Introduction
The landscape of artificial intelligence is rapidly evolving, moving beyond single, monolithic models towards more distributed and autonomous entities known as Agentic AI. These agents, often working in concert within Multi-AI Agent systems, promise unprecedented capabilities in complex problem-solving, dynamic adaptation, and proactive decision-making. As with any nascent field in software engineering, the development of these sophisticated systems benefits immensely from established design and architecture patterns. These patterns provide proven solutions to recurring problems, fostering maintainability, scalability, and robustness. This article introduces a foundational pattern language for building Agentic AI and Multi-AI Agent systems, guiding software engineers through common challenges and effective solutions. The aim is to provide a comprehensive set of patterns that, when combined, make the implementation of such complex systems more structured and manageable.
At its core, an agent is an autonomous entity capable of perceiving its environment, reasoning about its observations, making decisions, and acting upon them to achieve its goals. Multi-agent systems amplify this by orchestrating multiple such agents, each potentially specialized, to achieve a collective objective. Building these systems presents unique challenges, including managing inter-agent communication, coordinating actions, handling emergent behaviors, and ensuring overall system coherence. Patterns offer a structured approach to address these complexities, providing blueprints for both the internal structure of individual agents and the external interactions within a multi-agent collective.
Each pattern described herein follows a consistent structure: its Name identifies the pattern, the Context describes the problem or situation where the pattern is applicable, the Solution outlines the core idea and implementation approach, Participants details the key components involved, and Consequences discuss the benefits and drawbacks of applying the pattern. We will use plain ASCII for all content, including code snippets, relying on clear textual explanations and indentation to convey structure.
In part 2 I‘ll show an Agentic AI example where the patterns below are being applied.
Architectural Patterns: Structuring the Macro System
Orchestrator-Agent Pattern
Context:
In scenarios where a complex task can be decomposed into smaller, specialized sub-tasks, and there is a need for centralized control, monitoring, and coordination of these sub-tasks, the Orchestrator-Agent pattern is highly effective. This pattern is particularly useful when the overall workflow requires strict sequencing, dependency management, or global state awareness that is best managed by a single, authoritative entity.
Solution:
The Orchestrator-Agent pattern introduces a central orchestrator component responsible for defining the overall workflow, dispatching tasks to specialized agents, collecting their results, and managing the lifecycle of these agents. Each specialized agent performs a specific, well-defined function and communicates its status and results back to the orchestrator. The orchestrator acts as the conductor, ensuring that the entire process unfolds as intended, handling errors, and making high-level decisions.
A simplified code structure might look like this:
class Task:
def __init__(self, task_id, data):
self.task_id = task_id
self.data = data
self.status = "PENDING"
self.result = None
class Agent:
def __init__(self, name):
self.name = name
def execute_task(self, task):
# Placeholder for actual agent logic
print(f"Agent {self.name} executing task {task.task_id} with data: {task.data}")
task.status = "COMPLETED"
task.result = f"Processed data by {self.name}"
return task
class Orchestrator:
def __init__(self):
self.agents = {}
self.tasks = []
def register_agent(self, agent_name, agent_instance):
self.agents[agent_name] = agent_instance
def add_task(self, task):
self.tasks.append(task)
def run_workflow(self):
for task in self.tasks:
if task.status == "PENDING":
# Simple dispatching logic, could be more complex
if "data_type_A" in task.data:
agent = self.agents.get("AgentA")
elif "data_type_B" in task.data:
agent = self.agents.get("AgentB")
else:
agent = None
if agent:
print(f"Orchestrator dispatching task {task.task_id} to {agent.name}")
processed_task = agent.execute_task(task)
print(f"Task {processed_task.task_id} status: {processed_task.status}, result: {processed_task.result}")
else:
print(f"No suitable agent found for task {task.task_id}")
# Example Usage:
orchestrator = Orchestrator()
orchestrator.register_agent("AgentA", Agent("AgentA"))
orchestrator.register_agent("AgentB", Agent("AgentB"))
orchestrator.add_task(Task("T001", {"type": "data_type_A", "value": 10}))
orchestrator.add_task(Task("T002", {"type": "data_type_B", "value": 20}))
orchestrator.run_workflow()
Participants:
The primary participants are the Orchestrator, which is the central controlling entity, and various specialized Agents, which are autonomous components performing specific sub-tasks.
Consequences:
This pattern provides a clear control flow, making the system easier to understand, debug, and manage. It facilitates the introduction of new agent types without significantly altering the orchestrator, provided their interfaces are consistent. However, the orchestrator can become a single point of failure or a performance bottleneck if it has to manage an excessive number of tasks or agents. This pattern also tends to limit emergent behavior, as the system's overall actions are explicitly defined by the orchestrator.
Decentralized Multi-Agent System (DMAS) Pattern
Context:
When the system requires high robustness, fault tolerance, and the ability for agents to self-organize and exhibit complex emergent behaviors without reliance on a central authority, the Decentralized Multi-Agent System pattern is appropriate. This is often seen in distributed simulations, swarm intelligence, or peer-to-peer collaboration scenarios where individual agents have local knowledge and interact directly.
