Introduction

In the realm of generative AI, creating intelligent agents capable of performing complex tasks is a fascinating challenge. The CrewAI multi-agent platform provides a powerful framework for developing such systems. In this blog post, we'll explore the crucial aspect of implementing tasks and goals for agents, a fundamental skill for anyone working with CrewAI.

Understanding Tasks and Goals in CrewAI

Before diving into implementation, it's essential to grasp the concepts of tasks and goals within the CrewAI context:

1. Tasks: These are specific actions or operations that an agent can perform. They represent the building blocks of agent behavior.

2. Goals: Goals are high-level objectives that guide an agent's decision-making process. They provide purpose and direction to the agent's actions.

Implementing Tasks

Let's start by looking at how to implement tasks for CrewAI agents:

1. Define Task Structure

First, create a clear structure for your tasks. This typically involves:

• A unique identifier
• A description of the task
• Input parameters
• Expected output

Here's a simple example in Python:

class Task:
    def __init__(self, task_id, description, inputs, expected_output):
        self.task_id = task_id
        self.description = description
        self.inputs = inputs
        self.expected_output = expected_output

2. Create Task Execution Methods

Next, implement methods that allow agents to execute tasks. These methods should:

• Process input parameters
• Perform the required operations
• Return the result

Example:

class Agent:
    def execute_task(self, task):
        if task.task_id == "generate_image":
            return self.generate_image(task.inputs["prompt"])
        elif task.task_id == "analyze_text":
            return self.analyze_text(task.inputs["text"])

# Add more task types as needed

    def generate_image(self, prompt):

# Image generation logic here
        pass

    def analyze_text(self, text):

# Text analysis logic here
        pass

3. Task Decomposition

For complex tasks, it's often beneficial to break them down into smaller, manageable subtasks. This approach allows for more flexible and modular agent behavior.

Example:

def write_article(topic):
    subtasks = [
        Task("research", "Gather information on the topic", {"topic": topic}, "research_notes"),
        Task("outline", "Create an article outline", {"research_notes": "research_notes"}, "outline"),
        Task("write", "Write the article content", {"outline": "outline"}, "draft"),
        Task("edit", "Proofread and edit the article", {"draft": "draft"}, "final_article")
    ]
    return subtasks

Implementing Goals

Now, let's explore how to implement goals for CrewAI agents:

1. Define Goal Structure

Create a structure to represent goals, including:

• A description of the desired outcome
• Criteria for success
• Priority level

Example:

class Goal:
    def __init__(self, description, success_criteria, priority):
        self.description = description
        self.success_criteria = success_criteria
        self.priority = priority

2. Implement Goal-Oriented Behavior

Modify your agent class to incorporate goal-driven decision-making:

class Agent:
    def __init__(self):
        self.goals = []

    def add_goal(self, goal):
        self.goals.append(goal)

    def select_task(self, available_tasks):
        for goal in sorted(self.goals, key=lambda g: g.priority, reverse=True):
            for task in available_tasks:
                if self.task_contributes_to_goal(task, goal):
                    return task
        return None

    def task_contributes_to_goal(self, task, goal):

# Logic to determine if a task helps achieve the goal
        pass

3. Goal Evaluation and Adjustment

Implement mechanisms for agents to evaluate their progress towards goals and adjust their behavior accordingly:

class Agent:
    def evaluate_goal_progress(self, goal):

# Logic to assess progress towards the goal
        pass

    def adjust_goal_priority(self, goal, new_priority):
        goal.priority = new_priority

    def update_goals(self):
        for goal in self.goals:
            progress = self.evaluate_goal_progress(goal)
            if progress < 0.5:
                self.adjust_goal_priority(goal, goal.priority + 1)

Putting It All Together

Now that we have implemented both tasks and goals, let's see how they work together in a CrewAI agent system:

# Create an agent
agent = Agent()

# Add goals
agent.add_goal(Goal("Generate 10 unique images", lambda x: x >= 10, 2))
agent.add_goal(Goal("Analyze 100 text samples", lambda x: x >= 100, 1))

# Create tasks
image_task = Task("generate_image", "Create a unique image", {"prompt": "abstract art"}, "image")
text_task = Task("analyze_text", "Analyze text sentiment", {"text": "Sample text"}, "sentiment")

# Agent selects and executes tasks based on goals
while True:
    task = agent.select_task([image_task, text_task])
    if task:
        result = agent.execute_task(task)

# Process result and update goal progress
    else:
        break

# All goals achieved or no suitable tasks available

    agent.update_goals()

# Adjust goal priorities as needed

This example demonstrates how an agent can use its goals to guide task selection and execution, creating a purposeful and adaptive AI system.

Conclusion

Implementing tasks and goals for agents in CrewAI is a crucial step in developing sophisticated generative AI systems. By carefully structuring tasks, decomposing complex operations, and creating goal-oriented behaviors, you can create agents that are both flexible and focused on achieving specific objectives.

Tags: AI agents, task implementation, goal-oriented behavior, CrewAI development, generative AI systems