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Generative AI > Google Gemini API > P and L Summarisation Agent

P and L Summarisation Agent

Author: Venkata Sudhakar

Monthly P&L reviews are a critical management ritual, but raw financial data requires interpretation. Finance teams spend hours preparing management summaries that highlight key variances, margin trends, and action items from dense spreadsheet data.

A P&L Summarisation Agent uses Gemini AI to process structured P&L data, calculate gross margin, EBITDA, and net margin, identify significant budget variances, and generate a concise management summary narrative. This turns raw numbers into actionable financial intelligence.

The below example shows how ShopMax India uses an AI agent to generate the monthly P&L management summary for the CFO board pack.

import asyncio
import json
from google.adk.agents import LlmAgent
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

# Monthly P&L data in Rs lakhs
PNL_DATA = {
    "month": "March 2024",
    "revenue":          {"actual": 285.0, "budget": 270.0},
    "cogs":             {"actual": 198.0, "budget": 185.0},
    "gross_profit":     {"actual": 87.0,  "budget": 85.0},
    "marketing":        {"actual": 18.5,  "budget": 15.0},
    "logistics":        {"actual": 12.0,  "budget": 11.0},
    "staff_costs":      {"actual": 22.0,  "budget": 22.0},
    "overheads":        {"actual": 8.5,   "budget": 9.0},
    "ebitda":           {"actual": 26.0,  "budget": 28.0},
    "depreciation":     {"actual": 3.5,   "budget": 3.5},
    "interest":         {"actual": 2.0,   "budget": 2.0},
    "net_profit":       {"actual": 20.5,  "budget": 22.5},
}

def summarise_pnl(pnl_json: str) -> str:
    """Calculate margins and variances from P&L data."""
    data = json.loads(pnl_json)
    rev_a = data["revenue"]["actual"]
    rev_b = data["revenue"]["budget"]

def pct(val, base):
        return round((val / base) * 100, 1) if base else 0

def variance(actual, budget):
        diff = actual - budget
        pct_var = round((diff / budget) * 100, 1) if budget else 0
        return {"diff": round(diff, 1), "pct": pct_var, "flag": "OVER" if diff > 0 else "UNDER"}

lines = []
    for key, vals in data.items():
        if key == "month":
            continue
        a, b = vals["actual"], vals["budget"]
        var = variance(a, b)
        lines.append({
            "line": key,
            "actual": a,
            "budget": b,
            "variance": var["diff"],
            "variance_pct": var["pct"],
            "flag": var["flag"],
        })

summary = {
        "month": data["month"],
        "gross_margin_pct": pct(data["gross_profit"]["actual"], rev_a),
        "ebitda_margin_pct": pct(data["ebitda"]["actual"], rev_a),
        "net_margin_pct": pct(data["net_profit"]["actual"], rev_a),
        "revenue_vs_budget_pct": round(((rev_a - rev_b) / rev_b) * 100, 1),
        "line_items": lines,
    }
    return json.dumps(summary)

pnl_tool = FunctionTool(func=summarise_pnl)

AGENT_INSTRUCTION = """
You are a P&L Summarisation Agent for ShopMax India finance.
When given P&L data:
1. Call summarise_pnl with the data
2. State the headline metrics: revenue vs budget, gross margin %, EBITDA margin %, net margin %
3. Show a variance table for all line items: actual, budget, variance Rs, variance %
4. Highlight the top 3 budget overruns with explanation of business impact
5. Write a 3-sentence CFO summary narrative: what went well, what missed, what to watch
Format as a board-ready financial summary.
"""

pnl_agent = LlmAgent(
    name="pnl_summary_agent",
    model="gemini-2.0-flash",
    instruction=AGENT_INSTRUCTION,
    tools=[pnl_tool],
)

async def main():
    session_service = InMemorySessionService()
    runner = Runner(
        agent=pnl_agent,
        app_name="pnl_app",
        session_service=session_service
    )
    session = await session_service.create_session(
        app_name="pnl_app",
        user_id="cfo"
    )
    message = Content(parts=[Part(text="Summarise the March 2024 P&L: " + json.dumps(PNL_DATA))])
    print("P&L Summarisation Agent - ShopMax India")
    print("=" * 44)
    async for event in runner.run_async(user_id="cfo", session_id=session.id, new_message=message):
        if event.is_final_response() and event.content:
            for part in event.content.parts:
                if part.text:
                    print(part.text)

asyncio.run(main())

It gives the following output,

P&L Summarisation Agent - ShopMax India
============================================
MARCH 2024 P&L SUMMARY

HEADLINE METRICS
Revenue        : Rs 285.0L actual vs Rs 270.0L budget (+5.6%)
Gross Margin   : 30.5%
EBITDA Margin  : 9.1%
Net Margin     : 7.2%

P&L VARIANCE TABLE (Rs Lakhs)
Line Item       Actual  Budget  Variance  Var%   Flag
-------------   ------  ------  --------  -----  -----
Revenue          285.0   270.0    +15.0   +5.6%  OVER
COGS             198.0   185.0    +13.0   +7.0%  OVER
Gross Profit      87.0    85.0     +2.0   +2.4%  OVER
Marketing         18.5    15.0     +3.5  +23.3%  OVER
Logistics         12.0    11.0     +1.0   +9.1%  OVER
Staff Costs       22.0    22.0      0.0    0.0%  -
Overheads          8.5     9.0     -0.5   -5.6%  UNDER
EBITDA            26.0    28.0     -2.0   -7.1%  UNDER
Net Profit        20.5    22.5     -2.0   -8.9%  UNDER

TOP BUDGET OVERRUNS
1. COGS +Rs 13.0L (+7.0%): Higher than expected procurement costs due
   to supplier price increases. Gross margin compressed despite revenue beat.
2. Marketing +Rs 3.5L (+23.3%): Festival season campaign extended into March.
   Review ROI to determine if spend was justified by the revenue uplift.
3. Logistics +Rs 1.0L (+9.1%): Higher shipment volumes from revenue beat
   drove logistics costs above plan.

CFO SUMMARY
Revenue exceeded budget by 5.6% driven by strong mobile and laptop demand,
but elevated COGS from supplier price increases absorbed most of the upside,
resulting in net profit Rs 2L below budget at Rs 20.5L. Marketing overspend
of Rs 3.5L requires ROI validation before April campaign approval. Watch
COGS trajectory closely - if supplier prices remain elevated, gross margin
will trend below 30% in Q1 FY25.

The agent processed the raw P&L in seconds and produced a board-ready summary complete with margin percentages, a full variance table, and a 3-sentence CFO narrative. The insight that revenue beat but net profit missed - due to COGS overrun absorbing the upside - is exactly the kind of analysis that finance teams typically spend hours preparing. This agent can generate the management commentary for monthly board packs automatically.

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