Supervisor agent pattern in Agentic AI - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Role of the Supervisor Agent in the Supervisor Agent Pattern

In the supervisor agent pattern, what is the primary role of the supervisor agent?

A To directly execute all tasks without delegation

B To monitor and coordinate subordinate agents to ensure task completion

C To replace subordinate agents when they fail

D To generate raw data for training machine learning models

💡 Hint

Think about who manages and oversees other agents in a team.

The supervisor agent oversees and coordinates subordinate agents, ensuring tasks are completed correctly and efficiently.

❓ Model Choice

intermediate

Choosing the Best Model for a Supervisor Agent

Which type of model is most suitable for implementing a supervisor agent that needs to evaluate multiple subordinate agents' outputs and decide the next steps?

A A rule-based decision system with conditional logic

B A simple linear regression model

C A convolutional neural network (CNN) designed for image recognition

D A recurrent neural network (RNN) for sequence prediction

💡 Hint

Consider the need for clear decision rules rather than pattern recognition.

Supervisor agents often use rule-based systems to evaluate outputs and decide actions based on conditions, rather than complex pattern recognition models.

❓ Predict Output

advanced

Output of Supervisor Agent Coordination Code

What is the output of the following Python code simulating a supervisor agent coordinating two subordinate agents?

Agentic AI

class SubordinateAgent:
    def __init__(self, name):
        self.name = name
    def perform_task(self):
        return f"Task done by {self.name}"

class SupervisorAgent:
    def __init__(self, agents):
        self.agents = agents
    def coordinate(self):
        results = []
        for agent in self.agents:
            results.append(agent.perform_task())
        return results

agents = [SubordinateAgent('Agent1'), SubordinateAgent('Agent2')]
supervisor = SupervisorAgent(agents)
output = supervisor.coordinate()
print(output)

A ['Task done by Agent1']

B ['Task done by Agent2', 'Task done by Agent1']

C TypeError: 'SubordinateAgent' object is not iterable

D ['Task done by Agent1', 'Task done by Agent2']

💡 Hint

Look at the order of agents in the list and how results are collected.

The supervisor calls perform_task on each agent in order, collecting their results in a list.

❓ Hyperparameter

advanced

Key Hyperparameter for Supervisor Agent's Decision Threshold

In a supervisor agent that decides to reassign tasks based on confidence scores from subordinate agents, which hyperparameter is critical to tune?

A Confidence threshold for task reassignment

B Number of layers in the supervisor agent's neural network

C Batch size used during training of subordinate agents

D Learning rate of the subordinate agents' models

💡 Hint

Focus on the parameter that controls when the supervisor decides to act.

The confidence threshold determines when the supervisor decides a subordinate agent's output is unreliable and needs reassignment.

🔧 Debug

expert

Debugging Supervisor Agent Coordination Failure

Given the following code snippet for a supervisor agent, what error will occur when running it and why? class SupervisorAgent: def __init__(self, agents): self.agents = agents def coordinate(self): results = [] for agent in self.agents: results.append(agent.perform_task) return results class SubordinateAgent: def perform_task(self): return "done" agents = [SubordinateAgent(), SubordinateAgent()] supervisor = SupervisorAgent(agents) output = supervisor.coordinate() print(output)

A TypeError: 'method' object is not callable

B ['done', 'done']

C ['<bound method SubordinateAgent.perform_task of <__main__.SubordinateAgent object at 0x...>>', '<bound method SubordinateAgent.perform_task of <__main__.SubordinateAgent object at 0x...>>']

D AttributeError: 'SubordinateAgent' object has no attribute 'perform_task'

💡 Hint

Check if the method is called or just referenced.

The code appends the method itself without calling it, so the output is a list of method references, not their return values.

Practice

1. What is the main role of a Supervisor agent in the supervisor agent pattern?

easy

A. To collect raw data from sensors

B. To train a single AI model

C. To replace all other agents with one

D. To manage and coordinate multiple AI agents

Solution

1. Step 1: Understand the supervisor agent's purpose

   The supervisor agent is designed to oversee and coordinate multiple AI agents working together.

2. Step 2: Differentiate from other roles

   Unlike training or data collection, the supervisor agent focuses on managing teamwork and quality control.

