Autonomous Lead Generation & Qualification Agent
Multi-agent systems that prospect, personalize, listen, and forecast. These are the hardest problems in the booklet — they demand reasoning under uncertainty, real business-tool integration, and agents that must earn trust before a human hands them outreach or revenue decisions.
Background
Sales teams spend enormous time manually finding and qualifying leads across LinkedIn, company websites, and directories. Most of this work is repetitive pattern-matching against an Ideal Customer Profile (ICP) — exactly the kind of multi-step, tool-using task an agent should handle end to end.
Objective
Build a multi-agent system that takes an ICP definition, discovers matching companies/contacts from public sources, enriches each lead with firmographic data, scores and ranks them against the ICP with visible reasoning, and outputs a sales-ready, de-duplicated list.
Key Features
1. ICP intake (industry, company size, geography, signals of interest) 2. Prospect-discovery agent that searches public sources for candidate companies/contacts 3. Enrichment agent that adds firmographic data (size, industry, tech stack, funding/hiring signals) 4. Qualification/scoring agent that ranks each lead against the ICP with explicit reasoning 5. De-duplication and CRM-ready export (CSV / HubSpot-style format) 6. Re-runnable batch refresh so the list can be updated periodically
Expected Solution
A discovery → enrichment → scoring → export agent pipeline with clear hand-offs Caching/rate-limit handling so repeated runs don't hammer data sources A scoring rubric the agent applies consistently and explains per lead
Suggested Technology Stack
Backend: Python / FastAPI Orchestration: LangGraph Discovery/Search: Serper API / SerpAPI / public company directories Enrichment: Public company data sources (Crunchbase-style public pages, company websites) — no paid data purchase required for the demo Data handling: Pandas for dedup/scoring, CSV/HubSpot export format
Expected Outcomes
Significant reduction in manual prospecting hours for a sales team More consistent lead qualification than ad-hoc manual judgment A reusable discovery-enrichment-scoring agent pattern applicable beyond sales (e.g., vendor sourcing, partnership scouting)
Possible Use Cases
A B2B SaaS sales team building a target account list for a new region An agency prospecting on behalf of multiple clients A founder building an initial outbound list pre-seed/pre-Series A
Evaluation Parameters
Relevance of discovered leads against the stated ICP Accuracy and usefulness of enrichment data Quality and transparency of scoring reasoning Correctness of de-duplication and export format