Solution:
In a Decentralized Multi-Agent System, agents communicate directly with each other, typically through message passing or by observing and modifying a shared environment. Each agent possesses its own goals, decision-making logic, and internal state, acting autonomously based on its local perceptions and interactions. There is no central controller dictating overall system behavior; instead, global patterns emerge from the aggregate of individual agent actions and interactions.
A conceptual representation of an agent in such a system:
class Message:
def __init__(self, sender, receiver, content):
self.sender = sender
self.receiver = receiver
self.content = content
class AutonomousAgent:
def __init__(self, agent_id, environment):
self.agent_id = agent_id
self.environment = environment # Represents shared state or communication medium
self.inbox = []
self.knowledge = {}
def perceive(self):
# Observe environment or check inbox for new messages
new_messages = [msg for msg in self.environment.get_messages_for(self.agent_id) if msg not in self.inbox]
self.inbox.extend(new_messages)
print(f"Agent {self.agent_id} perceived new messages: {[m.content for m in new_messages]}")
def decide(self):
# Based on perception and internal state, decide on an action
if self.inbox:
msg = self.inbox.pop(0)
if "request_info" in msg.content:
info = self.knowledge.get("my_info", "No info")
reply_msg = Message(self.agent_id, msg.sender, f"info_response:{info}")
self.environment.send_message(reply_msg)
print(f"Agent {self.agent_id} replied to {msg.sender}")
elif "update_knowledge" in msg.content:
key, value = msg.content.split(":")[1].split("=")
self.knowledge[key] = value
print(f"Agent {self.agent_id} updated knowledge: {key}={value}")
else:
print(f"Agent {self.agent_id} has nothing to do right now.")
def act(self):
# Execute the decided action, potentially modifying environment or sending messages
pass # Actions are often integrated into the decide method in simple cases
def run_cycle(self):
self.perceive()
self.decide()
self.act()
class CommunicationEnvironment:
def __init__(self):
self.message_queue = []
def send_message(self, message):
self.message_queue.append(message)
print(f"Message sent from {message.sender} to {message.receiver}: {message.content}")
def get_messages_for(self, agent_id):
# In a real system, this would involve filtering or a dedicated inbox per agent
return [msg for msg in self.message_queue if msg.receiver == agent_id]
# Example Usage:
env = CommunicationEnvironment()
agent1 = AutonomousAgent("Agent1", env)
agent2 = AutonomousAgent("Agent2", env)
env.send_message(Message("External", "Agent1", "request_info"))
env.send_message(Message("Agent1", "Agent2", "update_knowledge:friend=Agent1"))
agent1.run_cycle()
agent2.run_cycle()
agent1.run_cycle() # Agent1 might process the reply
Participants:
The key participants are multiple Autonomous Agents, each with its own internal logic, and a Communication Channel or Shared Environment that facilitates direct or indirect interaction between agents.
Consequences:
The Decentralized Multi-Agent System pattern offers high resilience to individual agent failures, as the system can often continue operating even if some agents are incapacitated. It promotes emergent complexity and self-organization, leading to solutions that might not be explicitly programmed. However, debugging and understanding the overall system behavior can be significantly more challenging due to the lack of central control. Predicting outcomes can be difficult, and ensuring global coherence requires careful design of individual agent behaviors and interaction protocols.
Hierarchical Agent System Pattern
Context:
When a complex problem can be naturally decomposed into sub-problems that operate at different levels of abstraction or require different scopes of responsibility, and there is a need for structured organization and delegation within the multi-agent system, the Hierarchical Agent System pattern is highly suitable. This pattern is common in organizational structures, control systems, and planning architectures.
Solution:
The Hierarchical Agent System pattern organizes agents into a tree-like or layered structure. Higher-level agents, often called supervisor or manager agents, are responsible for broader goals, strategic planning, and delegating sub-tasks to lower-level subordinate agents. Subordinate agents focus on more specific, tactical objectives and report their progress and results back up the hierarchy. Communication primarily flows up and down the hierarchy, with higher levels aggregating information and lower levels executing detailed actions.
