People Analytics

1. Load Input Data:
    - Read the provided CSV file containing employee-level data (e.g., 'novamind_employees.csv').
    - Columns can include: employee_id, name, age, gender, role, department, tenure_years,
      salary, performance_score, absences_last_year, training_hours, engagement_score,
      last_promotion_year, manager_feedback.

2. Run Predictive Models:
    - Turnover Risk: Predict the likelihood of an employee leaving based on tenure, salary
      competitiveness, engagement score, and last promotion year.
    - Absenteeism Risk: Predict future absenteeism using history of absences, engagement,
      and workload indicators.
    - Promotion Likelihood: Evaluate readiness for promotion based on performance, tenure,
      training hours, engagement, and manager feedback.
    - Engagement Risk: Identify employees and teams with low engagement scores or declining
      trends.

3. Consult Knowledge Base:
    - Retrieve relevant HR policies from the company Knowledge Base.
    - Examples of policies:
        - Engagement score <70 is considered "at risk" and requires HR intervention.
        - Promotion eligibility requires ≥2 years tenure, performance score ≥4, and
          engagement score ≥75.
        - Employees with >5 absences in a year should enter the "Attendance Improvement Program."
        - High turnover risk employees should have stay interviews, career development plans,
          or flexible work options.
    - Cross-check predictions against these policies to ensure recommendations align with
      company rules.

4. Generate Insights and Recommendations:
    - For each employee, provide:
        - Analytical Result: e.g., "Employee X has 82% turnover risk."
        - Policy-Aligned Action: e.g., "Per company policy, schedule a stay interview and
          review salary."
    - Aggregate results into:
        - Individual Employee Reports
        - Departmental Summaries (average engagement, turnover hotspots)
        - Company-Wide Trends

Notes:
    - The CSV file provides the raw employee data.
    - The Knowledge Base ensures all recommendations follow company policies.
    - The agent combines predictive analytics with policy-aligned HR guidance to produce
      actionable insights.