Architecture Deep-Dive
LLM-Agentic Workflow for Lead Qualification
Complexity
High (Frontier)
Primary Stack
Python, LangChain
Status
Production Active
The Problem
High-intent leads were arriving as unstructured submissions and emails, forcing manual CRM entry and delaying sales response by up to 48 hours.
The Agentic Solution
We engineered a multi-agent orchestration layer with extraction, validation, and routing stages so data quality remains high before any downstream automation fires.
[System Architecture Diagram: Orchestration Flow]
Implementation Logic
from langchain.agents import AgentExecutor
from pydantic import BaseModel, Field
class LeadSchema(BaseModel):
email: str
intent_score: float = Field(description="Score from 0 to 1")
tech_stack: list[str]
async def process_inbound_email(payload: str) -> LeadSchema:
agent = create_openai_functions_agent(llm, tools=[web_search])
executor = AgentExecutor(agent=agent, tools=tools)
result = await executor.ainvoke({"input": f"Parse this: {payload}"})
return LeadSchema.parse_raw(result["output"])Impact Metrics
96.4%
Data Accuracy
-85%
Processing Latency