# Inbound Lead Qualification Agent

> An inbound-lead agent that qualifies and scores leads against your ICP from real data, routes hot ones to sales, and never fabricates firmographics or intent.

- **Category:** Sales & SDR
- **Difficulty:** Intermediate
- **Setup time:** 40 min
- **Stacks:** Claude, LangGraph, OpenAI
- **Version:** 2.0.0 (updated 2026-06-21)
- **Source:** https://agent-kits.com/kit/inbound-lead-qualifier

## Overview

Qualifies inbound leads against your ICP, scoring fit and intent from real data.

Routes strong, sales-ready leads to reps and nurtures the rest.

Marks missing data and low-confidence leads honestly instead of inflating them.

Defensive: never fabricates firmographics or intent, drafts respectful follow-ups, and respects privacy and consent.

## When to use it

- You get more inbound leads than reps can qualify by hand.
- You have an ICP and real lead/enrichment data to score against.
- You want strong leads routed fast and weaker ones nurtured, with reasons.
- You want honest scoring that flags missing data rather than guessing.

## When not to use it

- You want it to invent firmographics or intent to inflate scores — it won't.
- You want aggressive automated outreach blasted without rep review.
- You have no ICP or data for it to qualify against.
- You need it to close or negotiate (it qualifies and routes; humans sell).

## Architecture

- **Lead intake** — Receives the inbound lead's submission and any context from the form or channel.
- **Legitimate enrichment** — Adds only legitimately available or public data, respecting privacy and consent rules.
- **ICP fit scoring** — Scores firmographic and role fit against the ideal customer profile from real data.
- **Intent scoring** — Assesses buying intent from behavior and stated need, without inferring intent that isn't evidenced.
- **Data-integrity guard** — Blocks fabricated firmographics, seniority, budget, or intent, and lowers confidence when data is missing.
- **Routing engine** — Routes strong leads to sales, nurtures others, and sends high-value or unusual leads to a human.
- **Outreach drafting** — Drafts respectful follow-ups for human review rather than firing aggressive automated messages.

## Files in this bundle

- `system-prompt.md` — the agent's system prompt
- `tools.json` — tool/function definitions the agent expects
- `workflow.md` — step-by-step run loop
- `.env.example` — environment variables to fill in
- `kit.json` — full structured metadata
- `starters/` — minimal starter snippets
- `LICENSE` — Apache-2.0 (code, configuration, and schema)
- `LICENSE-CONTENT` — CC BY 4.0 (specification text and documentation)
- `NOTICE` — attribution and trademark notice
- `run.py` — **runnable demo**: real agent loop with mock tools (`pip install anthropic`, set `ANTHROPIC_API_KEY`, `python run.py`)
- `agentaz.json` / `agentaz.schema.json` — AgentAz™ governance spec + schema

## Implementation notes

- Score only from real data; fabricated firmographics or intent misroute the pipeline and burn rep time on leads that were never qualified.
- Treat missing data as lower confidence, never as a guess to fill in — 'unknown' is a valid and important output.
- Draft outreach for human review and keep it respectful; aggressive automated blasting damages deliverability, brand, and consent compliance.
- Use only legitimately provided or public enrichment and honor consent and unsubscribe; don't scrape private data or use shady lists.
- Route enterprise, sensitive, or unusual leads to a person rather than auto-handling them.
- Make the scoring rationale explicit (which ICP criteria matched) so reps trust and can act on the routing.
- Reserve the strong model for ambiguous cases and outreach drafting; a cheaper model can do initial scoring on clear-cut leads.

## Variations

- **Basic — Lead scorer:** Scores inbound leads against your ICP from provided data and routes strong ones to sales. On submission.
- **Advanced — Honest qualification with guardrails:** Adds intent scoring, legitimate enrichment, data-integrity guards, missing-data handling, and respectful follow-up drafts for review.
- **Enterprise — Revenue qualification engine:** Adds CRM integration, configurable ICP and scoring models, routing rules, consent/compliance controls, and analytics across the inbound funnel.

## FAQ

**Does it make up company data to score leads?**

No — that's its core constraint. It scores fit and intent only from the lead's submission and legitimate enrichment. If company size, role, or budget is unknown, it marks it unknown and lowers confidence rather than inventing a firmographic.

**What happens when a lead is missing key info?**

It scores with low confidence, flags exactly what's missing, and typically drafts a friendly message to gather the context — instead of guessing the data and producing a misleading 'hot lead.'

**Will it spam leads with automated outreach?**

No. It drafts respectful follow-ups for a human to review and send, and won't fire aggressive, high-volume automated sequences. It honors consent and unsubscribe rules, because pushy blasting hurts deliverability and brand.

**How does it handle enterprise or sensitive leads?**

It routes high-value, sensitive, or unusual leads to a person rather than auto-handling them, so a human applies judgment where the stakes are higher.

**Can reps trust the scores?**

Yes, because every score comes with the specific ICP criteria it matched and the data it used. The rationale is explicit, and missing data is shown rather than hidden behind an inflated score.

**Is enrichment privacy-compliant?**

It uses only legitimately provided or public data and respects consent and applicable rules. It won't scrape private data or use questionable purchased lists.


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© 2026 AgentKits · www.agent-kits.com

**Open source.** Code, configuration, and the AgentAz™ schema are licensed under
**Apache-2.0**; specification text and documentation under **CC BY 4.0** (with
attribution to AgentKits, link to https://www.agent-kits.com). See LICENSE, LICENSE-CONTENT,
and NOTICE in this bundle. "AgentAz" and "AgentKits" are trademarks of AgentKits.

**Flagship reference blueprint.** View the source, AgentAz™ spec, and frozen v1.0 schema
in the public registry: https://github.com/agent-kits/agentaz
Original: https://agent-kits.com/kit/inbound-lead-qualifier
