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The Agent can be created with minimal configuration or with extensive customization to suit your specific needs. The agent provides a robust foundation for AI-powered applications with built-in support for various advanced features.

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Basic Agent Creation

The simplest way to create an agent is with default settings: 
from upsonic import Agent

# Create agent with default settings
agent = Agent()
Warning: When creating an agent without specifying a model, it defaults to "openai/gpt-4o". Make sure you have the appropriate API key set in your environment.

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Agent with Custom Configuration

For more control over your agent’s behavior, you can specify various parameters: 
from upsonic import Agent

# Create agent with custom settings for financial analysis
agent = Agent(
    name="FinancialAnalyst",
    model="openai/gpt-4o-mini",
    debug=True,
    retry=3,
    mode="raise"
)

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Agent with Company Information

You can provide company context to help the agent understand your organization: 
agent = Agent(
    name="BankingAgent",
    company_url="https://fintechbank.com",
    company_objective="To provide secure, innovative financial services",
    company_description="A leading digital bank specializing in AI-powered financial solutions and personalized banking experiences"
)

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Complete Example: Advanced Agent Configuration

Here’s a comprehensive example showing how to create a fully-featured agent with all capabilities: 
from upsonic import Agent, Task, Memory
from upsonic.storage.providers.sqlite import SqliteStorage
from upsonic.models.providers import OpenAI
from upsonic.safety_engine.policies.crypto_policies import CryptoBlockPolicy
from upsonic.safety_engine.policies.phone_policies import AnonymizePhoneNumbersPolicy
from upsonic.reliability_layer.reliability_layer import ReliabilityProcessor


# Create storage and memory
storage = SqliteStorage("sessions", "profiles", "advanced_agent.db")
memory = Memory(
    storage=storage,
    session_id="advanced_session",
    user_id="user_001",
    full_session_memory=True,
    summary_memory=True,
    user_analysis_memory=True,
    model_provider=OpenAI(model_name="gpt-4o-mini")
)

# Create reliability layer
reliability_layer = ReliabilityProcessor(confidence_threshold=0.8)

# Create fully-featured banking agent
agent = Agent(
    # Identity
    name="BankingRiskAnalyst",
    agent_id_="banking_analyst_001",
    
    # Model and performance
    model="openai/gpt-4o-mini",
    retry=3,
    mode="raise",
    debug=True,
    
    # Company context
    company_url="https://securebank.com",
    company_objective="Providing secure, AI-powered financial services",
    company_description="Leading digital bank with advanced risk management",
    
    # Professional context
    role="Senior Financial Risk Analyst",
    goal="To provide accurate risk assessments and regulatory compliance analysis",
    instructions="Always follow banking regulations and provide detailed risk documentation",
    education="Finance and Risk Management PhD",
    work_experience="10 years in banking and financial risk analysis",
    
    # System configuration
    system_prompt="You are an expert financial AI assistant with advanced risk assessment and regulatory compliance capabilities.",
    
    # Memory and storage
    memory=memory,
    feed_tool_call_results=True,
    
    # Safety and reliability
    user_policy=CryptoBlockPolicy,
    agent_policy=AnonymizePhoneNumbersPolicy,
    reliability_layer=reliability_layer,
    
    # Tool configuration
    show_tool_calls=True,
    tool_call_limit=10,
    enable_thinking_tool=True,
    enable_reasoning_tool=True
)

# Create and execute a complex banking task
task = Task(
    description="""
    Analyze the following banking scenario and provide strategic risk management recommendations:
    
    A regional bank has $100M in capital and needs to decide between:
    1. Expanding digital banking services to new markets
    2. Developing AI-powered fraud detection systems
    3. Acquiring a smaller fintech startup
    
    Consider regulatory requirements, market conditions, competition, and risk exposure.
    """,
    enable_thinking_tool=True,
    enable_reasoning_tool=True,
    enable_cache=True,
    cache_method="vector_search",
    cache_threshold=0.8
)

# Execute the task
result = agent.print_do(task)

# Check cache statistics
print(f"Cache stats: {agent.get_cache_stats()}")
This comprehensive configuration demonstrates how to leverage all the major features of the Agent for sophisticated banking and fintech applications.

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Best Practices

  1. Start Simple: Begin with basic configuration and add features as needed for your banking use case
  2. Monitor Performance: Use debug mode to understand token usage and costs in financial applications
  3. Memory Management: Choose appropriate memory settings for customer relationship management and compliance tracking
  4. Safety First: Always implement appropriate safety policies for financial data and regulatory compliance
  5. Error Handling: Configure retry settings based on your reliability requirements for critical banking operations
  6. Resource Management: Be mindful of token consumption with reasoning capabilities in risk assessment scenarios
  7. Regulatory Compliance: Ensure all AI outputs meet banking regulations and compliance requirements
  8. Data Security: Implement proper data encryption and access controls for sensitive financial information
The Agent provides a powerful foundation for building sophisticated banking and fintech applications with enterprise-grade features, regulatory compliance, and advanced risk management capabilities.
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