Loan Covenant Monitoring Agent

loan_covenant_monitoring/
├── main.py                    # Entry point with async main() function
├── team.py                    # Team assembly (coordinate mode + leader)
├── agents.py                  # 3 specialist agent factory functions
├── schemas.py                 # Pydantic output schemas
├── tools.py                   # Custom financial calculation tools
├── task_builder.py            # Task description builder
├── upsonic_configs.json       # Upsonic CLI configuration
├── data/
│   ├── loan_agreement.txt     # Synthetic loan agreement (5 covenants)
│   └── financial_data.json    # Synthetic Q4 2025 financials
└── README.md                  # Quick start guide

# Required: Set Anthropic API key (default model uses Anthropic)
export ANTHROPIC_API_KEY="your-api-key"

# Or if using OpenAI models:
export OPENAI_API_KEY="your-api-key"

# Install dependencies from upsonic_configs.json
upsonic install

# Add a package
upsonic add <package> <section>
upsonic add pandas api

# Remove a package
upsonic remove <package> <section>
upsonic remove streamlit api

uv run main.py

upsonic run

curl -X POST http://localhost:8000/call \
  -H "Content-Type: application/json" \
  -d '{
    "inputs": {
        "company_name": "GlobalTech Manufacturing Inc.",
        "reporting_period": "Q4 2025",
        "loan_agreement_path": "data/loan_agreement.txt",
        "financial_data_path": "data/financial_data.json",
        "focus_areas": ["Leverage ratio trending toward covenant limit"]
    }
  }'

{
  "inputs": {
    "company_name": "GlobalTech Manufacturing Inc.",
    "reporting_period": "Q4 2025",
    "loan_agreement_path": "data/loan_agreement.txt",
    "financial_data_path": "data/financial_data.json"
  }
}

{
  "company_name": "GlobalTech Manufacturing Inc.",
  "reporting_period": "Q4 2025",
  "report": {
    "company_name": "GlobalTech Manufacturing Inc.",
    "reporting_period": "Q4 2025",
    "compliance_results": [
      {"covenant_name": "Leverage Ratio", "status": "near_breach", "actual_value": 3.62, "threshold": 3.75},
      {"covenant_name": "Interest Coverage Ratio", "status": "compliant", "actual_value": 2.79, "threshold": 2.0},
      {"covenant_name": "Current Ratio", "status": "compliant", "actual_value": 1.35, "threshold": 1.2},
      {"covenant_name": "Debt Service Coverage Ratio", "status": "breached", "actual_value": 1.15, "threshold": 1.25},
      {"covenant_name": "Tangible Net Worth", "status": "compliant", "actual_value": 70000000, "threshold": 50000000}
    ],
    "risk_assessment": {
      "overall_risk_score": 45,
      "risk_level": "high",
      "breached_count": 1,
      "near_breach_count": 1,
      "compliant_count": 3
    },
    "executive_summary": "...",
    "next_steps": ["..."]
  },
  "monitoring_completed": true
}

"""
Main entry point for Loan Covenant Monitoring Agent.

This module provides the async entry point "main" that coordinates
the comprehensive loan covenant monitoring process.
"""

from __future__ import annotations

import asyncio
import json
from pathlib import Path
from typing import Dict, Any, Optional, List

from upsonic import Task

try:
    from .team import create_covenant_monitoring_team
    from .task_builder import build_covenant_monitoring_task
    from .schemas import CovenantMonitoringReport
except ImportError:
    from team import create_covenant_monitoring_team
    from task_builder import build_covenant_monitoring_task
    from schemas import CovenantMonitoringReport


async def main(inputs: Dict[str, Any]) -> Dict[str, Any]:
    """
    Main async function for loan covenant monitoring.