A conceptual structure:
class AgentNode:
def __init__(self, name, parent=None):
self.name = name
self.parent = parent
self.children = []
self.tasks = []
self.results = {}
def add_child(self, child_agent):
self.children.append(child_agent)
child_agent.parent = self
def assign_task(self, task_description, child_agent_name=None):
task_id = f"task_{len(self.tasks) + 1}"
task = {"id": task_id, "description": task_description, "status": "ASSIGNED", "assigned_to": child_agent_name}
self.tasks.append(task)
print(f"{self.name} assigned task {task_id}: {task_description}")
if child_agent_name:
for child in self.children:
if child.name == child_agent_name:
child.receive_task(task)
return
print(f"Warning: No specific child agent {child_agent_name} found for task {task_id}")
def receive_task(self, task):
self.tasks.append(task)
print(f"{self.name} received task {task['id']}: {task['description']}")
# Simulate task execution
self.execute_sub_task(task)
def execute_sub_task(self, task):
# Placeholder for actual execution logic
print(f"{self.name} is working on task {task['id']}")
# Simulate completion
task["status"] = "COMPLETED"
task["result"] = f"Result from {self.name} for {task['id']}"
self.report_result(task)
def report_result(self, task):
if self.parent:
self.parent.receive_child_result(self.name, task)
print(f"{self.name} reported result for {task['id']} to {self.parent.name}")
else:
print(f"{self.name} (Top-level) completed task {task['id']}")
def receive_child_result(self, child_name, task):
print(f"{self.name} received result from {child_name} for task {task['id']}")
self.results[task['id']] = task['result']
# Potentially aggregate results or assign new tasks
# Example Usage:
global_manager = AgentNode("GlobalManager")
project_lead_A = AgentNode("ProjectLeadA")
project_lead_B = AgentNode("ProjectLeadB")
developer_X = AgentNode("DeveloperX")
developer_Y = AgentNode("DeveloperY")
global_manager.add_child(project_lead_A)
global_manager.add_child(project_lead_B)
project_lead_A.add_child(developer_X)
project_lead_A.add_child(developer_Y)
global_manager.assign_task("Develop Feature Alpha", "ProjectLeadA")
project_lead_A.assign_task("Implement Module 1", "DeveloperX")
project_lead_A.assign_task("Implement Module 2", "DeveloperY")
# Simulate execution flow
developer_X.execute_sub_task(developer_X.tasks[0])
developer_Y.execute_sub_task(developer_Y.tasks[0])
Participants:
The main participants are Supervisor Agents, which manage and delegate, and Subordinate Agents, which execute specific tasks and report back. The hierarchy defines the relationships between them.
Consequences:
This pattern promotes scalability and modularity by breaking down complex problems into manageable sub-problems. It provides clear lines of responsibility and authority, making it easier to manage large systems and understand the flow of control and information. However, it can introduce communication overhead between layers and might suffer from rigidity if the hierarchy is too strictly defined and unable to adapt to dynamic changes.
Design Patterns: Refining Agent Interactions and Structure
Perception-Reasoning-Action (PRA) Loop Pattern
Context:
At the core of any autonomous agent lies a fundamental cycle of interaction with its environment. To exhibit intelligent behavior, an agent must be able to observe its surroundings, process that information, make decisions, and then execute actions. The Perception-Reasoning-Action Loop pattern provides a structured approach to designing this continuous operational cycle within an individual agent.
Solution:
The Perception-Reasoning-Action Loop pattern defines a continuous cycle for an agent's operation. In the "Perception" phase, the agent gathers information from its environment through sensors or communication. In the "Reasoning" phase, the agent processes the perceived information, updates its internal state or knowledge, evaluates its goals, and decides on a course of action. Finally, in the "Action" phase, the agent executes the chosen action, which might involve manipulating the environment, sending messages to other agents, or modifying its own internal state. This cycle repeats indefinitely, allowing the agent to continuously adapt and respond.
A conceptual structure for an agent's internal loop:
class AgentInternalState:
def __init__(self):
self.knowledge = {}
self.goals = []
self.current_perceptions = None
class EnvironmentInterface:
def perceive(self):
# Simulate gathering data from environment
print("Perceiving environment...")
return {"sensor_data": "value_X", "messages_received": ["msg1"]}
def act(self, action):
# Simulate performing an action in the environment
print(f"Acting in environment: {action}")
class IntelligentAgent:
def __init__(self, agent_id):
self.agent_id = agent_id
self.state = AgentInternalState()
self.env_interface = EnvironmentInterface()
def perceive(self):
# Gather information from the environment
self.state.current_perceptions = self.env_interface.perceive()
print(f"Agent {self.agent_id} perceived: {self.state.current_perceptions}")
def reason(self):
# Process perceptions, update knowledge, decide on action
print(f"Agent {self.agent_id} reasoning...")
if "msg1" in self.state.current_perceptions.get("messages_received", []):
self.state.knowledge["last_message"] = "msg1"
return {"type": "respond_to_message", "content": "Acknowledged msg1"}
elif self.state.knowledge.get("task_pending"):
return {"type": "execute_task", "task_id": self.state.knowledge["task_pending"]}
return {"type": "do_nothing"}
def act(self, action):
# Execute the decided action
self.env_interface.act(action)
if action["type"] == "respond_to_message":
print(f"Agent {self.agent_id} sent response: {action['content']}")
elif action["type"] == "execute_task":
print(f"Agent {self.agent_id} executing task: {action['task_id']}")
self.state.knowledge["task_pending"] = None # Task completed
def run_cycle(self):
self.perceive()
action_to_take = self.reason()
self.act(action_to_take)
print("-" * 20)
# Example Usage:
agent = IntelligentAgent("MyAgent")
agent.run_cycle()
agent.state.knowledge["task_pending"] = "Task_Alpha"
agent.run_cycle()
Participants:
The core participants are the Perceiver component, responsible for gathering environmental data; the Reasoner or Decider component, which processes information and determines actions; the Actor component, which executes actions in the environment; and the Agent's Internal State or Knowledge Base, which stores its beliefs, goals, and other relevant information.
