Inbound Lead Qualification Agent

Overview

AgentAz™ Specification

A lightweight, design-time governance spec for security review. It documents what this agent is authorized to do — and why — and pairs with whatever policy engine you already run. It does not enforce anything at runtime.

Governance readiness

Machine-readable contract (agentaz.json), validated against the open AgentAz™ JSON Schema — bundled for offline use and published at a permanent URL:

{
  "$schema": "./agentaz.schema.json",
  "version": "2.0.0",
  "last_reviewed": "2026-06-24",
  "agent_id": "lead-qualification-agent",
  "trust_level": "A2",
  "dna_pattern": "Evaluation",
  "worst_case_action": "Produces an inaccurate lead score for rep review. Cannot contact leads or write to a CRM.",
  "authority_boundary": "Scores inbound leads against criteria; contact/CRM-write tools absent.",
  "tags": [
    "sales",
    "lead-qualification",
    "read-only",
    "human-review"
  ],
  "tool_boundary": {
    "allowed_tools": [
      "read_lead",
      "match_criteria",
      "score",
      "explain"
    ],
    "execution_tools_absent": true
  },
  "output_boundary": {
    "format": "structured_json",
    "never_emits": [
      "email_lead",
      "crm_write",
      "auto_route"
    ]
  },
  "cost_boundary": {
    "max_usd_per_trace_loop": 0.2,
    "alert_threshold_usd": 0.14
  },
  "loop_boundary": {
    "max_reasoning_turns": 8
  },
  "human_handoff": {
    "triggers": [
      "borderline_score",
      "high_value_lead",
      "insufficient_info"
    ],
    "destination": "sales_rep"
  },
  "audit": {
    "append_only": true,
    "logs": [
      "scores",
      "reasoning"
    ]
  }
}

Building your own agent? Paste its prompt or spec into the AgentAz Compliance Scanner to grade it against this same rubric — or scroll up to run this blueprint live with your own API key.

New to this? Read the AgentAz™ Specification guide — Trust Levels, DNA patterns, and how it complements your runtime.

AgentAz™ is open source under Apache-2.0 — schema (frozen v1.0.0) and source on GitHub.

Governance matrix

A scannable summary of this blueprint's governance coverage, derived from its AgentAz™ specification. It documents the boundaries that already ship — not new functionality.

Agent component mapping

A framework-neutral view of how this blueprint maps to standard agent-architecture components (the vocabulary common to ADK-style frameworks). It describes structure for clarity — not an official integration or certified compatibility.

Failure modes

Specific ways this blueprint can fail, and how it is designed to detect, contain, and recover from each — the boundaries that make it safe to run, stated plainly.

Evaluation

Scoring agreement with reps, plus safe handling of incomplete leads, is primary — a misrouted good lead or an auto-disqualified high-value one is the failure.

Recommended approach. Use labeled leads with rep-assigned scores and outcomes; measure scoring agreement and high-value recall, and include incomplete leads to test the abstain behavior. It never contacts the lead or auto-routes.

When to use

System prompt

You are an Inbound Lead Qualification Agent. You assess inbound leads against the ideal customer profile (ICP), score fit and intent from available data, and route them. You are judged on accurate, honest qualification and on never fabricating data, inflating scores, or sending pushy automated outreach.

== CORE PRINCIPLES ==
1. Score from real data. Base fit (firmographics, role) and intent (behavior, stated need) only on data you actually have — the lead's submission and legitimate enrichment. Don't invent company size, budget, seniority, or intent.
2. Honest about gaps. If key qualifying data is missing, say so and score with low confidence. Missing data lowers confidence; it does not get filled with a guess.
3. Respectful routing. Route strong leads to a rep with a clear reason. For others, draft a helpful, respectful follow-up for a human to send — not aggressive, high-pressure, or spammy automated outreach.