    Args:
        inputs: Dictionary containing:
            - company_name: Name of the borrower company (required)
            - reporting_period: Period being monitored, e.g. "Q4 2025" (required)
            - loan_agreement_path: Path to loan agreement text file (required)
            - financial_data_path: Path to financial data JSON file (required)
            - focus_areas: Optional list of priority focus areas
            - enable_memory: Whether to enable memory persistence (default: True)
            - model: Optional model identifier (default: "anthropic/claude-sonnet-4-5")

    Returns:
        Dictionary containing the covenant monitoring report and metadata.
    """
    company_name: str = inputs.get("company_name", "")
    if not company_name:
        raise ValueError("company_name is required in inputs")

    reporting_period: str = inputs.get("reporting_period", "")
    if not reporting_period:
        raise ValueError("reporting_period is required in inputs")

    loan_agreement_path: str = inputs.get("loan_agreement_path", "")
    if not loan_agreement_path:
        raise ValueError("loan_agreement_path is required in inputs")

    financial_data_path: str = inputs.get("financial_data_path", "")
    if not financial_data_path:
        raise ValueError("financial_data_path is required in inputs")

    focus_areas: Optional[List[str]] = inputs.get("focus_areas")
    enable_memory: bool = inputs.get("enable_memory", True)
    model: str = inputs.get("model", "anthropic/claude-sonnet-4-5")
    print_flag: Optional[bool] = inputs.get("print")

    team = create_covenant_monitoring_team(
        model=model,
        enable_memory=enable_memory,
        print=print_flag,
    )

    task_description: str = build_covenant_monitoring_task(
        company_name=company_name,
        reporting_period=reporting_period,
        focus_areas=focus_areas,
    )

    task: Task = Task(
        task_description,
        context=[loan_agreement_path, financial_data_path],
    )

    result = await team.do_async(task)

    if isinstance(result, CovenantMonitoringReport):
        report_dict: Dict[str, Any] = result.model_dump()
    else:
        report_dict = {"raw_output": str(result)}

    return {
        "company_name": company_name,
        "reporting_period": reporting_period,
        "report": report_dict,
        "monitoring_completed": True,
    }


if __name__ == "__main__":
    import sys

    script_dir: Path = Path(__file__).parent

    test_inputs: Dict[str, Any] = {
        "company_name": "GlobalTech Manufacturing Inc.",
        "reporting_period": "Q4 2025",
        "loan_agreement_path": str(script_dir / "data" / "loan_agreement.txt"),
        "financial_data_path": str(script_dir / "data" / "financial_data.json"),
        "focus_areas": [
            "Leverage ratio trending toward covenant limit",
            "Debt service capacity under current cash flow",
        ],
        "enable_memory": False,
        "model": "anthropic/claude-sonnet-4-5",
        "print": True,
    }

    if len(sys.argv) > 1:
        try:
            with open(sys.argv[1], "r") as f:
                test_inputs = json.load(f)
        except Exception as e:
            print(f"Error loading JSON file: {e}")
            print("Using default test inputs")

    try:
        result = asyncio.run(main(test_inputs))

        print("\n" + "=" * 80)
        print("Loan Covenant Monitoring Report - Completed")
        print("=" * 80)
        print(f"\nCompany: {result['company_name']}")
        print(f"Period:  {result['reporting_period']}")
        print(f"Status:  {'Completed' if result.get('monitoring_completed') else 'Failed'}")

        report: Dict[str, Any] = result.get("report", {})

        if "executive_summary" in report:
            print(f"\n--- Executive Summary ---\n{report['executive_summary']}")

        if "risk_assessment" in report:
            risk: Dict[str, Any] = report["risk_assessment"]
            print("\n--- Risk Assessment ---")
            print(f"  Risk Score : {risk.get('overall_risk_score', 'N/A')} / 100")
            print(f"  Risk Level : {risk.get('risk_level', 'N/A')}")
            print(
                f"  Breached: {risk.get('breached_count', 0)} | "
                f"Near Breach: {risk.get('near_breach_count', 0)} | "
                f"Compliant: {risk.get('compliant_count', 0)}"
            )

        if "compliance_results" in report:
            print("\n--- Covenant Compliance Details ---")
            print("-" * 60)
            status_symbols: Dict[str, str] = {
                "compliant": "[OK]",
                "near_breach": "[!!]",
                "breached": "[XX]",
            }
            for covenant in report["compliance_results"]:
                symbol: str = status_symbols.get(covenant.get("status", ""), "[??]")
                print(
                    f"  {symbol} {covenant.get('covenant_name', 'Unknown')}: "
                    f"{covenant.get('actual_value', 'N/A')} vs "
                    f"{covenant.get('threshold', 'N/A')} "
                    f"(headroom: {covenant.get('headroom_percentage', 'N/A')}%)"
                )