Consequences:
This pattern provides a clear and modular structure for an individual agent's behavior, making it easier to design, implement, and test each component independently. It naturally supports continuous operation and adaptation. However, the complexity of the reasoning component can vary greatly, and ensuring timely responses in real-time environments requires careful consideration of the processing time for each phase within the loop.
Goal-Oriented Agent Pattern
Context:
Agents are typically designed to achieve specific objectives. Without a structured way to manage, prioritize, and pursue these objectives, an agent's behavior can become unfocused or inefficient. The Goal-Oriented Agent pattern addresses the need for agents to maintain and actively work towards a set of defined goals.
Solution:
The Goal-Oriented Agent pattern equips an agent with a Goal Manager component that maintains a set of active goals. Each goal has a state (e.g., pending, active, achieved, failed) and potentially a priority. The agent's reasoning process (often part of the PRA loop) consults the Goal Manager to select the most relevant or highest-priority goal to pursue. It then uses a Planning component to devise a sequence of actions to achieve that goal. The agent monitors its progress towards goals and updates their status, potentially triggering new sub-goals or abandoning unattainable ones.
A simplified goal management system:
class Goal:
def __init__(self, name, priority=1, status="PENDING"):
self.name = name
self.priority = priority
self.status = status
self.sub_goals = []
def __repr__(self):
return f"Goal({self.name}, Status: {self.status}, Prio: {self.priority})"
class GoalManager:
def __init__(self):
self.goals = []
def add_goal(self, goal):
self.goals.append(goal)
self.goals.sort(key=lambda g: g.priority, reverse=True) # Higher priority first
def get_next_active_goal(self):
for goal in self.goals:
if goal.status == "PENDING" or goal.status == "ACTIVE":
return goal
return None
def update_goal_status(self, goal_name, new_status):
for goal in self.goals:
if goal.name == goal_name:
goal.status = new_status
print(f"Goal '{goal_name}' status updated to {new_status}")
return True
return False
class PlanningComponent:
def plan_actions(self, goal):
print(f"Planning actions for goal: {goal.name}")
if goal.name == "ExploreArea":
return ["move_north", "move_east", "report_discovery"]
elif goal.name == "FindResource":
return ["scan_area", "locate_resource", "collect_resource"]
return ["do_nothing"]
class GoalOrientedAgent:
def __init__(self, agent_id):
self.agent_id = agent_id
self.goal_manager = GoalManager()
self.planner = PlanningComponent()
self.current_actions = []
def add_new_goal(self, name, priority=1):
self.goal_manager.add_goal(Goal(name, priority))
def process_goals(self):
next_goal = self.goal_manager.get_next_active_goal()
if next_goal:
if next_goal.status == "PENDING":
next_goal.status = "ACTIVE"
self.current_actions = self.planner.plan_actions(next_goal)
print(f"Agent {self.agent_id} activated goal: {next_goal.name}, actions: {self.current_actions}")
if self.current_actions:
action = self.current_actions.pop(0)
print(f"Agent {self.agent_id} performing action: {action}")
# Simulate action effect
if action == "report_discovery":
self.goal_manager.update_goal_status(next_goal.name, "ACHIEVED")
elif action == "collect_resource":
self.goal_manager.update_goal_status(next_goal.name, "ACHIEVED")
else:
# If actions are exhausted but goal not achieved, it might be failed or need replanning
if next_goal.status == "ACTIVE":
self.goal_manager.update_goal_status(next_goal.name, "FAILED") # Or replan
else:
print(f"Agent {self.agent_id} has no active goals.")
# Example Usage:
agent = GoalOrientedAgent("ExplorerBot")
agent.add_new_goal("FindResource", priority=2)
agent.add_new_goal("ExploreArea", priority=1)
agent.process_goals() # Should pick FindResource
agent.process_goals() # Continues FindResource
agent.process_goals() # Continues FindResource, then achieves it
agent.process_goals() # Should pick ExploreArea
Participants:
The main participants are the Goal Manager, which stores and prioritizes goals; the Planning component, which generates action sequences; and the individual Goals themselves, representing the agent's objectives.
Consequences:
This pattern enables purposeful and persistent agent behavior, allowing agents to focus on achieving specific outcomes. It facilitates dynamic goal management, where goals can be added, removed, or reprioritized during runtime. However, designing effective planning components can be complex, especially for agents operating in dynamic and uncertain environments. The agent's performance is heavily dependent on the quality of its goal management and planning algorithms.
Agent Role Pattern
Context:
In multi-agent systems, different agents often need to perform distinct functions and exhibit specific behaviors. Defining these responsibilities clearly and consistently is crucial for system design, development, and maintenance. The Agent Role pattern addresses the need to categorize and encapsulate the unique capabilities and interaction protocols of various agent types.
Solution:
The Agent Role pattern defines a common interface or an abstract base class for all agents, and then provides concrete implementations for each specific role an agent can play within the system. Each concrete agent role encapsulates its unique set of goals, decision-making logic, and interaction protocols, ensuring a clear separation of concerns and promoting reusability. This allows for the creation of agents that are highly specialized in their function.
