How to Build a Multi-Agent Lead Qualification Workflow in Python¶

Inbound leads arrive faster than reps can read them, and the cost of mis-routing a hot lead is a lost deal. This recipe uses a Supervisor to score each lead and route it to the right play — hot leads get a personalized outreach draft, warm leads enter a nurture sequence, cold leads get a polite hold — so every lead gets the right next step within seconds.

Patterns used: Supervisor · Pipeline

Architecture¶

flowchart TD
    L[Inbound Lead] --> S[Supervisor\nscore hot / warm / cold]
    S -->|hot| A[AE\npersonalized outreach]
    S -->|warm| N[Nurture\n3-touch sequence]
    S -->|cold| C[Hold\npolite defer]
    A --> O[Routed Lead + Draft]
    N --> O
    C --> O

Implementation¶

pip install pyagent-patterns pyagent-providers

import asyncio
from pyagent_patterns.base import Agent
from pyagent_patterns.orchestration import Supervisor
from pyagent_providers import AnthropicLLM, OpenAILLM

fast_llm = OpenAILLM("gpt-4o-mini")
smart_llm = AnthropicLLM("claude-sonnet-4-20250514")

# ── Scorer: classifies the lead into a play ─────────────────────────────────────
scorer = Agent(
    "scorer", fast_llm,
    system_prompt=(
        "Score this inbound lead as exactly one of: hot, warm, cold. "
        "Hot = strong fit + buying signal (title, company size, intent). "
        "Warm = good fit, weak signal. Cold = poor fit or no signal. "
        "Reply with ONLY the label."
    ),
)

# ── Specialist plays per score ──────────────────────────────────────────────────
ae = Agent(
    "account_exec", smart_llm,
    system_prompt=(
        "Draft a 4-sentence personalized outreach email for this hot lead. Reference their role and "
        "the trigger that makes now the right time. End with one specific call-to-action."
    ),
)
nurture = Agent(
    "nurture", fast_llm,
    system_prompt=(
        "Add this warm lead to a 3-touch nurture sequence. Draft touch 1: a useful, no-ask message "
        "with a relevant resource. Outline touches 2 and 3 in one line each."
    ),
)
cold_hold = Agent(
    "cold_hold", fast_llm,
    system_prompt="Write a brief, polite holding reply and tag the lead for a 90-day re-check.",
)

crm = Supervisor(
    classifier=scorer,
    routes={"hot": ae, "warm": nurture, "cold": cold_hold},
    default_route="warm",
)

async def main():
    lead = "VP Eng at a 500-person fintech, downloaded the multi-agent whitepaper twice this week."
    result = await crm.run(lead)
    print(f"Routed to: {result.metadata['route_key']}\n")
    print(result.output)

asyncio.run(main())

Expected Output¶

Routed to: hot

Subject: multi-agent orchestration for fintech-scale reliability

Hi — saw your team is digging into multi-agent patterns (twice on the whitepaper this week!). At a
500-person fintech, the hard part is usually deterministic recovery and cost control across agents,
which is exactly what PyAgent's router + recovery layers handle. Worth a 20-minute walkthrough on how
teams your size cut model spend ~60%? — open to Thursday or Friday?

A warm lead would instead return touch 1 of a nurture sequence; a cold lead, a polite hold — all from the same routing call.

Customization¶

Enrich the lead before scoring¶

Prepend a Pipeline stage that pulls firmographics from your CRM so the scorer sees company size, tech stack, and prior touches:

from pyagent_patterns.orchestration import Pipeline
enrich = Agent("enrich", fast_llm, system_prompt="Add firmographics (size, industry, stack) from CRM context.")
qualified = Pipeline(stages=[enrich, scorer])  # feed the enriched lead into the supervisor

Add an SDR hand-off for hot leads¶

sdr_handoff = Agent("sdr", fast_llm, system_prompt="Write a Slack hand-off note + suggested meeting slots for the AE.")
crm.routes["hot"] = Pipeline(stages=[ae, sdr_handoff])

Weighted scoring with explicit criteria¶

scorer.system_prompt += " Weight: title 40%, company size 30%, intent signal 30%. Show the breakdown."

When to Use¶

Situation	Fit
Route each input to one of several mutually-exclusive plays	✅ Supervisor
Enrich → score as ordered stages	✅ Pipeline
Several scorers should vote on borderline leads	❌ Use Voting
The plan depends on the lead (variable subtasks)	❌ Use Orchestrator-Workers

Cost Profile¶

Stage	Typical model	Avg cost	Volume (1k leads/day)
Scorer	gpt-4o-mini	$0.0002	$6/mo
Hot play (AE draft)	claude-sonnet	$0.003	applies to ~20% of leads
Warm/cold plays	gpt-4o-mini	$0.0004	majority
Blended per lead	mix	~$0.0009	~$27/mo

Only hot leads hit the premium model, so cost scales with your hot-lead rate, not total volume.