Agent API design patterns in Agentic AI - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Understanding the role of the 'Planner' in Agent API design

In an agent API design pattern, what is the primary role of the 'Planner' component?

A It executes the actions decided by the agent without modification.

B It decides the sequence of actions the agent should take to achieve its goal.

C It stores the agent's memory and past interactions for future reference.

D It handles the user interface and visual representation of the agent.

💡 Hint

Think about which part of the agent figures out what to do next.

The 'Planner' is responsible for deciding the sequence of steps or actions the agent should take to reach its goal. It plans the path forward.

❓ Predict Output

intermediate

Output of agent action execution with error handling

What will be the output of the following agent API code snippet when the action 'fetch_data' fails?

Agentic AI

class Agent:
    def __init__(self):
        self.actions = {'fetch_data': self.fetch_data}
    def fetch_data(self):
        raise Exception('Network error')
    def execute(self, action_name):
        try:
            return self.actions[action_name]()
        except Exception as e:
            return f'Error: {e}'
agent = Agent()
result = agent.execute('fetch_data')
print(result)

A Network error

B Exception: Network error

C None

D Error: Network error

💡 Hint

Look at the except block and what it returns.

The execute method catches the exception and returns a string starting with 'Error:' followed by the exception message.

❓ Model Choice

advanced

Choosing the best agent API pattern for dynamic task switching

Which agent API design pattern best supports dynamic switching between multiple tasks based on real-time input?

A Hierarchical pattern with layered decision-making and task delegation.

B Reactive pattern that immediately responds to inputs without internal state.

C Batch processing pattern that queues tasks and processes them sequentially.

D Static pipeline pattern with fixed sequence of operations.

💡 Hint

Consider which pattern allows flexible control and task management.

The hierarchical pattern allows the agent to delegate tasks and switch dynamically based on input, supporting complex decision-making.

❓ Hyperparameter

advanced

Impact of 'timeout' hyperparameter in agent API calls

In an agent API design, setting a 'timeout' hyperparameter controls:

A The maximum time the agent waits for an action to complete before aborting.

B The number of retries the agent makes after a failed action.

C The priority level of the agent's tasks in the queue.

D The size of the memory buffer storing past interactions.

💡 Hint

Think about what 'timeout' usually means in computing.

The 'timeout' sets how long the agent waits for an action to finish before stopping it to avoid hanging.

🔧 Debug

expert

Debugging unexpected agent response in API design

An agent API is designed to return a JSON response with keys 'status' and 'result'. The following code snippet returns {'status': 'success'} but misses 'result'. What is the likely cause?

def agent_response(data):
    if data:
        return {'status': 'success'}
    else:
        return {'status': 'fail', 'result': None}
response = agent_response({'task': 'run'})
print(response)

A The print statement is missing the 'result' key access.

B The input data is empty, so the else branch is not executed.

C The function does not include 'result' in the success case, causing incomplete response.

D The function should return a string, not a dictionary.

💡 Hint

Check what keys are returned in each branch of the function.

The success branch returns only 'status' without 'result', so the response is missing that key.

Practice

1. What is the main purpose of using Agent API design patterns in AI systems?

easy

A. To organize how AI agents communicate and work together

B. To speed up the training of machine learning models

C. To store large datasets efficiently

D. To improve the hardware performance of AI servers

Solution

1. Step 1: Understand the role of Agent API design patterns

   These patterns help define clear communication and interaction rules between AI agents.

2. Step 2: Compare with other options

   Options A, C, and D relate to training speed, data storage, and hardware, which are not the focus of Agent API design patterns.