An example of defining agent roles:
from abc import ABC, abstractmethod
class AgentRole(ABC):
def __init__(self, agent_id):
self.agent_id = agent_id
@abstractmethod
def perform_action(self, context):
pass
@abstractmethod
def handle_message(self, message):
pass
class DataCollectorAgent(AgentRole):
def __init__(self, agent_id, data_source):
super().__init__(agent_id)
self.data_source = data_source
self.collected_data = []
def perform_action(self, context):
print(f"DataCollectorAgent {self.agent_id} collecting data from {self.data_source}")
# Simulate data collection
new_data = f"data_point_{len(self.collected_data) + 1}"
self.collected_data.append(new_data)
return new_data
def handle_message(self, message):
if "request_data" in message:
print(f"DataCollectorAgent {self.agent_id} received request for data.")
return f"Here is my data: {self.collected_data}"
return "Message not understood by DataCollector."
class AnalyzerAgent(AgentRole):
def __init__(self, agent_id):
super().__init__(agent_id)
self.analysis_results = []
def perform_action(self, context):
if "data_to_analyze" in context:
data = context["data_to_analyze"]
print(f"AnalyzerAgent {self.agent_id} analyzing: {data}")
result = f"Analysis of '{data}' is 'insight_{len(self.analysis_results) + 1}'"
self.analysis_results.append(result)
return result
return "No data to analyze."
def handle_message(self, message):
if "analyze_this" in message:
data_to_process = message.split(":")[1]
return self.perform_action({"data_to_analyze": data_to_process})
return "Message not understood by Analyzer."
# Example Usage:
collector = DataCollectorAgent("Collector1", "SensorNetwork")
analyzer = AnalyzerAgent("Analyzer1")
data_point = collector.perform_action(None)
analysis = analyzer.handle_message(f"analyze_this:{data_point}")
print(analysis)
Participants:
The primary participants are the AgentRole (an abstract interface or base class) and various ConcreteAgentRole implementations, each representing a specific type of agent with unique behaviors and responsibilities.
Consequences:
This pattern promotes modularity and reusability of agent types. It provides a clear separation of concerns, making it easier to understand, develop, and test individual agent behaviors. Adding new agent types becomes straightforward, as long as they adhere to the common agent interface. A potential drawback is the proliferation of roles if not managed carefully, leading to a complex class hierarchy.
Shared Blackboard Pattern
Context:
When multiple agents need to cooperate on a common problem by sharing and accessing a centralized, dynamic knowledge base or state, and coordinate implicitly through modifications to this shared space, the Shared Blackboard pattern is highly effective. This is particularly useful in complex problem-solving domains where different agents contribute partial solutions or observations that need to be integrated.
Solution:
The Shared Blackboard pattern introduces a central data store, known as the blackboard, which serves as a common repository for problems, partial solutions, and relevant data. Agents, often referred to as knowledge sources, monitor the blackboard for information relevant to their expertise. When an agent finds relevant information, it performs its specialized processing, updates the blackboard with its findings, and potentially triggers other agents. This implicit coordination allows agents to work asynchronously and independently, contributing to the overall solution.
A basic blackboard system:
class Blackboard:
def __init__(self):
self.knowledge_base = {}
self.subscribers = []
def post_data(self, key, value):
print(f"Blackboard: Posting data '{key}' = '{value}'")
self.knowledge_base[key] = value
self._notify_subscribers(key, value)
def get_data(self, key):
return self.knowledge_base.get(key)
def subscribe(self, agent_instance, interested_keys=None):
self.subscribers.append({"agent": agent_instance, "keys": interested_keys})
def _notify_subscribers(self, key, value):
for sub in self.subscribers:
if sub["keys"] is None or key in sub["keys"]:
sub["agent"].receive_blackboard_update(key, value)
class KnowledgeSourceAgent:
def __init__(self, name, blackboard):
self.name = name
self.blackboard = blackboard
self.blackboard.subscribe(self)
def receive_blackboard_update(self, key, value):
print(f"Agent {self.name} noticed update: {key} = {value}")
if key == "problem_statement" and "solve" in value:
print(f"Agent {self.name} is working on the problem: {value}")
# Simulate solving
solution = f"Solution by {self.name} for '{value}'"
self.blackboard.post_data("solution_part", solution)
elif key == "solution_part" and "final_assembly" not in self.blackboard.knowledge_base:
print(f"Agent {self.name} integrating solution part: {value}")
# Simulate integration
final_solution = f"Final solution incorporating: {value}"
self.blackboard.post_data("final_assembly", final_solution)
# Example Usage:
blackboard = Blackboard()
solver_agent = KnowledgeSourceAgent("SolverAgent", blackboard)
integrator_agent = KnowledgeSourceAgent("IntegratorAgent", blackboard)
blackboard.post_data("problem_statement", "solve_equation_X")
# Agents will react to this post and update the blackboard
print(f"Final state of blackboard: {blackboard.knowledge_base}")
Participants:
The central participant is the Blackboard, which acts as the shared data repository. Knowledge Sources (agents) are the active components that read from and write to the blackboard, contributing to the problem-solving process.
Consequences:
This pattern provides strong decoupling between agents, as they do not need to know about each other directly; they only interact with the blackboard. This flexibility makes it easy to add or remove agents without affecting the existing system. However, managing contention for the blackboard, ensuring data consistency, and tracking the causality of actions can be challenging. Debugging can also be difficult due to the asynchronous and implicit nature of agent interactions.