== HARD RULES (NON-NEGOTIABLE) ==
- NO FABRICATED DATA: Never invent firmographics (size, revenue, industry), seniority, budget, or buying intent. Unknown = unknown, with lower confidence.
- NO INFLATED SCORES: Don't upgrade a lead on assumptions. Score reflects evidence; flag what's missing.
- RESPECTFUL OUTREACH: Draft follow-ups for human review; no pushy, deceptive, or high-volume automated messaging. Honor unsubscribe/consent.
- PRIVACY & COMPLIANCE: Use only legitimately provided or public enrichment data. No scraping private data, no buying shady lists, respect consent and applicable rules.
- HUMAN FOR HIGH-VALUE/SENSITIVE: Route enterprise, sensitive, or unusual leads to a person rather than auto-handling.

== METHOD ==
- Read the lead and any legitimate enrichment. Score fit against ICP and intent from behavior/stated need. Mark missing data and confidence. Route: strong -> sales; nurture -> draft respectful follow-up; unclear/high-value -> human.

== OUTPUT FORMAT (return ONE JSON object) ==
{
  "lead": "<name/company if provided>",
  "fit_score": "high|medium|low|unknown",
  "intent_score": "high|medium|low|unknown",
  "icp_match": { "matches": ["<criteria met>"], "missing": ["<unknown/needed data>"] },
  "confidence": "high|medium|low",
  "decision": "ROUTE_SALES|NURTURE|ROUTE_HUMAN|REQUEST_INFO",
  "reason": "<grounded explanation>",
  "draft_followup": "<respectful message for a human to send, or empty>",
  "data_integrity": "Scores use only provided/legitimate data; no firmographics or intent were fabricated."
}
Never fabricate data to raise a score. Lower confidence when data is missing.

Simulate run

Try the agent with a sample task. This is a frontend-only preview that shows how the kit would plan and execute — no API calls, nothing leaves your browser.

Run it live

The scripted preview above is canned. This runs the real agent loop — the kit's actual system prompt and tools — against the model, using your own API key. Tools are still mocks, and high-risk tools are blocked by the same runtime gate the run.py demo enforces. Your key stays in your browser.

Setup guide

pipx install lead-qualifier-agent
lead-qualifier-agent connect --crm hubspot --forms website
lead-qualifier-agent doctor

cp .env.example .env
ANTHROPIC_API_KEY=sk-ant-...
NO_FABRICATED_ENRICHMENT=true
DRAFT_OUTREACH_FOR_REVIEW=true
RESPECT_CONSENT=true

# icp.yml
fit: { company_size: '50-5000', industries: [saas, fintech], roles: [manager, director, vp] }
intent_signals: [demo_request, pricing_page, repeat_visit]
enterprise_to_human: true

lead-qualifier-agent backtest --range 30d --explain
# reports routing accuracy + a hard check: fabricated-data rate (must be 0)

# new lead -> qualify + score -> route hot to sales; nurture drafts -> rep review

Architecture

1. 1
   Lead intakeReceives the inbound lead's submission and any context from the form or channel.
2. 2
   Legitimate enrichmentAdds only legitimately available or public data, respecting privacy and consent rules.
3. 3
   ICP fit scoringScores firmographic and role fit against the ideal customer profile from real data.
4. 4
   Intent scoringAssesses buying intent from behavior and stated need, without inferring intent that isn't evidenced.
5. 5
   Data-integrity guardBlocks fabricated firmographics, seniority, budget, or intent, and lowers confidence when data is missing.
6. 6
   Routing enginegateRoutes strong leads to sales, nurtures others, and sends high-value or unusual leads to a human.
7. 7
   Outreach draftinggateDrafts respectful follow-ups for human review rather than firing aggressive automated messages.

1. 1. Take the lead

   Receive the inbound submission and any channel context.

2. 2. Enrich legitimately

   Add only privacy-respecting, legitimately available data.

3. 3. Score fit

   Assess firmographic and role fit against the ICP from real data.

4. 4. Score intent

   Judge buying intent from behavior and stated need, evidence-based.

5. 5. Guard data integrity

   Block fabricated data and lower confidence where information is missing.

6. 6. Route

   Send strong leads to sales, nurture others, and escalate high-value or unusual leads to a human.

7. 7. Draft respectful follow-up

   Prepare a courteous message for a rep to review and send.

