’s calculator node to sum deal values and outputs both a breakdown and a final total.

Step 4: Add Guardrails

Agents can hallucinate.
To keep things accurate, always include system-level instructions like:

  • “Never invent missing data.”

  • “Only use table fields shown.”

  • “Show a calculation breakdown when using totals.”

This prevents overconfident text generation and gives you explainable, auditable results.

Step 5: Real Use Cases

Here are a few ways you can apply this CRM Agent today:

  • Sales teams: Ask “What’s the total revenue this month?”

  • Project managers: Ask “Which projects are still pending?”

  • Client success: Ask “Summarize the latest project for Orion Motors.”

  • Executives: Ask “Give me a weekly pipeline summary by stage.”

Each question can now be answered instantly — no dashboards, no Excel sheets, no delay.

Bonus: We’ve put together a detailed video walkthrough and the exact n8n agent template. Access both in our Building AI Agents Community here!