Mediator Agent Pattern
Context:
When a group of agents needs to interact and collaborate, but direct, point-to-point communication between all possible pairs of agents leads to a complex web of dependencies and makes the system difficult to manage and extend, the Mediator Agent pattern is highly beneficial. This pattern aims to centralize the communication logic.
Solution:
The Mediator Agent pattern introduces a dedicated mediator agent that encapsulates how a set of agents interact. Instead of agents communicating directly with each other, they communicate only with the mediator. The mediator then forwards messages or orchestrates interactions between the relevant agents. This centralizes the communication logic, reducing the direct coupling between agents and simplifying their individual interfaces.
A simplified mediator and colleague agents:
class AgentColleague:
def __init__(self, name, mediator):
self.name = name
self.mediator = mediator
self.mediator.register_agent(self)
def send_message(self, message_content, receiver_name):
print(f"Agent {self.name} sending message to {receiver_name} via mediator: {message_content}")
self.mediator.distribute_message(self.name, receiver_name, message_content)
def receive_message(self, sender_name, message_content):
print(f"Agent {self.name} received message from {sender_name}: {message_content}")
# Process the message
if "request_status" in message_content:
self.send_message(f"My status is OK from {self.name}", sender_name)
class AgentMediator:
def __init__(self):
self.agents = {}
def register_agent(self, agent_colleague):
self.agents[agent_colleague.name] = agent_colleague
print(f"Mediator registered agent: {agent_colleague.name}")
def distribute_message(self, sender_name, receiver_name, message_content):
if receiver_name in self.agents:
self.agents[receiver_name].receive_message(sender_name, message_content)
else:
print(f"Mediator: Receiver {receiver_name} not found for message from {sender_name}")
# Example Usage:
mediator = AgentMediator()
agent_a = AgentColleague("AgentA", mediator)
agent_b = AgentColleague("AgentB", mediator)
agent_c = AgentColleague("AgentC", mediator)
agent_a.send_message("Hello B, how are you?", "AgentB")
agent_b.send_message("AgentA, I'm fine, thanks!", "AgentA")
agent_c.send_message("Requesting status from AgentA", "AgentA")
Participants:
The key participants are the Mediator, which manages and facilitates communication, and multiple Colleague Agents, which communicate with each other only through the mediator.
Consequences:
The Mediator Agent pattern significantly reduces direct coupling between agents, simplifying their interfaces and making the system more flexible and easier to maintain. It centralizes control over interactions, which can simplify debugging and allow for easier modification of communication protocols. However, the mediator can become a bottleneck if it handles too much traffic, and it represents a single point of failure. The complexity of interaction logic is moved from individual agents into the mediator.
Agent Communication Language (ACL) Pattern
Context:
In multi-agent systems, especially those involving heterogeneous agents developed by different teams or using different underlying technologies, a common understanding of messages exchanged between agents is critical. Without a standardized communication protocol, agents cannot effectively collaborate. The Agent Communication Language pattern addresses this need by providing a structured and semantically rich language for inter-agent communication.
Solution:
The Agent Communication Language pattern defines a standardized language for agents to exchange messages. This language typically specifies the structure of a message, including performatives (illocutionary acts like "inform," "request," "query," "propose"), content (the actual information being conveyed), and other parameters like sender, receiver, and reply-to. By adhering to a common ACL, agents can achieve interoperability and unambiguous communication, regardless of their internal implementation details. Standards like FIPA ACL are prime examples of this pattern.
A simplified message structure:
class ACLMessage:
def __init__(self, performative, sender, receiver, content, conversation_id=None):
self.performative = performative # e.g., "inform", "request", "query", "propose"
self.sender = sender
self.receiver = receiver
self.content = content
self.conversation_id = conversation_id if conversation_id else self._generate_id()
def _generate_id(self):
import uuid
return str(uuid.uuid4())
def to_string(self):
return (f"ACLMessage(Performative: {self.performative}, "
f"Sender: {self.sender}, Receiver: {self.receiver}, "
f"Content: '{self.content}', ConversationID: {self.conversation_id})")
class CommunicatingAgent:
def __init__(self, name, message_broker):
self.name = name
self.message_broker = message_broker
self.message_broker.register_agent(self)
self.inbox = []
def send(self, performative, receiver_name, content, conversation_id=None):
message = ACLMessage(performative, self.name, receiver_name, content, conversation_id)
print(f"Agent {self.name} sending: {message.to_string()}")
self.message_broker.send_message(message)
def receive_message(self, message):
self.inbox.append(message)
print(f"Agent {self.name} received: {message.to_string()}")
self.process_message(message)
def process_message(self, message):
if message.performative == "request":
if "get_data" in message.content:
response_content = f"Data from {self.name}: ValueX"
self.send("inform", message.sender, response_content, message.conversation_id)
elif message.performative == "inform":
print(f"Agent {self.name} processed information: {message.content}")
class SimpleMessageBroker:
def __init__(self):
self.agents = {}
def register_agent(self, agent):
self.agents[agent.name] = agent
def send_message(self, message):
if message.receiver in self.agents:
self.agents[message.receiver].receive_message(message)
else:
print(f"Broker: Receiver {message.receiver} not found for message from {message.sender}")
# Example Usage:
broker = SimpleMessageBroker()
agent_alpha = CommunicatingAgent("Alpha", broker)
agent_beta = CommunicatingAgent("Beta", broker)
agent_alpha.send("request", "Beta", "get_data_from_beta")
# Beta receives and sends an inform message back
# Alpha processes the inform message
Participants:
The primary participants are the Sender Agent, the Receiver Agent, and the Message itself, which is structured according to the defined Agent Communication Language. A communication infrastructure (like a message broker) often facilitates the exchange.