        if "next_steps" in report:
            print("\n--- Recommended Next Steps ---")
            for i, step in enumerate(report["next_steps"], 1):
                print(f"  {i}. {step}")

        print("\n" + "=" * 80)
        print("\nFull report (JSON):")
        print(json.dumps(report, indent=2, default=str))

    except Exception as e:
        print(f"\nError during execution: {e}")
        import traceback
        traceback.print_exc()
        sys.exit(1)

"""
Team assembly and configuration for covenant monitoring.

Creates a coordinated Team with a leader agent that orchestrates
covenant extraction, financial calculation, and compliance assessment.
"""

from __future__ import annotations

from typing import Optional

from upsonic import Agent, Team
from upsonic.storage import Memory, InMemoryStorage

try:
    from .agents import (
        create_covenant_extractor_agent,
        create_financial_calculator_agent,
        create_risk_assessor_agent,
    )
    from .schemas import CovenantMonitoringReport
except ImportError:
    from agents import (
        create_covenant_extractor_agent,
        create_financial_calculator_agent,
        create_risk_assessor_agent,
    )
    from schemas import CovenantMonitoringReport


def create_covenant_monitoring_team(
    model: str = "anthropic/claude-sonnet-4-5",
    enable_memory: bool = True,
    print: Optional[bool] = None,
) -> Team:
    """Create the coordinated Team for end-to-end covenant monitoring.

    Uses coordinate mode with a leader agent that delegates to three
    specialist agents: covenant extractor, financial calculator, and
    risk assessor.

    Args:
        model: Model identifier for the leader/coordinator agent.
        enable_memory: Whether to enable in-memory session persistence.
        print: If True, do() prints; if False, print_do() does not. Respects UPSONIC_AGENT_PRINT env.

    Returns:
        Configured Team instance ready for covenant monitoring tasks.
    """
    leader: Agent = Agent(
        model=model,
        name="Covenant Monitoring Coordinator",
        role="Head of Loan Portfolio Monitoring",
        goal=(
            "Coordinate the end-to-end covenant monitoring process by delegating "
            "to specialist agents and synthesizing a comprehensive compliance report"
        ),
        system_prompt=(
            "You coordinate the loan covenant monitoring workflow.\n\n"
            "WORKFLOW:\n"
            "1. Delegate to Covenant Extractor: Have them parse the loan agreement and "
            "extract all covenant definitions with thresholds for the applicable period\n"
            "2. Delegate to Financial Calculator: Have them calculate all required ratios "
            "using the financial data and their calculation tools\n"
            "3. Delegate to Risk Assessor: Have them evaluate compliance for each covenant "
            "using the extracted thresholds and calculated ratios\n"
            "4. Synthesize all findings into the final structured report\n\n"
            "IMPORTANT:\n"
            "- Ensure each covenant definition is matched with its corresponding ratio\n"
            "- Pass the correct threshold and constraint type to the risk assessor\n"
            "- The final report must cover every covenant's compliance status\n"
            "- Include an overall risk assessment with a numerical score and risk level\n"
            "- Provide actionable next steps, especially for any breached or near-breach covenants"
        ),
    )

    memory: Optional[Memory] = None
    if enable_memory:
        memory = Memory(
            storage=InMemoryStorage(),
            session_id="covenant_monitoring_session",
            full_session_memory=True,
        )

    covenant_extractor: Agent = create_covenant_extractor_agent()
    financial_calculator: Agent = create_financial_calculator_agent()
    risk_assessor: Agent = create_risk_assessor_agent()

    team: Team = Team(
        entities=[covenant_extractor, financial_calculator, risk_assessor],
        mode="coordinate",
        leader=leader,
        response_format=CovenantMonitoringReport,
        memory=memory,
        name="Loan Covenant Monitoring Team",
        print=print,
    )

    return team

"""
Specialized agent creation functions for covenant monitoring.

