​
Agent Tasks

Tasks are the specific units of work that AI agents perform. Understanding how to define and manage tasks is essential for creating effective agent systems.

​
What is an Agent Task?

Definition

A task is a specific, well-defined piece of work assigned to an agent with clear inputs, expected outputs, and success criteria.

Just as human jobs are broken into specific responsibilities, agent systems work best when complex goals are divided into manageable tasks.

​
Anatomy of a Task

A well-defined task includes:

Objective

What the task should accomplish

Inputs

Information provided to complete the task

Process

Steps to follow or approach to take

Outputs

Expected results or deliverables

​
Task Types

​
1. Information Tasks

These tasks involve finding, analyzing, or summarizing information.

Examples:

  • Research a topic
  • Summarize a document
  • Extract data from text
  • Answer questions

​
2. Creation Tasks

These tasks involve generating new content.

Examples:

  • Write an article
  • Create a marketing plan
  • Generate images (with appropriate tools)
  • Design a workflow

​
3. Analysis Tasks

These tasks involve evaluating information and drawing conclusions.

Examples:

  • Analyze data trends
  • Review content for quality
  • Evaluate options
  • Identify patterns

​
4. Interaction Tasks

These tasks involve communicating or working with users or other systems.

Examples:

  • Answer customer questions
  • Guide users through processes
  • Collect information from users
  • Notify about events

​
Creating Tasks in Praison Labs

​
Task Flow

The typical lifecycle of a task follows this pattern:

​
Task Configuration 

task = Task(
    description="Research AI trends",
    expected_output="Summary report",
    agent=research_agent
)

​
Understanding Tasks




​
Task Types

1

Basic Task

Simple, single-operation tasks with clear inputs and outputs

2

Decision Task

Tasks involving choices and conditional paths

decision_task = Task(
    type="decision",
    conditions={
        "success": ["next_task"],
        "failure": ["error_task"]
    }
)
3

Loop Task

Repetitive operations over collections

loop_task = Task(
    type="loop",
    items=data_list,
    operation="process_item"
)

​
Task Relationships

Properly managing task dependencies is crucial for complex workflows. Always ensure proper context sharing and error handling.

​
Context Sharing 

task_a = Task(name="research")
task_b = Task(
    name="analyze",
    context=[task_a]  # Uses task_a's output
)

​
Task Best Practices

Be Specific

Clearly define what the task should accomplish

One Goal Per Task

Each task should focus on a single objective

Provide Context

Include necessary background information

Define Success

Specify what a successful outcome looks like

Break complex goals into multiple smaller tasks rather than creating one large, complex task.

​
Common Task Mistakes

Vague Instructions

Unclear about what the agent should do

Too Many Objectives

Asking for too many things in one task

Missing Context

Not providing necessary background information

Unrealistic Expectations

Asking for tasks beyond agent capabilities

In the next lesson, we’ll learn about creating your first complete agent application.