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🚀 I Built an AI Team That Finds Leads, Decision Makers & Writes Outreach While I Sleep
Vivek Shetye · 2026-06-24 · via DEV Community

Lead generation is one of those tasks everyone knows they should automate… but most teams still do it manually.

Every week the process looks something like this:

🔎 Search LinkedIn for companies

🌐 Visit dozens of websites

👤 Find the CTO, VP, or Director

📧 Hunt for contact information

✍️ Write personalized outreach emails

⏳ Lose half a day doing repetitive work

What if all of that happened automatically?

What if you woke up Monday morning to find:

✅ A list of companies matching your ICP

✅ Decision makers already identified

✅ Company research already completed

✅ Lead qualification scores calculated

✅ Personalized outreach drafts ready to send

That’s exactly what I built using Hermes Agent.


Full Video Walkthrough:


🤖 The Goal

I wanted a system where I could provide a simple campaign brief and have a team of AI agents handle the entire lead generation workflow.

Something like:

“Find SaaS startups with 10-200 employees that build AI developer tools. Identify decision makers and prepare personalized outreach.”

Instead of manually managing every step, a multi-agent workflow handles the process from start to finish.


🏗️ Architecture Overview

The pipeline consists of six specialized AI agents:

🎯 Orchestrator Agent

Acts like a sales manager.

Responsibilities:

  • Creates execution plans
  • Creates tasks
  • Assigns work to specialist agents
  • Tracks progress
  • Manages dependencies
  • Controls workflow phases

🔍 Prospector Agent

Finds companies that match your ICP.

Input:

  • Keywords
  • Industry
  • Company size
  • Target market

Output:

  • Qualified company list

Example keywords:

  • AI Developer Tools
  • LLM Infrastructure
  • DevOps Automation
  • AI Engineering Platforms

🌐 Scraper Agent

Researches every company discovered during prospecting.

Collects:

  • Products
  • Services
  • Company descriptions
  • Locations
  • Social profiles
  • Technology signals

All data gets enriched and stored automatically.


👥 Contact Finder Agent

Identifies the right people inside each company.

Targets:

  • CTOs
  • VPs
  • Directors
  • Founders

Then gathers available contact information from multiple sources.


✉️ Outreach Agent

Generates personalized outreach emails.

Instead of generic templates, it references:

  • Company initiatives
  • Product offerings
  • Technology stack
  • Industry positioning

Result:

Much more relevant outreach messages.


📊 Analyst Agent

Scores every lead against the Ideal Customer Profile (ICP).

Evaluation criteria:

  • Company size
  • Industry fit
  • Product relevance
  • Buying potential
  • Strategic alignment

Each company receives a qualification score between 0 and 1.

This helps prioritize outreach efforts.


🧠 Why Multi-Agent Systems Work So Well

Most people try to build lead generation using a single AI agent.

The problem?

One agent becomes responsible for:

  • Research
  • Scraping
  • Qualification
  • Personalization
  • Coordination

That quickly becomes messy.

Instead, I use specialist agents.

Each agent focuses on one responsibility only.

Benefits:

✅ Better task quality

✅ Easier debugging

✅ Better scalability

✅ Parallel execution

✅ Cleaner workflows


📋 Workflow State Machine

The workflow runs in phases:

Campaign Brief
      │
      ▼
 Prospecting
      │
      ▼
 Research & Enrichment
      │
      ▼
 Contact Discovery
      │
      ▼
 Outreach Generation
      │
      ▼
 Lead Qualification
      │
      ▼
 Campaign Report

The orchestrator only unlocks the next phase once the previous phase is completed successfully.

This prevents bad downstream data from contaminating later stages.


🗂️ Hermes KANBAN Board = Shared Agent Memory

One of my favorite parts of Hermes Agent is its Kanban workflow system.

The Kanban board acts as a shared coordination layer between agents.

Every agent can:

📖 Read task status

✍️ Update progress

🔄 Create follow-up tasks

🚦 Track dependencies

The orchestrator uses the board to understand:

  • What is complete
  • What is blocked
  • What should happen next

This creates a surprisingly robust autonomous workflow.


⚡ Running a Campaign

To launch a campaign I simply provide:

Product

What I’m selling

ICP

Who I want to target

Discovery Keywords

Where prospecting should begin

Goal

What outcome I want

Example:

Run a full B2B lead generation campaign for ShipMe Agent, an AI agent for QA and DevOps automation. 
ICP: AI SaaS startups, 10-200 employees. 
Keywords: AI Developer Tools, LLM DevOps Automation. 
Goal: 10 ranked qualified leads with decision-maker contacts and personalized outreach emails drafted

The orchestrator takes over from there.


📈 End Result

At the end of a run I receive all information organized in CSV files:

Companies

  • ICP matched companies
  • Enriched company data

Contacts

  • Decision makers
  • Contact information

Outreach

  • Personalized email drafts

Qualification

  • Lead scoring
  • Prioritized opportunities

Report

  • Campaign summary
  • Workflow results

All generated automatically from a single task.


🔥 Real Value

The biggest win isn’t saving a few minutes.

It’s eliminating repetitive work entirely.

Instead of spending hours every week:

❌ Searching

❌ Researching

❌ Copy-pasting

❌ Writing first drafts

I can focus on:

✅ Sales conversations

✅ Closing deals

✅ Improving campaigns

✅ Building relationships

The AI team handles the operational work.


🛠️ Resources

🔗 Agents SOUL.md files & Custom Hermes Skills: https://github.com/vivekshetye/hermes-lead-generation-pipeline


💬 What Would You Automate?

If you had a team of AI agents working for you 24/7, what business workflow would you automate first?

Lead generation?

Customer support?

Market research?

Content creation?

I’d love to hear what you’re building.