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Finance

Finance

Accounting Entry Validator

You are an Accounting Entry Validator Agent responsible for performing the first level of verification on accounting transactions. Your role is to detect and correct common errors, validate data integrity, and ensure transactions meet basic accounting standards before they proceed to compliance auditing. Your primary responsibilities include: - Validating required fields (date, amount, account_code, description, type) - Checking data format and type consistency - Detecting and correcting common formatting errors - Identifying duplicate or near-duplicate transactions - Enforcing business rules and account code ranges - Outputting validation results to a structured file for the Compliance Audit Agent You work as the first line of defense in the accounting workflow, ensuring only properly formatted and validated transactions reach the compliance stage.

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Purpose

You are an Accounting Entry Validator Agent responsible for performing the first level of verification on accounting transactions. Your role is to detect and correct common errors, validate data integrity, and ensure transactions meet basic accounting standards before they proceed to compliance auditing. Your primary responsibilities include: - Validating required fields (date, amount, account_code, description, type) - Checking data format and type consistency - Detecting and correcting common formatting errors - Identifying duplicate or near-duplicate transactions - Enforcing business rules and account code ranges - Outputting validation results to a structured file for the Compliance Audit Agent You work as the first line of defense in the accounting workflow, ensuring only properly formatted and validated transactions reach the compliance stage.

AI-Powered IntelligenceAdvanced AI capabilities for automated processing and analysis

Enterprise ReadyBuilt for production with security, scalability, and reliability

Seamless IntegrationEasy to integrate with your existing systems and workflows

Agent Capabilities

This agent is equipped with the following advanced capabilities:

Available Tools

File Tools

FileTools from agno framework

Reasoning Tools

ReasoningTools from agno framework

Calculator

CalculatorTools from agno framework

Websearch

DuckDuckGoTools is a convenience wrapper around WebSearchTools with the backend defaulting to "duckduckgo". Args: enable_search (bool): Enable web search function. enable_news (bool): Enable news search function. modifier (Optional[str]): A modifier to be prepended to search queries. fixed_max_results (Optional[int]): A fixed number of maximum results. proxy (Optional[str]): Proxy to be used for requests. timeout (Optional[int]): The maximum number of seconds to wait for a response. verify_ssl (bool): Whether to verify SSL certificates. timelimit (Optional[str]): Time limit for search results. Valid values: "d" (day), "w" (week), "m" (month), "y" (year). region (Optional[str]): Region for search results (e.g., "us-en", "uk-en", "ru-ru"). backend (Optional[str]): Backend to use for searching (e.g., "api", "html", "lite"). Defaults to "duckduckgo".

Validate Transaction

Validate a single transaction for required fields and data integrity. Returns (bool, list_of_errors).

def validate_transaction(transaction_data):
    """
    Validate a single transaction for required fields and data integrity.
    Returns(bool, list_of_errors).
    """
    errors = []
    required_fields = ["date", "amount", "account_code", "description", "type"]
    for field in required_fields:
        if field not in transaction_data or transaction_data[field] in [None, "", " "]:
            errors.append(f"Missing or empty field: {field}")
    try:
        datetime.strptime(transaction_data.get("date", ""), "%Y-%m-%d")
    except ValueError:
        errors.append("Invalid date format, expected YYYY-MM-DD")
    if not isinstance(transaction_data.get("amount"), (int, float)):
        errors.append("Amount must be numeric")
    if not str(transaction_data.get("account_code", "")).isdigit():
        errors.append("Invalid account code: must be numeric")
    return (len(errors) == 0, errors)

Correct Common Errors

Apply automatic corrections to common transaction errors. Returns corrected transaction_data and list of applied corrections.

def correct_common_errors(transaction_data):
    """
    Apply automatic corrections to common transaction errors.
    Returns corrected transaction_data and list of applied corrections.
    """
    corrections = []
    if transaction_data.get("description"):
        transaction_data["description"] = transaction_data["description"].strip()
    if "account_code" in transaction_data and transaction_data["account_code"]:
        if isinstance(transaction_data["account_code"], int):
            transaction_data["account_code"] = str(transaction_data["account_code"]).zfill(4)
            corrections.append("Normalized account code format")
    if "amount" in transaction_data:
        try:
            transaction_data["amount"] = float(transaction_data["amount"])
        except ValueError:
            corrections.append("Amount correction failed - non-numeric")
    return transaction_data, corrections

Check Duplicate Transactions

Check for potential duplicate transactions based on amount, account, and date. Returns list of (transaction, duplicate_info) for potential duplicates.

def check_duplicate_transactions(transactions):
    """
    Check for potential duplicate transactions based on amount, account, and date.
    Returns list of(transaction, duplicate_info) for potential duplicates.
    """
    duplicates = []
    for i, t1 in enumerate(transactions):
        for j, t2 in enumerate(transactions[i + 1 :], i + 1):
            if (
                t1.get("amount") == t2.get("amount")
                and t1.get("account_code") == t2.get("account_code")
                and t1.get("date") == t2.get("date")
                and t1.get("description") == t2.get("description")
            ):
                duplicates.append((t1, {"type": "exact_duplicate", "duplicate_of": t2}))
            elif(
                t1.get("amount") == t2.get("amount")
                and t1.get("account_code") == t2.get("account_code")
                and t1.get("date") == t2.get("date")
            ):
                duplicates.append((t1, {"type": "near_duplicate", "duplicate_of": t2}))
    return duplicates

Validate Business Rules

Validate transaction against business rules and account code ranges. Returns (bool, list_of_violations).

def validate_business_rules(transaction_data, business_rules=None):
    """
    Validate transaction against business rules and account code ranges.
    Returns(bool, list_of_violations).
    """
    if business_rules is None:
        business_rules = {
            "account_code_ranges": {
                "assets": (1000, 1999),
                "liabilities": (2000, 2999),
                "equity": (3000, 3999),
                "revenue": (4000, 4999),
                "expenses": (5000, 5999),
            },
            "max_amount": 1000000,
            "min_amount": 0.01,
            "prohibited_accounts": ["0000", "9999"],
        }
    violations = []
    account_code = int(transaction_data.get("account_code", 0))
    valid_range = False
    for category, (min_code, max_code) in business_rules["account_code_ranges"].items():
        if min_code <= account_code <= max_code:
            valid_range = True
            break
    if not valid_range:
        violations.append(f"Account code {account_code} is outside valid ranges")
    amount = transaction_data.get("amount", 0)
    if amount > business_rules["max_amount"]:
        violations.append(f"Amount {amount} exceeds maximum allowed {business_rules['max_amount']}")
    if amount < business_rules["min_amount"]:
        violations.append(f"Amount {amount} is below minimum allowed {business_rules['min_amount']}")
    if str(transaction_data.get("account_code")) in business_rules["prohibited_accounts"]:
        violations.append(f"Account code {transaction_data.get('account_code')} is prohibited")
    return (len(violations) == 0, violations)

Required Inputs

Generated Outputs

Business Value

Automated processing reduces manual effort and improves accuracy

Consistent validation logic ensures compliance and audit readiness

Early detection of issues minimizes downstream risks and costs

Graph

Accounting Entry Validator preview

Pricing

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Dedicated support team and onboarding assistance.

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