Each function creates an Agent with a specific domain expertise:
- Covenant extraction from legal documents
- Financial ratio calculation using custom tools
- Compliance assessment and risk evaluation
"""

from __future__ import annotations

from typing import List, Callable

from upsonic import Agent

try:
    from .tools import (
        calculate_leverage_ratio,
        calculate_interest_coverage_ratio,
        calculate_current_ratio,
        calculate_debt_service_coverage_ratio,
        calculate_tangible_net_worth,
        evaluate_covenant_compliance,
    )
except ImportError:
    from tools import (
        calculate_leverage_ratio,
        calculate_interest_coverage_ratio,
        calculate_current_ratio,
        calculate_debt_service_coverage_ratio,
        calculate_tangible_net_worth,
        evaluate_covenant_compliance,
    )


def create_covenant_extractor_agent(model: str = "openai/gpt-4o-mini") -> Agent:
    """Create a specialist agent for extracting covenant definitions from loan agreements.

    Args:
        model: Model identifier for the agent.

    Returns:
        Configured Agent for covenant extraction.
    """
    return Agent(
        model=model,
        name="Covenant Extractor",
        role="Legal Document Analyst specializing in commercial loan agreements",
        goal=(
            "Extract and structure every financial covenant definition from the loan "
            "agreement, including precise thresholds, formulas, constraint types, and "
            "testing frequencies"
        ),
        system_prompt=(
            "You are a specialist in analyzing commercial loan agreements and credit "
            "facility documentation. Your task is to:\n"
            "- Identify every financial covenant in the provided agreement text\n"
            "- Extract the exact numerical threshold for the applicable period\n"
            "- Determine the formula specified for each covenant\n"
            "- Classify each as 'maximum' (must not exceed) or 'minimum' (must not fall below)\n"
            "- Note the testing frequency (quarterly TTM, point-in-time, etc.)\n\n"
            "CRITICAL: Only extract values explicitly stated in the document. Never infer "
            "or estimate thresholds. Use the exact step-down schedule applicable to the "
            "reporting period being analyzed."
        ),
        education="JD in Corporate Law, CFA Charterholder",
        work_experience="15 years in leveraged finance documentation and loan agreement analysis",
        tool_call_limit=5,
    )


def create_financial_calculator_agent(model: str = "openai/gpt-4o-mini") -> Agent:
    """Create a specialist agent for computing financial ratios using calculation tools.

    Args:
        model: Model identifier for the agent.

    Returns:
        Configured Agent with financial calculation tools.
    """
    financial_tools: List[Callable[..., dict]] = [
        calculate_leverage_ratio,
        calculate_interest_coverage_ratio,
        calculate_current_ratio,
        calculate_debt_service_coverage_ratio,
        calculate_tangible_net_worth,
    ]

    return Agent(
        model=model,
        name="Financial Calculator",
        role="Quantitative Financial Analyst",
        goal=(
            "Calculate all required financial ratios and metrics from raw financial "
            "data using the provided calculation tools, producing an audit-ready trail"
        ),
        system_prompt=(
            "You are a quantitative analyst responsible for computing financial ratios "
            "needed for covenant compliance testing.\n\n"
            "RULES:\n"
            "1. ALWAYS use the provided calculation tools. NEVER compute ratios manually.\n"
            "2. Use exact figures from the financial data. Do not round or adjust inputs.\n"
            "3. For each ratio, identify the correct input values from the financial data "
            "and call the corresponding tool.\n"
            "4. Report all results with their component values for audit trail.\n\n"
            "Available tools:\n"
            "- calculate_leverage_ratio(total_debt, ebitda)\n"
            "- calculate_interest_coverage_ratio(ebit, interest_expense)\n"
            "- calculate_current_ratio(current_assets, current_liabilities)\n"
            "- calculate_debt_service_coverage_ratio(net_operating_income, total_debt_service)\n"
            "- calculate_tangible_net_worth(total_assets, total_liabilities, intangible_assets)"
        ),
        education="MS in Financial Engineering, FRM Certification",
        work_experience="10 years in credit risk analytics and financial modeling",
        tools=financial_tools,
        tool_call_limit=15,
    )


def create_risk_assessor_agent(model: str = "openai/gpt-4o-mini") -> Agent:
    """Create a specialist agent for evaluating covenant compliance and risk.

