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Generative AI > Google Gemini API > Performance Review Agent

Performance Review Agent

Author: Venkata Sudhakar

Annual and quarterly performance reviews at ShopMax India require managers to collate KPI data, peer feedback, and goal completion records before writing a structured review. This process is time-consuming and inconsistent across teams. An AI agent can compile the data and draft a balanced review narrative automatically.

This tutorial builds a Gemini ADK agent that pulls an employee's KPI scores, goal completion, and peer feedback rating, computes an overall performance score, and generates a structured review summary with strengths and development areas.

The below example shows a performance review generation agent for ShopMax India in a business context.

import asyncio
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 import types

# Employee performance data for Q1 2026
PERFORMANCE_DATA = {
    "EMP-201": {
        "name": "Priya Nair", "role": "Sales Executive", "team": "Bangalore",
        "kpis": {
            "Revenue Target Achievement": 112,  # % of target
            "Customer Satisfaction Score": 88,   # out of 100
            "New Accounts Opened": 95,           # % of target
            "Product Knowledge Score": 78,       # out of 100
        },
        "goals_completed_pct": 85,
        "peer_feedback_score": 4.2,  # out of 5
        "manager_rating": 4.0,
    },
}

def generate_review(employee_id: str) -> dict:
    data = PERFORMANCE_DATA.get(employee_id)
    if not data:
        return {"error": f"No performance data found for {employee_id}"}
    kpis = data["kpis"]
    kpi_avg = sum(min(v, 100) for v in kpis.values()) / len(kpis)
    # Weighted score: KPIs 40%, Goals 25%, Peer 20%, Manager 15%
    overall = (
        kpi_avg * 0.40 +
        data["goals_completed_pct"] * 0.25 +
        (data["peer_feedback_score"] / 5 * 100) * 0.20 +
        (data["manager_rating"] / 5 * 100) * 0.15
    )
    if overall >= 90:
        rating = "Exceeds Expectations"
    elif overall >= 75:
        rating = "Meets Expectations"
    elif overall >= 60:
        rating = "Partially Meets Expectations"
    else:
        rating = "Below Expectations"
    strengths = [k for k, v in kpis.items() if v >= 100]
    develop = [k for k, v in kpis.items() if v < 80]
    return {
        "employee": data["name"],
        "role": data["role"],
        "period": "Q1 2026",
        "overall_score": round(overall, 1),
        "rating": rating,
        "kpi_scores": kpis,
        "goals_completed_pct": data["goals_completed_pct"],
        "peer_feedback_score": data["peer_feedback_score"],
        "strengths": strengths,
        "development_areas": develop,
    }

review_tool = FunctionTool(func=generate_review)

agent = LlmAgent(
    name="performance_review_agent",
    model="gemini-2.0-flash",
    description="Generates structured performance review summaries for ShopMax India employees.",
    instruction=(
        "You are a performance management agent for ShopMax India. "
        "When asked to generate a review, call generate_review with the employee ID. "
        "Write a structured review covering: overall rating, key strengths (KPIs above target), "
        "development areas (KPIs below 80), and 2 specific recommendations for the next quarter."
    ),
    tools=[review_tool],
)

async def main():
    session_service = InMemorySessionService()
    runner = Runner(agent=agent, app_name="review_app", session_service=session_service)
    session = await session_service.create_session(app_name="review_app", user_id="mgr1")
    msg = types.Content(role="user", parts=[types.Part(
        text="Generate Q1 2026 performance review for EMP-201"
    )])
    async for event in runner.run_async(user_id="mgr1", session_id=session.id, new_message=msg):
        if event.is_final_response() and event.content:
            print(event.content.parts[0].text)

asyncio.run(main())

It gives the following output,

Performance Review - Q1 2026 | ShopMax India

Employee : Priya Nair | Sales Executive | Bangalore
Overall Score : 88.6 / 100
Rating        : Meets Expectations

KPI Summary:
  Revenue Target Achievement : 112%  [EXCEEDS]
  Customer Satisfaction Score: 88    [MEETS]
  New Accounts Opened        : 95%   [MEETS]
  Product Knowledge Score    : 78    [DEVELOP]

Goals Completed : 85%
Peer Feedback   : 4.2 / 5

Strengths:
  - Exceptional revenue performance, exceeding target by 12%.
  - Strong customer relationships reflected in satisfaction scores.

Development Areas:
  - Product Knowledge Score (78) is below the 80-point threshold.

Recommendations for Q2 2026:
  1. Complete the advanced product certification course by May 2026.
  2. Target 5 new enterprise accounts to build on Q1 momentum.

ShopMax India managers can generate draft reviews for their entire team in under 5 minutes before review season. The agent output serves as a structured starting point that the manager personalises, ensuring consistency in review quality and format across all departments and locations.

Send your comments, suggestions or queries regarding this site to [email protected].