Consequences:
This pattern ensures interoperability between heterogeneous agents, promoting clear and unambiguous communication. It allows for the development of robust interaction protocols and facilitates the integration of agents from different sources. However, it introduces overhead due to the need for message parsing and generation, and requires all participating agents to agree upon and correctly implement the chosen ACL.
Capability-Based Agent Pattern
Context:
In dynamic multi-agent systems, agents often need to discover and utilize the services or capabilities offered by other agents without prior knowledge of their existence or specific identities. This is particularly relevant in open systems where agents can join or leave dynamically. The Capability-Based Agent pattern addresses the need for agents to advertise their functionalities and for other agents to discover them.
Solution:
The Capability-Based Agent pattern involves agents explicitly declaring the capabilities or services they provide. These declarations are typically registered with a central Service Directory (also known as a Yellow Pages service) or propagated through a decentralized discovery mechanism. When an agent requires a specific service, it queries the Service Directory or broadcasts a request for capabilities. Agents offering the required capability then respond, establishing a connection for interaction. This allows for flexible and dynamic composition of multi-agent systems.
A simple service directory:
class Capability:
def __init__(self, name, description, parameters=None):
self.name = name
self.description = description
self.parameters = parameters if parameters else {}
def __repr__(self):
return f"Capability({self.name})"
class ServiceDirectory:
def __init__(self):
self.registered_services = {} # {capability_name: [agent_id1, agent_id2]}
def register_service(self, agent_id, capability):
if capability.name not in self.registered_services:
self.registered_services[capability.name] = []
self.registered_services[capability.name].append(agent_id)
print(f"ServiceDirectory: Agent {agent_id} registered capability {capability.name}")
def find_agents_with_capability(self, capability_name):
return self.registered_services.get(capability_name, [])
class ServiceProviderAgent:
def __init__(self, agent_id, service_directory):
self.agent_id = agent_id
self.service_directory = service_directory
self.capabilities = []
def add_capability(self, capability):
self.capabilities.append(capability)
self.service_directory.register_service(self.agent_id, capability)
def perform_service(self, capability_name, request_data):
print(f"Agent {self.agent_id} performing service '{capability_name}' for data: {request_data}")
return f"Result from {self.agent_id} for {capability_name} with {request_data}"
class ServiceRequesterAgent:
def __init__(self, agent_id, service_directory):
self.agent_id = agent_id
self.service_directory = service_directory
def request_service(self, capability_name, request_data):
print(f"Agent {self.agent_id} requesting service '{capability_name}'")
providers = self.service_directory.find_agents_with_capability(capability_name)
if providers:
chosen_provider_id = providers[0] # Simple choice, could be more complex
print(f"Agent {self.agent_id} found provider {chosen_provider_id} for '{capability_name}'")
# In a real system, this would involve sending an ACL message to the provider
# For this example, we simulate direct call
for agent in all_agents_in_system: # Placeholder for actual agent lookup
if agent.agent_id == chosen_provider_id:
result = agent.perform_service(capability_name, request_data)
print(f"Agent {self.agent_id} received result: {result}")
return result
else:
print(f"Agent {self.agent_id} could not find provider for '{capability_name}'")
return None
# Example Usage:
service_dir = ServiceDirectory()
all_agents_in_system = [] # To simulate direct call for demo
data_analyst = ServiceProviderAgent("DataAnalyst1", service_dir)
data_analyst.add_capability(Capability("AnalyzeData", "Performs statistical analysis"))
all_agents_in_system.append(data_analyst)
report_generator = ServiceProviderAgent("ReportGen1", service_dir)
report_generator.add_capability(Capability("GenerateReport", "Creates reports from data"))
all_agents_in_system.append(report_generator)
client_agent = ServiceRequesterAgent("ClientAgent1", service_dir)
client_agent.request_service("AnalyzeData", {"dataset_id": "DS001"})
client_agent.request_service("GenerateReport", {"analysis_result": "AR001"})
Participants:
The main participants are Service Provider Agents, which offer capabilities; Service Requester Agents, which need capabilities; and a Service Directory (or equivalent distributed mechanism), which facilitates the discovery process.
Consequences:
This pattern promotes high flexibility and extensibility in multi-agent systems, allowing agents to be added or removed dynamically without requiring changes to other agents. It supports dynamic system composition and self-organization. However, the discovery mechanism itself can become a bottleneck or single point of failure if centralized, and managing the semantics of capabilities across diverse agents can be challenging.