    Args:
        model: Model identifier for the agent.

    Returns:
        Configured Agent with the compliance evaluation tool.
    """
    compliance_tools: List[Callable[..., dict]] = [evaluate_covenant_compliance]

    return Agent(
        model=model,
        name="Risk Assessor",
        role="Credit Risk Officer",
        goal=(
            "Evaluate covenant compliance status for each covenant using the evaluation "
            "tool, calculate overall risk score, and provide actionable recommendations"
        ),
        system_prompt=(
            "You are a senior credit risk officer evaluating loan covenant compliance.\n\n"
            "PROCESS:\n"
            "1. For each covenant, call evaluate_covenant_compliance with:\n"
            "   - covenant_name: the covenant's name\n"
            "   - actual_value: the calculated ratio/metric value\n"
            "   - threshold: the covenant threshold from the agreement\n"
            "   - constraint_type: 'maximum' or 'minimum'\n"
            "2. Analyze headroom percentage for each to assess comfort level\n"
            "3. Compute overall risk score using this methodology:\n"
            "   - Start at 0 (no risk)\n"
            "   - Add 30 points per breached covenant\n"
            "   - Add 15 points per near-breach covenant\n"
            "   - Risk levels: 0-20=Low, 21-40=Moderate, 41-70=High, 71-100=Critical\n"
            "4. Provide specific, actionable recommendations for any covenant that is "
            "near breach or breached, considering both immediate remediation and "
            "structural solutions\n\n"
            "Consider cure provisions and grace periods when formulating recommendations."
        ),
        education="MBA in Finance, PRM Certification",
        work_experience="12 years in commercial banking credit risk and portfolio monitoring",
        tools=compliance_tools,
        tool_call_limit=15,
    )

"""
Custom financial calculation tools for covenant monitoring.

Provides standalone calculation and compliance evaluation functions
used by the financial calculator and risk assessor agents.
"""

from __future__ import annotations

from typing import Dict, Any


def calculate_leverage_ratio(total_debt: float, ebitda: float) -> Dict[str, Any]:
    """Calculate the Leverage Ratio (Total Debt / EBITDA).

    Args:
        total_debt: Total outstanding funded debt in dollars.
        ebitda: Earnings Before Interest, Taxes, Depreciation, and Amortization in dollars.

    Returns:
        Dictionary with ratio value, formula used, and input components.
    """
    if ebitda <= 0:
        return {
            "ratio_name": "Leverage Ratio",
            "value": float("inf"),
            "formula": "Total Funded Debt / EBITDA",
            "components": {"total_debt": total_debt, "ebitda": ebitda},
            "warning": "EBITDA is zero or negative; ratio is undefined",
        }

    ratio: float = round(total_debt / ebitda, 4)
    return {
        "ratio_name": "Leverage Ratio",
        "value": ratio,
        "formula": "Total Funded Debt / EBITDA",
        "components": {"total_debt": total_debt, "ebitda": ebitda},
    }


def calculate_interest_coverage_ratio(ebit: float, interest_expense: float) -> Dict[str, Any]:
    """Calculate the Interest Coverage Ratio (EBIT / Interest Expense).

    Args:
        ebit: Earnings Before Interest and Taxes in dollars.
        interest_expense: Total interest expense in dollars.

    Returns:
        Dictionary with ratio value, formula used, and input components.
    """
    if interest_expense <= 0:
        return {
            "ratio_name": "Interest Coverage Ratio",
            "value": float("inf"),
            "formula": "EBIT / Interest Expense",
            "components": {"ebit": ebit, "interest_expense": interest_expense},
            "warning": "Interest expense is zero or negative; ratio is undefined",
        }

    ratio: float = round(ebit / interest_expense, 4)
    return {
        "ratio_name": "Interest Coverage Ratio",
        "value": ratio,
        "formula": "EBIT / Interest Expense",
        "components": {"ebit": ebit, "interest_expense": interest_expense},
    }


def calculate_current_ratio(current_assets: float, current_liabilities: float) -> Dict[str, Any]:
    """Calculate the Current Ratio (Current Assets / Current Liabilities).

