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What is the Task?

Agents are just like the humans. As humans we are creating some virtual steps for our tasks. Like first i will search for the websites, then i will read the content then i will categorize them. For the agents task we have the some structure. The key points for the tasks are:
  • Tasks are the parts of the main job: You need to create the steps to archive your main task over Task system.
  • They increase the accruacy: With the small jobs agents are more focusing on what to do right now and their accuracy increases.
  • You can manage the Tools and Context:: With this focusing setting the tools for the tasks the overall token usage and accuracy increases. Agents only get the required context at the right time.

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Core Principles For Tasks

When you are creating a Task, ensure that you define these elements as well:
  • description: Each task should have a clear, actionable description that matches its purpose.
  • context management: Tasks often depend on the results or context of previous tasks. Make sure to specify context when needed.
  • Be specific: Provide specific instructions for what the task should accomplish and how.

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Defining Description

Descriptions should be simple and actionable. Keep them short to increase accuracy. There is an tradeoff between the task number and cost. If you want to get more accurate results you need to open more tasks and split your tasks. Good Descriptions 
description="Check their website for the following points: 'Terms and Sales', 'About Us', 'SSL Licence' "
description="Go to website and extract their Terms of Sales link"
description="Explain the changes between today and yesterday financial operations"
descirption="Write an feedback for the coding standards of this code"
Bad Descriptions 
description="Analyze their website for the missing things"
description="Make the financial analyze"
description="Review the pull request"

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Let’s Create task for Analyzing the Merchant Websites

In Upsonic we have a Task class and they are the core parts of anything that you do actually. When you create an task you can use that task in an Direct LLM Call, Agent and Multi-Agent teams. In this example we will add the task to our merchant Website analyzer. 
# Upsonic Docs: Create an Agent
# https://docs.upsonic.ai/guides/2-create-an-agent



# Imports
from upsonic import Agent
from upsonic import Task
from upsonic import WebSearch, WebRead


# Agent Creation
merchant_analyzer_agent = Agent(
    name="Merchant Analyzer V1",
    role="Analyzing the merchant websites.",
    goal="Getting the right result on their websites and giving as the user requested format",
    instructions="""
    Identify and analyze the product's category and brand.
    If the product belongs to a series, pay special attention to series details and related products.
    Summarize product trends in clear bullet points for easy review.
    Prioritize up-to-date information and highlight any unusual patterns or outliers.
    """
)


# The Company We Want to Analyze
company_name = "Toscrape.com Books"


# Task Creation
task1 = Task(
    description=f"Find the website of the {company_name}",
    tools=[WebSearch, WebRead]    
)

task2 = Task(
    description="Extract the categories in the website", 
    context=[task1],
    tools=[WebRead]
)

task3 = Task(
    description="Extract the products that they are sellin in their websites by the categories", 
    context=[task1, task2],
    tools=[WebRead]
)


# Run the tasks
merchant_analyzer_agent.print_do(task1)
merchant_analyzer_agent.print_do(task2)
merchant_analyzer_agent.print_do(task3)

final_result = task3.response

print("Final Result")
print(final_result)

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Adding Files and Documents to Tasks

The context attribute is the comprehensive way to provide any contextual information to your tasks. This includes files, images, documents, other tasks, knowledge bases, or any other relevant data.

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Adding Files to Tasks

# Task with file attachments
task_with_files = Task(
    description="Analyze the attached documents and extract key information",
    context=["report.pdf", "data.csv", "image.png"],
    tools=[WebRead]  # If you need web tools for additional research
)

# Task with mixed context (files + other tasks + text)
task_with_mixed_context = Task(
    description="Compare the uploaded data with the previous analysis results",
    context=[
        "new_data.xlsx",           # File attachment
        previous_analysis_task,   # Previous task result
        "Focus on Q4 trends",     # Text context
        knowledge_base            # Knowledge base
    ]
)

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Supported File Types

The framework automatically handles various file formats:
  • Images: PNG, JPEG, GIF, BMP, TIFF
  • Documents: PDF, Word (.docx), Text (.txt), Markdown (.md)
  • Data: CSV, JSON, XML, Excel (.xlsx)
  • Code: Python (.py), JavaScript (.js), etc.
# Example with multiple file types
comprehensive_task = Task(
    description="Create a comprehensive analysis report",
    context=[
        "financial_data.xlsx",    # Excel data
        "charts.png",            # Image
        "market_report.pdf",     # PDF document
        "raw_data.json",         # JSON data
        previous_research_task,  # Previous task
        "Additional context: Focus on emerging markets"  # Text context
    ]
)

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Need more advanced features?

The Task system offers several advanced configurations to enhance task management:
  • Structured Output: Define custom Pydantic models for structured responses, ensuring consistent and validated output formats.
  • Contextual Dependencies: Define tasks with dependencies on the results or context of previous tasks, ensuring coherent workflows.
  • Tool Assignment: Assign specific tools to tasks to optimize resource usage and improve accuracy.
  • Caching Mechanisms: Implement intelligent caching with vector search or LLM-based similarity matching to reduce redundant processing.
  • Guardrail Validation: Apply custom validation functions with retry logic to ensure task outputs meet specific quality standards.
  • External Execution: Configure tasks to pause for external tool execution, enabling integration with external systems and human-in-the-loop workflows.
  • Comprehensive Context: Include any contextual information in the context attribute - files, images, documents, other tasks, knowledge bases, or any other relevant data for multimodal processing capabilities.
  • Context Integration: Leverage knowledge bases and RAG systems to provide rich contextual information for task execution.
  • Performance Monitoring: Track task execution metrics including duration, token usage, and cost analysis.
For detailed examples and advanced patterns, see our comprehensive Task Concept Documentation.
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