Operational Declassification of ORSA Agent

@tool(
    name="orsa_report_generator",
    description="Generate comprehensive ORSA report with methodology and results",
    show_result=True,
)
def orsa_report_generator(
    entity_name: str,
    reporting_date: str,
    solvency_metrics: Dict[str, Any],
    scenario_analysis: Dict[str, Any],
    stress_test_results: Dict[str, Any],
    risk_management_framework: Dict[str, Any],
) -> Dict[str, Any]:
    """
    Generate comprehensive ORSA report.

    Args:
        entity_name: Name of the insurance entity
        reporting_date: Reporting date
        solvency_metrics: Current solvency metrics
        scenario_analysis: Scenario analysis results
        stress_test_results: Stress testing results
        risk_management_framework: Risk management framework details

    Returns:
        Dictionary containing ORSA report sections
    """
    report = {
        "orsa_report": {
            "entity_name": entity_name,
            "reporting_date": reporting_date,
            "report_type": "Own Risk and Solvency Assessment(ORSA)",
            "sections": {
                "executive_summary": {
                    "key_findings": [
                        f"Current solvency ratio: {solvency_metrics.get('solvency_ratio', 0)}%",
                        f"Capital adequacy level: {solvency_metrics.get('adequacy_level', 'Unknown')}",
                        f"Risk concentration: {max(solvency_metrics.get('risk_concentration', {}).values(), default=0):.1f}% in largest risk module",
                    ],
                    "recommendations": [
                        "Maintain current risk management framework",
                        "Monitor key risk indicators regularly",
                        "Review capital allocation strategy annually",
                    ],
                },
                "methodology": {
                    "approach": "Standard Formula with internal model elements",
                    "assumptions": "Based on current business plan and market conditions",
                    "validation": "Independent model validation completed",
                    "governance": "Board-approved ORSA policy and process",
                },
                "risk_assessment": {
                    "current_risk_profile": solvency_metrics,
                    "risk_concentration": solvency_metrics.get("risk_concentration", {}),
                    "risk_limits": risk_management_framework.get("risk_limits", {}),
                    "risk_monitoring": "Daily market risk, weekly credit risk, monthly comprehensive",
                },
                "capital_adequacy": {
                    "current_position": solvency_metrics,
                    "projections": scenario_analysis,
                    "stress_testing": stress_test_results,
                    "capital_management": "Risk-based dividend policy with stress testing",
                },
                "forward_looking_assessment": {
                    "scenario_analysis": scenario_analysis,
                    "business_plan_impact": "Growth strategies tested against solvency constraints",
                    "regulatory_developments": "Monitoring IFRS 17 and Solvency II developments",
                    "esg_integration": "Climate risk scenarios included in stress testing",
                },
            },
        }
    }
    return report

@tool(
    name="calculate_solvency_ratio",
    description="Calculate Solvency II ratio and capital adequacy metrics",
    show_result=True,
)
def calculate_solvency_ratio(
    own_funds: float,
    scr_amount: float,
    mcr_amount: float,
    risk_modules: Dict[str, float],
) -> Dict[str, Any]:
    """
    Calculate Solvency II ratio and capital adequacy metrics.

    Args:
        own_funds: Available own funds
        scr_amount: Solvency Capital Requirement
        mcr_amount: Minimum Capital Requirement
        risk_modules: Dictionary of risk module amounts

    Returns:
        Dictionary containing solvency metrics and analysis
    """
    solvency_ratio = (own_funds / scr_amount) * 100 if scr_amount > 0 else 0
    mcr_ratio = (own_funds / mcr_amount) * 100 if mcr_amount > 0 else 0
    total_risk = sum(risk_modules.values())
    risk_concentration = {
        k: (v / total_risk * 100) if total_risk > 0 else 0
        for k, v in risk_modules.items()
    }
    adequacy_level = (
        "Excellent"
        if solvency_ratio >= 150
        else(
            "Good"
            if solvency_ratio >= 130
            else "Adequate" if solvency_ratio >= 100 else "Inadequate"
        )
    )
    return {
        "solvency_ratio": round(solvency_ratio, 2),
        "mcr_ratio": round(mcr_ratio, 2),
        "adequacy_level": adequacy_level,
        "risk_concentration": risk_concentration,
        "capital_buffer": own_funds - scr_amount,
        "regulatory_margin": own_funds - mcr_amount,
    }

@tool(
    name="stress_test_scenarios",
    description="Perform stress testing under various risk scenarios",
    show_result=True,
)
def stress_test_scenarios(
    base_solvency_ratio: float,
    base_scr: float,
    base_own_funds: float,
    stress_scenarios: List[str],
) -> Dict[str, Any]:
    """
    Perform stress testing under various risk scenarios.

    Args:
        base_solvency_ratio: Base solvency ratio
        base_scr: Base SCR amount
        base_own_funds: Base own funds
        stress_scenarios: List of stress scenarios to test

    Returns:
        Dictionary containing stress test results
    """
    scenario_definitions = {
        "interest_rate_shock": {"description": "Parallel shift in interest rates", "scr_impact": 0.05, "own_funds_impact": -0.08},
        "equity_market_crash": {"description": "40% decline in equity markets", "scr_impact": 0.08, "own_funds_impact": -0.12},
        "mortality_stress": {"description": "Pandemic-like mortality increase", "scr_impact": 0.10, "own_funds_impact": -0.15},
        "lapse_shock": {"description": "Mass lapse event", "scr_impact": 0.04, "own_funds_impact": -0.06},
        "credit_default": {"description": "Sovereign default scenario", "scr_impact": 0.06, "own_funds_impact": -0.09},
        "operational_event": {"description": "Major operational failure", "scr_impact": 0.02, "own_funds_impact": -0.20},
    }
    stress_results = {}
    for scenario in stress_scenarios:
        if scenario in scenario_definitions:
            d = scenario_definitions[scenario]
            stressed_scr = base_scr * (1 + d["scr_impact"])
            stressed_own_funds = base_own_funds * (1 + d["own_funds_impact"])
            stressed_solvency_ratio = (stressed_own_funds / stressed_scr) * 100
            stress_results[scenario] = {
                "description": d["description"],
                "base_solvency_ratio": round(base_solvency_ratio, 2),
                "stressed_solvency_ratio": round(stressed_solvency_ratio, 2),
                "impact_on_solvency_ratio": round(stressed_solvency_ratio - base_solvency_ratio, 2),
                "stressed_scr": round(stressed_scr, 0),
                "stressed_own_funds": round(stressed_own_funds, 0),
                "adequacy_level": "Excellent" if stressed_solvency_ratio >= 150 else "Good" if stressed_solvency_ratio >= 130 else "Adequate" if stressed_solvency_ratio >= 100 else "Inadequate",
            }
    return {"base_metrics": {"solvency_ratio": base_solvency_ratio, "scr": base_scr, "own_funds": base_own_funds}, "stress_results": stress_results}