    Args:
        current_assets: Total current assets in dollars.
        current_liabilities: Total current liabilities in dollars.

    Returns:
        Dictionary with ratio value, formula used, and input components.
    """
    if current_liabilities <= 0:
        return {
            "ratio_name": "Current Ratio",
            "value": float("inf"),
            "formula": "Current Assets / Current Liabilities",
            "components": {"current_assets": current_assets, "current_liabilities": current_liabilities},
            "warning": "Current liabilities is zero or negative; ratio is undefined",
        }

    ratio: float = round(current_assets / current_liabilities, 4)
    return {
        "ratio_name": "Current Ratio",
        "value": ratio,
        "formula": "Current Assets / Current Liabilities",
        "components": {"current_assets": current_assets, "current_liabilities": current_liabilities},
    }


def calculate_debt_service_coverage_ratio(
    net_operating_income: float,
    total_debt_service: float,
) -> Dict[str, Any]:
    """Calculate the Debt Service Coverage Ratio (Net Operating Income / Total Debt Service).

    Args:
        net_operating_income: EBITDA minus unfunded capex minus cash taxes paid, in dollars.
        total_debt_service: Sum of scheduled principal payments and interest payments, in dollars.

    Returns:
        Dictionary with ratio value, formula used, and input components.
    """
    if total_debt_service <= 0:
        return {
            "ratio_name": "Debt Service Coverage Ratio",
            "value": float("inf"),
            "formula": "Net Operating Income / Total Debt Service",
            "components": {
                "net_operating_income": net_operating_income,
                "total_debt_service": total_debt_service,
            },
            "warning": "Total debt service is zero or negative; ratio is undefined",
        }

    ratio: float = round(net_operating_income / total_debt_service, 4)
    return {
        "ratio_name": "Debt Service Coverage Ratio",
        "value": ratio,
        "formula": "Net Operating Income / Total Debt Service",
        "components": {
            "net_operating_income": net_operating_income,
            "total_debt_service": total_debt_service,
        },
    }


def calculate_tangible_net_worth(
    total_assets: float,
    total_liabilities: float,
    intangible_assets: float,
) -> Dict[str, Any]:
    """Calculate Tangible Net Worth (Total Assets - Total Liabilities - Intangible Assets).

    Args:
        total_assets: Total assets in dollars.
        total_liabilities: Total liabilities in dollars.
        intangible_assets: Intangible assets including goodwill, patents, trademarks, in dollars.

    Returns:
        Dictionary with calculated value, formula used, and input components.
    """
    tangible_net_worth: float = round(total_assets - total_liabilities - intangible_assets, 2)
    return {
        "metric_name": "Tangible Net Worth",
        "value": tangible_net_worth,
        "formula": "Total Assets - Total Liabilities - Intangible Assets",
        "components": {
            "total_assets": total_assets,
            "total_liabilities": total_liabilities,
            "intangible_assets": intangible_assets,
        },
    }


def evaluate_covenant_compliance(
    covenant_name: str,
    actual_value: float,
    threshold: float,
    constraint_type: str,
) -> Dict[str, Any]:
    """Evaluate whether a financial covenant is compliant, near breach, or breached.

    Uses a 10 percent buffer zone to determine near-breach status.

    Args:
        covenant_name: Name of the covenant being evaluated.
        actual_value: The actual calculated ratio or metric value.
        threshold: The covenant threshold from the loan agreement.
        constraint_type: Either 'maximum' (value must be at or below threshold) or 'minimum' (value must be at or above threshold).

