Agent roles and specialization in Agentic AI - ML Experiment: Train & Evaluate

Experiment - Agent roles and specialization

Problem:You have a group of AI agents designed to work together on a task. Currently, all agents perform the same general role, which causes inefficiency and slower task completion.

Current Metrics:Task completion time: 120 seconds; Task accuracy: 75%; Resource usage: High

Issue:Agents are not specialized, leading to duplicated work and inefficient resource use.

Your Task

Assign specialized roles to agents to reduce task completion time below 90 seconds while maintaining or improving task accuracy above 80%.

You cannot add more agents.

You must keep the total resource usage below the current level.

Hint 1

Hint 2

Hint 3

Solution

Agentic AI

class Agent:
    def __init__(self, role):
        self.role = role

    def perform_task(self, data):
        match self.role:
            case 'data_collector':
                return self.collect_data(data)
            case 'data_processor':
                return self.process_data(data)
            case 'result_aggregator':
                return self.aggregate_results(data)

    def collect_data(self, data):
        # Simulate data collection
        return [d * 2 for d in data]

    def process_data(self, data):
        # Simulate data processing
        return [d + 1 for d in data]

    def aggregate_results(self, data):
        # Simulate result aggregation
        return sum(data) / len(data)

# Create specialized agents
agents = [
    Agent('data_collector'),
    Agent('data_processor'),
    Agent('result_aggregator')
]

# Sample data
raw_data = [1, 2, 3, 4, 5]

# Agent workflow
collected = agents[0].perform_task(raw_data)
processed = agents[1].perform_task(collected)
final_result = agents[2].perform_task(processed)

print(f'Final result: {final_result}')

Divided the original general task into three specialized roles: data_collector, data_processor, and result_aggregator.

Assigned each agent a specific role to avoid duplicated work.

Implemented a clear workflow where each agent performs its specialized subtask in sequence.

Results Interpretation

Before specialization: Task completion time was 120 seconds with 75% accuracy and high resource usage.

After specialization: Task completion time improved to 85 seconds, accuracy increased to 82%, and resource usage decreased to moderate.

Specializing agents into distinct roles reduces duplicated effort, improves efficiency, and enhances overall task performance.

Bonus Experiment

Try adding a communication protocol where agents share intermediate results dynamically to further improve accuracy and reduce completion time.

💡 Hint

Implement message passing or shared memory between agents to coordinate better and adapt to changing data.

Practice

1. What is the main purpose of defining agent roles in agentic AI systems?

easy

A. To increase the number of agents randomly

B. To make agents learn without any rules

C. To assign specific tasks each agent can perform

D. To remove all specialization from agents

Solution

1. Step 1: Understand agent roles

   Agent roles define what tasks or functions an agent is responsible for in a system.

2. Step 2: Connect roles to task assignment

   Assigning specific tasks to agents based on their roles helps organize and manage the system efficiently.

3. Final Answer:

   To assign specific tasks each agent can perform -> Option C

4. Quick Check:

   Agent roles = task assignment [OK]

Hint: Agent roles match agents to tasks clearly [OK]

Common Mistakes:

• Thinking roles increase agent count
• Believing roles remove rules
• Confusing roles with random behavior

2. Which of the following is the correct way to define a specialized agent role in Python?

easy

A. class DataCleanerAgent(Agent): pass

B. def DataCleanerAgent: pass

C. class DataCleanerAgent pass

D. agent DataCleanerAgent() {}

Solution

1. Step 1: Recall Python class syntax

   In Python, classes are defined using class ClassName(BaseClass): syntax.

2. Step 2: Check each option

   class DataCleanerAgent(Agent): pass correctly defines a class inheriting from Agent. Others have syntax errors.

3. Final Answer:

   class DataCleanerAgent(Agent): pass -> Option A

4. Quick Check:

   Python class syntax = class DataCleanerAgent(Agent): pass [OK]

Hint: Python classes use 'class Name(Base):' syntax [OK]

Common Mistakes:

• Missing parentheses in class definition
• Using 'def' instead of 'class' for classes
• Incorrect use of 'agent' keyword

3. Given the code below, what will be the output?

class Agent:
    def act(self):
        return "Generic action"

class CleanerAgent(Agent):
    def act(self):
        return "Cleaning task"

agent = CleanerAgent()
print(agent.act())

medium

A. Generic action

B. Cleaning task

C. Error: act method missing

D. None

Solution

1. Step 1: Understand method overriding

   The CleanerAgent class overrides the act method from Agent to return "Cleaning task".

2. Step 2: Check the printed output

   Creating an instance of CleanerAgent and calling act() returns "Cleaning task".

3. Final Answer:

   Cleaning task -> Option B

4. Quick Check:

   Overridden method returns "Cleaning task" [OK]

Hint: Child class method overrides parent method [OK]

Common Mistakes:

• Assuming parent method runs instead
• Expecting an error due to missing method
• Confusing method names

4. Identify the error in the following agent specialization code:

class Agent:
    def perform_task(self):
        print("Performing general task")

class SpecializedAgent(Agent):
    def perform_task(self):
        print("Performing special task")

agent = SpecializedAgent()
agent.perform_task

medium

A. Missing parentheses when calling perform_task method

B. SpecializedAgent does not inherit from Agent

C. Method perform_task is not defined in Agent

D. agent variable is not assigned

Solution

1. Step 1: Check method call syntax

   The code calls agent.perform_task without parentheses, so the method is not executed.

2. Step 2: Understand method invocation

   To run the method and see output, parentheses () are needed: agent.perform_task().

3. Final Answer:

   Missing parentheses when calling perform_task method -> Option A

4. Quick Check:

   Method call needs () [OK]

Hint: Always use () to call methods [OK]

Common Mistakes:

• Forgetting parentheses on method calls
• Thinking inheritance is missing
• Assuming method is undefined

5. You want to create an agent system where one agent specializes in data cleaning and another in data analysis. Which design approach best fits this specialization?

hard

A. Create agents without roles and assign tasks randomly at runtime

B. Use one agent class with a single method handling both cleaning and analysis

C. Make one agent do all tasks sequentially without specialization

D. Create two agent classes, DataCleanerAgent and DataAnalyzerAgent, each with specific methods

Solution

1. Step 1: Understand specialization benefits

   Specialization means agents focus on specific tasks to improve efficiency and clarity.

2. Step 2: Match design to specialization

   Creating separate classes for cleaning and analysis clearly separates roles and responsibilities.

3. Final Answer:

   Create two agent classes, DataCleanerAgent and DataAnalyzerAgent, each with specific methods -> Option D

4. Quick Check:

   Separate classes = clear specialization [OK]

Hint: Separate classes for separate tasks [OK]

Common Mistakes:

• Using one class for all tasks
• Assigning tasks randomly
• Ignoring specialization benefits