Agent API design patterns in Agentic AI

Introduction

Agent API design patterns help organize how AI agents communicate and work together. They make building smart systems easier and clearer.

When building a chatbot that needs to handle different tasks like answering questions and booking appointments.

When creating a system where multiple AI agents share information to solve a problem.

When designing an AI assistant that needs to follow clear steps to complete a user's request.

When you want to make your AI code easier to maintain and expand later.

When you need your AI agents to work in a predictable and reliable way.

Syntax

Agentic AI

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

    def receive(self, message):
        # Process incoming message
        pass

    def send(self, message, other_agent):
        # Send message to another agent
        other_agent.receive(message)

    def act(self):
        # Perform agent's action
        pass

This is a simple pattern showing how agents send and receive messages.

Each agent has clear methods for communication and action.

Examples

An agent that replies back with the same message it got.

Agentic AI

class EchoAgent(Agent):
    def receive(self, message):
        print(f"{self.name} received: {message}")
        self.send(f"Echo: {message}", other_agent)

An agent that manages or coordinates other agents' work.

Agentic AI

class CoordinatorAgent(Agent):
    def act(self):
        print(f"{self.name} is coordinating tasks.")

Sample Model

This program shows two agents: one coordinates, the other echoes messages. The echo agent replies back when it receives a message.

Agentic AI

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

    def receive(self, message):
        pass

    def send(self, message, other_agent):
        other_agent.receive(message)

    def act(self):
        pass

class EchoAgent(Agent):
    def receive(self, message):
        print(f"{self.name} received: {message}")
        self.send(f"Echo: {message}", other_agent)

class CoordinatorAgent(Agent):
    def receive(self, message):
        print(f"{self.name} received: {message}")
    def act(self):
        print(f"{self.name} is coordinating tasks.")

# Create agents
coordinator = CoordinatorAgent("Coordinator")
echo = EchoAgent("Echo")

# Link agents
other_agent = coordinator

# Run
coordinator.act()
echo.receive("Hello")

Important Notes

Design your agents with clear roles to keep communication simple.

Use message passing to let agents work independently but still cooperate.

Keep agent methods small and focused for easier debugging.

Summary

Agent API design patterns organize how AI agents talk and act.

They help build clear, maintainable, and cooperative AI systems.

Simple message passing and role definition are key ideas.

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