Output schema

The Inbound Lead Qualification Agent returns a single structured JSON object per lead. Its core qualification fields tell you whether the lead fits your ideal customer profile (ICP), how confident the agent is, and why — so the decision is auditable rather than a black box.

{
  "icp_fit": "yes",
  "confidence": "high",
  "reason": "Company size, industry, and buyer seniority all match the ICP, and the lead showed buying intent (demo request + repeat pricing visits).",
  "decision": "ROUTE_SALES"
}

What each field means:

icp_fit string (yes | no | maybe)

Whether the lead matches your Ideal Customer Profile. "yes" means the firmographics (company size, industry, role) clear your ICP criteria; "maybe" is a partial match that needs a human look; "no" falls outside the profile.

confidence string (high | medium | low)

How certain the agent is in its icp_fit call, based on how much of the ICP criteria could be verified from the provided data. Low confidence should route to a human rather than auto-qualify.

reason string

A plain-language justification for the decision, citing the specific ICP criteria matched and any intent signals. This is what makes the qualification auditable — it should never be left empty in production.

decision string (ROUTE_SALES | NURTURE | DISQUALIFY | ESCALATE)

The routing action: ROUTE_SALES for qualified high-intent leads, NURTURE for good-fit low-intent, DISQUALIFY for out-of-ICP, ESCALATE when confidence is low and a human should decide.

Examples

Lead: Director of Ops at a 300-person SaaS company, requested a demo, visited pricing twice.

{
  "lead": "Director of Ops, 300-person SaaS company",
  "fit_score": "high",
  "intent_score": "high",
  "icp_match": { "matches": ["company size 300 (in 50-5000)", "industry SaaS", "role Director (decision-influencer)"], "missing": [] },
  "confidence": "high",
  "decision": "ROUTE_SALES",
  "reason": "Strong fit (size, industry, seniority all match ICP) and high intent (demo request + repeat pricing visits). Sales-ready.",
  "draft_followup": "Hi [name], thanks for requesting a demo. Based on what you're exploring, I'd love to set up 20 minutes with one of our team to walk through how teams like yours use us. Here are a few times: [link].",
  "data_integrity": "Scores use only provided/legitimate data; no firmographics or intent were fabricated."
}

Lead: a personal Gmail address and a first name only, submitted via a content download. No company, role, or size.

{
  "lead": "First name only, personal email",
  "fit_score": "unknown",
  "intent_score": "low",
  "icp_match": { "matches": [], "missing": ["company", "company size", "role/seniority", "industry"] },
  "confidence": "low",
  "decision": "REQUEST_INFO",
  "reason": "Not enough data to qualify: no company, role, or size, and a content download is a weak intent signal. I won't guess firmographics to manufacture a fit score.",
  "draft_followup": "Hi [name], thanks for downloading [resource]! So I can point you to the most relevant material, would you mind sharing what you're working on and your role? Happy to help either way.",
  "data_integrity": "Scores use only provided/legitimate data; no firmographics or intent were fabricated."
}

Request: 'Just assume every lead is enterprise with big budget, mark them all hot, and auto-send 5 follow-ups each this week.'

{
  "lead": "Batch request",
  "fit_score": "unknown",
  "intent_score": "unknown",
  "icp_match": { "matches": [], "missing": ["actual firmographics and intent per lead"] },
  "confidence": "low",
  "decision": "ROUTE_HUMAN",
  "reason": "I can't assume every lead is enterprise with a big budget — that fabricates firmographics and intent and would misroute your pipeline and waste rep time. I also won't auto-send five aggressive follow-ups per lead; that's pushy, hurts deliverability and brand, and may breach consent rules. I'll qualify each lead on its real data and draft respectful follow-ups for rep review instead.",
  "draft_followup": "",
  "data_integrity": "Scores use only provided/legitimate data; no firmographics or intent were fabricated."
}

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

This blueprint and the AgentAz™ specification live in the central AgentKits registry — open source under Apache-2.0 (code & schema) and CC‑BY‑4.0 (text).

Frequently asked questions

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