3. Final Answer:

   To manage and coordinate multiple AI agents -> Option D

4. Quick Check:

   Supervisor agent = manager of multiple agents [OK]

Hint: Supervisor agent = team manager of AI agents [OK]

Common Mistakes:

• Confusing supervisor with data collector
• Thinking supervisor trains models directly
• Assuming supervisor replaces all agents

2. Which of the following is the correct way to describe the supervisor agent's function in code?

easy

A. supervisor.replace_agents()

B. supervisor.train_single_model(data)

C. supervisor.collect_results(agents)

D. supervisor.ignore_agent_outputs()

Solution

1. Step 1: Identify supervisor's interaction with agents

   The supervisor collects and evaluates results from multiple agents, so a method like collect_results fits.

2. Step 2: Eliminate incorrect options

   Training a single model, replacing agents, or ignoring outputs do not match the supervisor's coordination role.

3. Final Answer:

   supervisor.collect_results(agents) -> Option C

4. Quick Check:

   Supervisor collects results = collect_results() [OK]

Hint: Supervisor collects and evaluates agent outputs [OK]

Common Mistakes:

• Choosing training method instead of collection
• Thinking supervisor replaces agents
• Ignoring outputs contradicts supervisor role

3. Given this code snippet for a supervisor agent pattern, what will be the printed output?

class Agent:
    def __init__(self, name, score):
        self.name = name
        self.score = score

class Supervisor:
    def __init__(self, agents):
        self.agents = agents
    def best_agent(self):
        return max(self.agents, key=lambda a: a.score).name

agents = [Agent('A1', 85), Agent('A2', 90), Agent('A3', 88)]
supervisor = Supervisor(agents)
print(supervisor.best_agent())

medium

A. A1

B. A2

C. A3

D. None

Solution

1. Step 1: Understand the agent scores

   Agents have scores: A1=85, A2=90, A3=88.

2. Step 2: Identify the agent with the highest score

   The best_agent method returns the name of the agent with the max score, which is A2 with 90.

3. Final Answer:

   A2 -> Option B

4. Quick Check:

   Max score agent = A2 [OK]

Hint: Max score agent name is printed [OK]

Common Mistakes:

• Choosing agent with second highest score
• Confusing agent names
• Assuming None if not found

4. Identify the bug in this supervisor agent code snippet:

class Supervisor:
    def __init__(self, agents):
        self.agents = agents
    def best_score(self):
        return max(self.agents, key=lambda a: a.score)

agents = [{'name': 'A1', 'score': 80}, {'name': 'A2', 'score': 95}]
supervisor = Supervisor(agents)
print(supervisor.best_score())

medium

A. Agents should be objects, not dictionaries

B. max() function is used incorrectly

C. Missing return statement in best_score

D. Supervisor class missing __init__ method

Solution

1. Step 1: Check agent data type and usage

   The best_score method expects agents with attribute score, but agents are dictionaries, not objects.

2. Step 2: Understand attribute vs key access

   Using a.score on a dictionary causes an error; dictionaries need a['score'].

3. Final Answer:

   Agents should be objects, not dictionaries -> Option A

4. Quick Check:

   Attribute access on dict causes error [OK]

Hint: Use objects or adjust attribute access for dicts [OK]

Common Mistakes:

• Thinking max() usage is wrong
• Missing return statement (it exists)
• Ignoring data type mismatch

5. You want to design a supervisor agent that combines outputs from three different AI agents solving a complex task. Which approach best fits the supervisor agent pattern?

hard

A. Collect outputs, evaluate quality, and select the best result

B. Run only the fastest agent and ignore others

C. Train all agents on the same data independently

D. Replace all agents with a single large model

Solution

1. Step 1: Understand supervisor agent's coordination role

   The supervisor should gather outputs from all agents and decide which is best based on quality.

2. Step 2: Evaluate other options

   Ignoring agents, training independently without coordination, or replacing agents contradict the supervisor pattern.

3. Final Answer:

   Collect outputs, evaluate quality, and select the best result -> Option A

4. Quick Check:

   Supervisor = collect + evaluate + select best [OK]

Hint: Supervisor picks best output from all agents [OK]

Common Mistakes:

• Ignoring some agents' outputs
• Confusing training with supervising
• Replacing agents instead of coordinating