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Generative AI > Google Gemini API > Territory Planning Agent

Territory Planning Agent

Author: Venkata Sudhakar

ShopMax India has a field sales team covering 12 cities. As the business grows, territory assignments made 2 years ago no longer reflect current market potential - some reps are overloaded while others have thin pipelines. A Territory Planning Agent analyses revenue potential, rep capacity, and account concentration to recommend balanced territory realignments.

This tutorial builds a Territory Planning Agent using Google ADK and Gemini 2.0 Flash. The agent evaluates each territory on market potential and rep workload, identifies imbalances, and recommends reassignments with projected revenue impact.

The below example shows the Territory Planning Agent analysing and rebalancing the West India sales territories.

from google.adk.agents import Agent
from google.adk.tools import FunctionTool
from google.adk.runners import Runner
from google.adk.sessions import InMemorySessionService
from google.genai.types import Content, Part

# Territory and rep data
TERRITORIES = [
    {"rep": "Vikram", "city": "Mumbai", "accounts": 42, "pipeline_lakh": 380,
     "market_potential_lakh": 500, "last_year_revenue_lakh": 210},
    {"rep": "Meena", "city": "Pune", "accounts": 18, "pipeline_lakh": 95,
     "market_potential_lakh": 280, "last_year_revenue_lakh": 65},
    {"rep": "Rajesh", "city": "Nashik", "accounts": 12, "pipeline_lakh": 45,
     "market_potential_lakh": 150, "last_year_revenue_lakh": 38},
    {"rep": "Anita", "city": "Surat", "accounts": 28, "pipeline_lakh": 160,
     "market_potential_lakh": 320, "last_year_revenue_lakh": 95},
    {"rep": "Kiran", "city": "Ahmedabad", "accounts": 35, "pipeline_lakh": 280,
     "market_potential_lakh": 420, "last_year_revenue_lakh": 155},
]

# Ideal accounts per rep
IDEAL_ACCOUNTS = 25
IDEAL_PIPELINE_LAKH = 200

def analyse_territory_balance(region: str) -> dict:
    """Analyse workload and market penetration for each territory."""
    analysis = []
    for t in TERRITORIES:
        penetration = round(t["last_year_revenue_lakh"] / t["market_potential_lakh"] * 100)
        workload = "overloaded" if t["accounts"] > IDEAL_ACCOUNTS * 1.3 else (
                   "underloaded" if t["accounts"] < IDEAL_ACCOUNTS * 0.7 else "balanced")
        untapped = t["market_potential_lakh"] - t["last_year_revenue_lakh"]
        analysis.append({
            "rep": t["rep"],
            "city": t["city"],
            "accounts": t["accounts"],
            "workload_status": workload,
            "market_penetration_pct": penetration,
            "untapped_potential_lakh": untapped,
            "pipeline_lakh": t["pipeline_lakh"]
        })
    total_potential = sum(t["market_potential_lakh"] for t in TERRITORIES)
    total_revenue = sum(t["last_year_revenue_lakh"] for t in TERRITORIES)
    return {
        "region": region,
        "territories": analysis,
        "total_market_potential_lakh": total_potential,
        "total_revenue_lakh": total_revenue,
        "overall_penetration_pct": round(total_revenue / total_potential * 100)
    }

def get_reassignment_options() -> list:
    """Suggest account moves to rebalance workload."""
    # Vikram is overloaded (42 accounts), Rajesh is underloaded (12 accounts)
    return [
        {
            "from_rep": "Vikram",
            "to_rep": "Meena",
            "accounts_to_move": 8,
            "rationale": "Meena has high Pune market potential (Rs 280 L) but only 18 accounts",
            "projected_revenue_uplift_lakh": 35
        },
        {
            "from_rep": "Kiran",
            "to_rep": "Rajesh",
            "accounts_to_move": 5,
            "rationale": "Nashik market is underpenetrated at 25% - needs more account focus",
            "projected_revenue_uplift_lakh": 22
        }
    ]

territory_tool = FunctionTool(func=analyse_territory_balance)
reassign_tool = FunctionTool(func=get_reassignment_options)

territory_agent = Agent(
    model="gemini-2.0-flash",
    name="territory_planning_agent",
    instruction="""
        You are a Territory Planning Agent for ShopMax India.
        When asked to review territory assignments:
        1. Analyse territory balance and market penetration
        2. Fetch reassignment recommendations
        3. Produce a territory review report:
           - Current state: workload and penetration per rep
           - Imbalances identified
           - Recommended reassignments with rationale
           - Projected revenue uplift from rebalancing
        Be specific about which accounts to move and why.
    """,
    tools=[territory_tool, reassign_tool]
)

session_service = InMemorySessionService()
runner = Runner(agent=territory_agent, app_name="territory", session_service=session_service)

session = session_service.create_session(app_name="territory", user_id="sales_ops")
message = Content(role="user", parts=[Part(text="Review and rebalance West India territory assignments for Q2 2026.")])
for event in runner.run(user_id="sales_ops", session_id=session.id, new_message=message):
    if event.is_final_response():
        print(event.content.parts[0].text)

It gives the following output,

TERRITORY REVIEW - WEST INDIA (Q2 2026)
========================================

Overall Market Penetration: 34% (Rs 563 L of Rs 1670 L potential)

Current Territory Status
Rep      City        Accounts  Workload     Penetration  Untapped
Vikram   Mumbai      42        OVERLOADED   42%          Rs 290 L
Kiran    Ahmedabad   35        OVERLOADED   37%          Rs 265 L
Anita    Surat       28        Balanced     30%          Rs 225 L
Meena    Pune        18        Underloaded  23%          Rs 215 L
Rajesh   Nashik      12        Underloaded  25%          Rs 112 L

Recommended Reassignments

1. Move 8 accounts: Vikram -> Meena
   Rationale: Pune has Rs 280 L potential with only 23% penetration.
   Meena has capacity and local market knowledge.
   Projected uplift: Rs 35 lakhs

2. Move 5 accounts: Kiran -> Rajesh
   Rationale: Nashik is underpenetrated at 25%. Rajesh needs
   more accounts to build pipeline momentum.
   Projected uplift: Rs 22 lakhs

Total Projected Revenue Uplift: Rs 57 lakhs
Implement by: 15-Apr-2026 before Q2 planning cycle closes.

The Territory Planning Agent can be run at the start of each quarter before quota-setting. Extend it to factor in travel time between accounts, rep seniority, and industry specialisation. Connect to your CRM to pull live account counts and pipeline values instead of static data.

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