3. Final Answer:

   To organize how AI agents communicate and work together -> Option A

4. Quick Check:

   Agent API design patterns = organize communication [OK]

Hint: Agent API patterns focus on agent communication, not hardware or data [OK]

Common Mistakes:

• Confusing design patterns with hardware optimization
• Thinking patterns speed up model training directly
• Mixing data storage with agent communication

2. Which of the following is the correct way to define a simple message passing function in an Agent API?

easy

A. def send_message(agent, message): return message + agent

B. def send_message(agent, message): agent.send(message)

C. def send_message(agent, message): return agent.receive(message)

D. def send_message(agent, message): print(agent + message)

Solution

1. Step 1: Analyze the function purpose

   The function should send a message to an agent and get a response by calling the agent's receive method.

2. Step 2: Check each option

   def send_message(agent, message): return agent.receive(message) correctly calls agent.receive(message). def send_message(agent, message): agent.send(message) calls agent.send which is not standard. Options A and C incorrectly try to add or print agent and message.

3. Final Answer:

   def send_message(agent, message): return agent.receive(message) -> Option C

4. Quick Check:

   Message passing calls agent.receive(message) [OK]

Hint: Message passing calls agent.receive(message) to send data [OK]

Common Mistakes:

• Using agent.send instead of agent.receive
• Trying to concatenate agent object with string
• Printing instead of returning the message

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

class Agent:
    def receive(self, message):
        return f"Received: {message}"

def send_message(agent, message):
    return agent.receive(message)

agent = Agent()
print(send_message(agent, "Hello"))

medium

A. Error: method not found

B. Hello

C. send_message(agent, Hello)

D. Received: Hello

Solution

1. Step 1: Understand the Agent class and receive method

   The receive method returns the string 'Received: ' plus the message passed.

2. Step 2: Trace the send_message call

   send_message calls agent.receive with "Hello", so it returns 'Received: Hello'.

3. Final Answer:

   Received: Hello -> Option D

4. Quick Check:

   agent.receive("Hello") = "Received: Hello" [OK]

Hint: Agent.receive returns 'Received: ' plus message [OK]

Common Mistakes:

• Expecting just the message without prefix
• Thinking send_message prints instead of returns
• Assuming method does not exist causing error

4. Identify the error in the following Agent API code snippet:

class Agent:
    def receive(self, message):
        print(f"Got message: {message}")

def send_message(agent, message):
    return agent.receive(message)

agent = Agent()
response = send_message(agent, "Hi")
print(response)

medium

A. The receive method should return a value, not just print

B. send_message should not call agent.receive

C. Agent class is missing an __init__ method

D. The print statement in send_message is incorrect

Solution

1. Step 1: Check receive method behavior

   receive only prints the message but does not return anything, so it returns None by default.

2. Step 2: Analyze send_message and print(response)

   send_message returns None, so printing response outputs None, which is likely unintended.

3. Final Answer:

   The receive method should return a value, not just print -> Option A

4. Quick Check:

   receive must return message for send_message to work [OK]

Hint: receive must return, not just print, to pass data back [OK]

Common Mistakes:

• Ignoring that print returns None
• Thinking __init__ is required here
• Confusing print location with syntax error

5. You want to design an Agent API where multiple agents can collaborate by passing messages and roles define their behavior. Which design pattern best supports this?

hard

A. Factory pattern to create agents dynamically

B. Mediator pattern to centralize communication between agents

C. Singleton pattern to ensure one agent instance

D. Observer pattern to notify agents of state changes

Solution

1. Step 1: Understand collaboration and role-based behavior

   Agents need a central way to communicate and coordinate roles effectively.

2. Step 2: Match design patterns to needs

   The Mediator pattern centralizes communication, making it ideal for agent collaboration. Singleton limits to one instance, Factory creates objects, Observer handles notifications but not central communication.

3. Final Answer:

   Mediator pattern to centralize communication between agents -> Option B

4. Quick Check:

   Collaboration with roles = Mediator pattern [OK]

Hint: Mediator centralizes agent communication for collaboration [OK]

Common Mistakes:

• Choosing Singleton which limits to one agent
• Confusing Factory with communication pattern
• Using Observer which is for event notification only