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Generative AI > Google Gemini API > Training Recommendation Agent

Training Recommendation Agent

Author: Venkata Sudhakar

Investing in employee learning is critical for ShopMax India's growth. However, a generic training catalogue overwhelms employees and wastes L&D budget on irrelevant courses. A personalised recommendation agent can match each employee to the most relevant training based on their current skills, role requirements, and career aspirations.

This tutorial builds a Gemini ADK agent that analyses an employee's skill profile against their role's required competencies, identifies gaps, and recommends specific courses from ShopMax India's training catalogue with priority ordering.

The below example shows a training recommendation 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

# Training catalogue
COURSES = [
    {"id": "C001", "title": "Advanced Excel for Business Analysis",
     "skill": "Excel", "level": "Advanced", "duration_hrs": 8},
    {"id": "C002", "title": "SQL for Data Analysts",
     "skill": "SQL", "level": "Intermediate", "duration_hrs": 12},
    {"id": "C003", "title": "Python for Business Automation",
     "skill": "Python", "level": "Beginner", "duration_hrs": 16},
    {"id": "C004", "title": "Effective Presentation Skills",
     "skill": "Communication", "level": "Intermediate", "duration_hrs": 4},
    {"id": "C005", "title": "Data Storytelling with Power BI",
     "skill": "Data Visualisation", "level": "Intermediate", "duration_hrs": 10},
    {"id": "C006", "title": "GST and Taxation Fundamentals",
     "skill": "Taxation", "level": "Beginner", "duration_hrs": 6},
]

ROLE_REQUIREMENTS = {
    "Sales Executive": ["Excel", "Communication", "Data Visualisation"],
    "Data Analyst":    ["Python", "SQL", "Excel", "Data Visualisation"],
    "Finance Manager": ["Excel", "SQL", "Taxation"],
}

EMPLOYEES = {
    "EMP-401": {"name": "Rahul Gupta", "role": "Data Analyst",
                "current_skills": ["Excel", "SQL"]},
}

def recommend_training(employee_id: str) -> dict:
    emp = EMPLOYEES.get(employee_id)
    if not emp:
        return {"error": f"Employee {employee_id} not found"}
    required = ROLE_REQUIREMENTS.get(emp["role"], [])
    gaps = [s for s in required if s not in emp["current_skills"]]
    recommended = []
    for gap in gaps:
        for course in COURSES:
            if course["skill"] == gap:
                recommended.append({
                    "course_id": course["id"],
                    "title": course["title"],
                    "skill_addressed": gap,
                    "duration_hrs": course["duration_hrs"],
                    "priority": "HIGH",
                })
                break
    total_hrs = sum(r["duration_hrs"] for r in recommended)
    return {
        "employee": emp["name"],
        "role": emp["role"],
        "skill_gaps": gaps,
        "recommended_courses": recommended,
        "total_learning_hours": total_hrs,
        "skills_already_proficient": emp["current_skills"],
    }

train_tool = FunctionTool(func=recommend_training)

agent = LlmAgent(
    name="training_recommendation_agent",
    model="gemini-2.0-flash",
    description="Recommends personalised training courses for ShopMax India employees based on skill gaps.",
    instruction=(
        "You are an L&D agent for ShopMax India. "
        "When asked for training recommendations, call recommend_training with the employee ID. "
        "Present the skill gaps, recommended courses in priority order, and total learning hours required. "
        "Suggest a realistic completion timeline based on 2 hours of learning per week."
    ),
    tools=[train_tool],
)

async def main():
    session_service = InMemorySessionService()
    runner = Runner(agent=agent, app_name="train_app", session_service=session_service)
    session = await session_service.create_session(app_name="train_app", user_id="hr1")
    msg = types.Content(role="user", parts=[types.Part(
        text="What training should Rahul Gupta (EMP-401) complete?"
    )])
    async for event in runner.run_async(user_id="hr1", 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,

Training Recommendations - Rahul Gupta | Data Analyst

Current Skills  : Excel, SQL
Role Requires   : Python, SQL, Excel, Data Visualisation
Skill Gaps      : Python, Data Visualisation

Recommended Courses (Priority: HIGH):

1. Python for Business Automation  (C003)
   Skill: Python | Duration: 16 hrs | Level: Beginner

2. Data Storytelling with Power BI  (C005)
   Skill: Data Visualisation | Duration: 10 hrs | Level: Intermediate

Total Learning : 26 hours
At 2 hrs/week  : ~13 weeks to complete (by July 2026)

Recommend enrolling in Python first as it is foundational for data work.

ShopMax India's L&D team can run this agent for every employee before the annual training budget allocation. The output feeds a department-level skills gap dashboard, allowing the L&D manager to prioritise which courses to license, which to run in-house, and where to focus the training budget for maximum business impact.

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