AutoGenIntermediate⏱ ~35 minutes

Lead Scoring Agent

Stop guessing which leads to call first — let AI score and prioritize your pipeline

★ 4.6(27 reviews)•Sales & Marketing

Preview Code ↓

$129$258

✓ Full source code & documentation
✓ Commercial license included
✓ 30-day email support
✓ Free updates for 1 year

What You Get

Everything included in this template package

💻

Working Agent Code

3 AutoGen agents for enrichment, scoring, and routing

⚙️

Configuration File

Scoring weights, ICP criteria, and routing rules

💬

Prompt Templates

10 prompts for different lead analysis scenarios

📖

Setup Guide

CRM integration walkthrough with HubSpot examples

↔️

Example I/O

Sample lead data with scored and routed outputs

📐

Architecture Diagram

Lead flow pipeline diagram

😤

The Problem

Your sales team wastes 40% of their time on leads that never convert. Manual lead scoring is inconsistent — different reps evaluate the same lead differently. Meanwhile, hot leads go cold waiting in the queue.

✨

The Solution

This agent system automatically enriches leads with company data, analyzes behavioral signals and fit criteria, assigns a score from 1-100, and routes high-priority leads to the right rep with a one-paragraph context brief.

How It Works

Your AI crew handles the entire workflow

Input

Your task description, data, or trigger event

→

↓

AI Agents

Data EnricherPulls company size, industry, tech stack, and funding data from Clearbit

Score CalculatorEvaluates fit and intent signals against your ICP to assign a 1-100 score

Lead RouterAssigns scored leads to the right rep with context summaries

→

↓

Output

Structured results, reports, and actionable insights

Code Preview

Sample of the AutoGen agent setup

Preview only

crew.py

import autogen
from tools import ClearbitEnrich, HubSpotClient

enricher = autogen.AssistantAgent(
    name="DataEnricher",
    system_message="You enrich lead data with company"
        " info from Clearbit. Extract company size,"
        " industry, funding stage, and tech stack.",
    llm_config=llm_config
)

scorer = autogen.AssistantAgent(
    name="ScoreCalculator",
    system_message="You score leads 1-100 based on:"
        " company fit (40%), behavioral signals (30%),"
        " and timing indicators (30%).",
    llm_config=llm_config
)

router = autogen.AssistantAgent(
    name="LeadRouter",
    system_message="Route scored leads to the best rep"
        " based on territory, expertise, and capacity.",
    llm_config=llm_config
)

groupchat = autogen.GroupChat(
    agents=[enricher, scorer, router],
    messages=[], max_round=5
)

Example Input & Output

See what goes in and what comes out

Input

New lead: John Martinez
Email: john@techstartup.io
Company: TechStartup.io
Form fill: Requested demo of Enterprise plan
Pages visited: Pricing (3x), API docs, Case studies

Output

🎯 Lead Score: 87/100 (Hot)

📊 Breakdown:
• Company Fit: 92/100 — Series A startup, 45 employees, SaaS, matches ICP
• Behavioral Intent: 85/100 — Visited pricing 3x, read case studies, requested Enterprise demo
• Timing: 82/100 — Active evaluation phase, recent funding round

📋 Summary: VP Engineering at funded SaaS startup actively evaluating Enterprise tier. High urgency.

➡️ Routed to: Sarah (Enterprise AE, SaaS vertical)
⏰ SLA: Contact within 2 hours