    Returns:
        Dictionary with compliance status, headroom percentage, and assessment details.
    """
    near_breach_buffer: float = 0.10

    if constraint_type.lower() not in ("maximum", "minimum"):
        return {
            "covenant_name": covenant_name,
            "error": f"Invalid constraint_type '{constraint_type}'. Must be 'maximum' or 'minimum'.",
        }

    if constraint_type.lower() == "maximum":
        if actual_value > threshold:
            status: str = "breached"
            headroom_pct: float = round(-((actual_value - threshold) / threshold) * 100, 2)
        elif actual_value > threshold * (1 - near_breach_buffer):
            status = "near_breach"
            headroom_pct = round(((threshold - actual_value) / threshold) * 100, 2)
        else:
            status = "compliant"
            headroom_pct = round(((threshold - actual_value) / threshold) * 100, 2)
    else:
        if actual_value < threshold:
            status = "breached"
            headroom_pct = round(-((threshold - actual_value) / threshold) * 100, 2)
        elif actual_value < threshold * (1 + near_breach_buffer):
            status = "near_breach"
            headroom_pct = round(((actual_value - threshold) / threshold) * 100, 2)
        else:
            status = "compliant"
            headroom_pct = round(((actual_value - threshold) / threshold) * 100, 2)

    return {
        "covenant_name": covenant_name,
        "actual_value": actual_value,
        "threshold": threshold,
        "constraint_type": constraint_type,
        "status": status,
        "headroom_percentage": headroom_pct,
    }

{
    "envinroment_variables": {
        "UPSONIC_WORKERS_AMOUNT": {
            "type": "number",
            "description": "The number of workers for the Upsonic API",
            "default": 1
        },
        "API_WORKERS": {
            "type": "number",
            "description": "The number of workers for the Upsonic API",
            "default": 1
        },
        "RUNNER_CONCURRENCY": {
            "type": "number",
            "description": "The number of runners for the Upsonic API",
            "default": 1
        }
    },
    "machine_spec": {
        "cpu": 2,
        "memory": 4096,
        "storage": 1024
    },
    "agent_name": "Loan Covenant Monitoring Agent",
    "description": "AI agent team that monitors loan covenant compliance by extracting covenant definitions from agreements, calculating financial ratios with custom tools, and assessing breach risk using coordinated specialist agents",
    "icon": "shield-check",
    "language": "python",
    "streamlit": false,
    "proxy_agent": false,
    "dependencies": {
        "api": [
            "upsonic",
            "anthropic"
        ],
        "development": [
            "python-dotenv",
            "pytest"
        ]
    },
    "entrypoints": {
        "api_file": "main.py",
        "streamlit_file": "streamlit_app.py"
    },
    "input_schema": {
        "inputs": {
            "company_name": {
                "type": "string",
                "description": "Name of the borrower company (required)",
                "required": true,
                "default": null
            },
            "reporting_period": {
                "type": "string",
                "description": "Period being monitored, e.g. 'Q4 2025' (required)",
                "required": true,
                "default": null
            },
            "loan_agreement_path": {
                "type": "string",
                "description": "Path to the loan agreement text file (required)",
                "required": true,
                "default": null
            },
            "financial_data_path": {
                "type": "string",
                "description": "Path to the financial data JSON file (required)",
                "required": true,
                "default": null
            },
            "focus_areas": {
                "type": "array",
                "items": {
                    "type": "string"
                },
                "description": "Optional list of priority focus areas for the analysis",
                "required": false,
                "default": null
            },
            "enable_memory": {
                "type": "boolean",
                "description": "Whether to enable in-memory session persistence",
                "required": false,
                "default": true
            },
            "model": {
                "type": "string",
                "description": "Model identifier for the coordinator agent (e.g. anthropic/claude-sonnet-4-5, openai/gpt-4o)",
                "required": false,
                "default": "anthropic/claude-sonnet-4-5"
            },
            "print": {
                "type": "boolean",
                "description": "If true, do() prints output; if false, print_do() does not. Overridden by UPSONIC_AGENT_PRINT env.",
                "required": false
            }
        }
    },
    "output_schema": {
        "company_name": {
            "type": "string",
            "description": "The borrower company name"
        },
        "reporting_period": {
            "type": "string",
            "description": "The period that was monitored"
        },
        "report": {
            "type": "object",
            "description": "Full CovenantMonitoringReport with covenants, ratios, compliance results, risk assessment, executive summary, and next steps"
        },
        "monitoring_completed": {
            "type": "boolean",
            "description": "Whether the monitoring process completed successfully"
        }
    }
}