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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.
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 Intelligence — Advanced AI capabilities for automated processing and analysis
Enterprise Ready — Built for production with security, scalability, and reliability
Seamless Integration — Easy 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
File Tools
FileTools from agno framework
Reasoning Tools
ReasoningTools from agno framework
Reasoning Tools
ReasoningTools from agno framework
Calculator
CalculatorTools 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".
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).
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.
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.
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).
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
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