Capstone 5: Multi-Agent Lakehouse Governance Team

+---------------------------------------------------------------------------------+
|                           PROJECT 5 ARCHITECTURE                                |
+---------------------------------------------------------------------------------+
|                             [TEAM SUPERVISOR]                                   |
|                             (Orchestrates tasks)                                |
|                               /       |       \                                 |
|                              v        v        v                                |
|                        [CATALOGER] [SECOPS] [FINOPS]                            |
|                         Documents   Audits   Optimizes                          |
|                         Schemas     PII/RBAC Storage/Compute                    |
|                              \        |        /                                |
|                               v       v       v                                 |
|                         [UNIFIED AUDIT REPORT]                                  |
+---------------------------------------------------------------------------------+

Objective

Build a multi-agent system using LangGraph where three specialized AI agents collaborate to review a newly provisioned lakehouse table:

1. Cataloger Agent: Generates column documentation and lineage tags.
2. SecOps Auditor: Checks for unmasked PII, SSNs, credit cards, or excessive read privileges.
3. FinOps Optimizer: Recommends partitioning columns, clustering keys, and Z-ORDER strategies to minimize cloud warehouse queries.

Complete Multi-Agent Workflow Implementation

from typing import TypedDict, List
from langgraph.graph import StateGraph, END
from openai import OpenAI

client = OpenAI()

class GovernanceState(TypedDict):
    table_name: str
    ddl: str
    catalog_notes: str
    security_verdict: str
    finops_recommendations: str
    final_report: str

# Agent 1: Cataloger
def cataloger_node(state: GovernanceState) -> dict:
    prompt = f"Document this table schema concisely:
{state['ddl']}"
    res = client.chat.completions.create(model="gpt-4o-mini", messages=[{"role": "user", "content": prompt}])
    return {"catalog_notes": res.choices[0].message.content}

# Agent 2: SecOps Auditor
def secops_node(state: GovernanceState) -> dict:
    prompt = f"Audit this table DDL for PII risks (emails, phones, credit cards):
{state['ddl']}"
    res = client.chat.completions.create(model="gpt-4o-mini", messages=[{"role": "user", "content": prompt}])
    return {"security_verdict": res.choices[0].message.content}

# Agent 3: FinOps Optimizer
def finops_node(state: GovernanceState) -> dict:
    prompt = f"Recommend clustering and partitioning strategies for this table:
{state['ddl']}"
    res = client.chat.completions.create(model="gpt-4o-mini", messages=[{"role": "user", "content": prompt}])
    return {"finops_recommendations": res.choices[0].message.content}

# Agent 4: Supervisor Synthesizer
def supervisor_node(state: GovernanceState) -> dict:
    report = f"""
# Data Lakehouse Governance Report: {state['table name']}

### 1. Catalog & Documentation
{state['catalog_notes']}

### 2. SecOps & Compliance
{state['security_verdict']}

### 3. FinOps & Performance Recommendations
{state['finops_recommendations']}
"""
    return {"final_report": report}

# Build LangGraph
workflow = StateGraph(GovernanceState)
workflow.add_node("cataloger", cataloger_node)
workflow.add_node("secops", secops_node)
workflow.add_node("finops", finops_node)
workflow.add_node("supervisor", supervisor_node)

workflow.set_entry_point("cataloger")
workflow.add_edge("cataloger", "secops")
workflow.add_edge("secops", "finops")
workflow.add_edge("finops", "supervisor")
workflow.add_edge("supervisor", END)

app = workflow.compile()
print("Multi-Agent Governance Graph successfully compiled!")