Start with one specific outcome: “I want to scrape 50 qualified SaaS leads daily, enrich them with contact info, and push them to HubSpot for outreach.“

Map your workflow stages:

Input: Where does data come from? (LinkedIn Sales Navigator, Apollo, manual upload)
Enrichment: What info needs adding? (Email, phone, recent company news)
Logic: What rules apply? (exclude competitors, require Email format validation)
Action: What gets triggered? (CRM update, Slack notification, Email sequence start)
Validation: Where does human review happen? (before CRM push, after enrichment)

Action: Draw this on paper first. Use boxes for each stage and arrows for data flow. This becomes your blueprint.

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STEP

Prepare and clean your input data

Start small and clean:

Use 10 leads for your first test
Check each row manually: valid email format, correct company name, matches ICP
Remove duplicates using email as the unique identifier
Fill in missing fields where possible

If scraping data:

Run Apify scraper on one LinkedIn search result page only
Export to Google Sheets
Add columns for: validation_status, enrichment_status, human_review_needed

Action: Create a data template with required fields: first_name, last_name, email, company, linkedin_url, validation_status

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STEP

Set your data trigger

Choose your trigger type:

Option A: scheduled (recommended for beginners)

Set n8n to run every morning at 8 AM
Process 10-20 leads maximum per run
Easier to monitor and debug

Option B: real-time

Trigger when new row added to Google Sheets
Use webhook from your scraping tool
More complex but faster processing

Action: Start with scheduled. Set it to run once daily at a time when you can monitor results.

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STEP

Add your first agent

In n8n, drag your first agent.

An agent works thanks to 3 components:

LLM node (OpenAI GPT-4 or Claude)
Memory storage (Google Sheets or Make data store)
Web research tool (Perplexity or SerpAPI)

Let’s see how to add them to your “agent” block.

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STEP

Add a Large Language Model (LLM) node

Choose the right LLM for your task:

OpenAI GPT-4: Best for writing personalized emails
Claude: Better for data analysis and logic
Perplexity: Excellent for research tasks

Set up your LLM prompt template, copy ours below.

Action: Test your prompt with 3 different companies before connecting to the full workflow.

“You are a sales research assistant.

Input: Company name and LinkedIn URL

Task: Write a 2-sentence personalized opener for a cold email Output format: JSON with “opener” and “reasoning” fields 
Context: B2B SaaS outreach to decision-makers"

STEP

Add memory to your agent

Simple memory setup:

Use Google Sheets as your memory store
Create columns: lead_id, conversation_context, last_interaction, next_action
Update after each agent interaction

Advanced memory (optional):

Connect to Supabase for faster queries
Store conversation history and research results
Enable context carry-over between workflow runs

Action: Start with Google Sheets memory. Upgrade only when you process 100+ leads daily.

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STEP

Enable web research

Set up web research node:

Add Perplexity or SerpAPI to your Make workflow
Get your API key from the provider’s dashboard
Create research query template: “Find recent news and LinkedIn profile for [company name]“

Research query examples:

“What are [company name]’s main challenges in 2024?”
“Find [contact name]’s recent LinkedIn posts about [industry topic]”
“Get [company name]’s latest product launches or funding news”

Action: Test research queries manually first. Ensure results are relevant and accurate.

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STEP

Test your agentic workflow

Before going live, test every part. n8n offers a built-in chat mode where you can simulate inputs and see how your agent reacts.

Try giving it bad data. See what breaks. Then add loops or error conditions to make it more resilient. This is where the “agent” part really comes to life, it’s not just automation, it’s adaptation.

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STEP

Contact leads with multichannel outreach

Now that your AI agent has generated custom openers for each lead, it’s time to reach out. Duplicate this simple LGM workflow to contact them on Email + LinkedIn.

Then:

Name the column in your Google Sheet whatever you want (e.g., “opener”, “ai_opening_line”, “intro_copy”).
When you import into LGM, map that column to a Custom Attribute.
In your LGM campaign, reference it with the variable {{customAttribute1}} inside your email and LinkedIn message steps.

Result: Your automated outreach will feel personal, because each lead will receive a message with personalized context.

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Duplicate campaign