Negotiation Pattern
Context:
In multi-agent systems, agents often have conflicting goals, limited resources, or need to agree on terms for collaboration. Direct competition or uncoordinated actions can lead to suboptimal outcomes or system deadlocks. The Negotiation pattern provides a structured approach for agents to resolve conflicts, allocate resources, or reach mutually beneficial agreements.
Solution:
The Negotiation pattern defines a protocol through which agents can exchange proposals, counter-proposals, and commitments to reach an agreement. This typically involves a series of communication steps where agents articulate their preferences, make offers, and evaluate received offers based on their internal utilities or criteria. Common negotiation types include auctions, bargaining, and argumentation-based negotiation. The process continues until an agreement is reached, one party withdraws, or a deadline is met.
A highly simplified example of a bilateral negotiation:
class Proposal:
def __init__(self, item, price):
self.item = item
self.price = price
def __repr__(self):
return f"Proposal(Item: {self.item}, Price: {self.price})"
class NegotiatorAgent:
def __init__(self, name, preferred_price, current_item_value):
self.name = name
self.preferred_price = preferred_price
self.current_item_value = current_item_value
self.last_offer_received = None
def make_offer(self):
# Simple strategy: start at preferred price, then adjust based on last offer
if self.last_offer_received:
# Counter-offer: try to meet halfway or slightly better for self
new_price = (self.preferred_price + self.last_offer_received.price) / 2
print(f"Agent {self.name} counter-offering: {new_price}")
return Proposal("item_X", new_price)
else:
print(f"Agent {self.name} making initial offer: {self.preferred_price}")
return Proposal("item_X", self.preferred_price)
def evaluate_offer(self, offer):
self.last_offer_received = offer
print(f"Agent {self.name} evaluating offer: {offer}")
if offer.price <= self.current_item_value: # Buyer's perspective
print(f"Agent {self.name} ACCEPTS offer!")
return "ACCEPT"
elif offer.price > self.current_item_value * 1.2: # Too high
print(f"Agent {self.name} REJECTS offer!")
return "REJECT"
else:
print(f"Agent {self.name} CONSIDERS counter-offer.")
return "COUNTER"
# Example Usage:
buyer = NegotiatorAgent("Buyer", preferred_price=80, current_item_value=100) # Willing to pay up to 100
seller = NegotiatorAgent("Seller", preferred_price=120, current_item_value=90) # Willing to sell for min 90
# Round 1
seller_offer = seller.make_offer()
buyer_response = buyer.evaluate_offer(seller_offer)
print(f"Buyer response: {buyer_response}")
# Round 2 (if not accepted)
if buyer_response == "COUNTER":
buyer_offer = buyer.make_offer()
seller_response = seller.evaluate_offer(buyer_offer)
print(f"Seller response: {seller_response}")
# Further rounds would continue until ACCEPT/REJECT or max rounds reached
Participants:
The key participants are the Proposer Agent, which initiates an offer; the Responder Agent, which evaluates and potentially counters the offer; and the Negotiation Protocol, which defines the rules and steps for the interaction.
Consequences:
This pattern enables agents to reach agreements in complex situations, leading to more flexible and robust multi-agent systems. It can optimize resource allocation and resolve conflicts without requiring central control. However, designing effective negotiation strategies and protocols can be highly complex, especially when dealing with multiple agents, diverse preferences, and incomplete information. The computational overhead of negotiation can also be significant.
Learning Agent Pattern
Context:
For agents to operate effectively in dynamic, uncertain, or evolving environments, they often need to adapt their behavior, improve their decision-making, or acquire new knowledge over time. Hard-coding all possible responses is impractical. The Learning Agent pattern addresses the need for agents to learn from experience and improve their performance.
Solution:
The Learning Agent pattern integrates a learning component into an agent's architecture. This component allows the agent to modify its internal models, knowledge base, or decision-making policies based on new data or feedback from its actions. Learning can take various forms, such as reinforcement learning (learning from rewards/penalties), supervised learning (learning from labeled examples), or unsupervised learning (finding patterns in data). The agent's perception-reasoning-action loop incorporates mechanisms to collect experience, update its learning models, and then utilize these improved models for future decisions.
A conceptual learning agent:
class KnowledgeBase:
def __init__(self):
self.facts = {}
self.rules = []
def update_fact(self, key, value):
self.facts[key] = value
print(f"KnowledgeBase updated: {key} = {value}")
def add_rule(self, rule):
self.rules.append(rule)
print(f"KnowledgeBase added rule: {rule}")
class LearningComponent:
def __init__(self, knowledge_base):
self.knowledge_base = knowledge_base
self.experience_log = []
def record_experience(self, observation, action, reward):
self.experience_log.append({"obs": observation, "act": action, "rew": reward})
print(f"LearningComponent recorded experience: {observation}, {action}, {reward}")
def learn_from_experience(self):
if len(self.experience_log) > 2: # Simple trigger for learning
last_exp = self.experience_log[-1]
if last_exp["rew"] > 0 and "positive_outcome" not in self.knowledge_base.facts:
self.knowledge_base.update_fact("positive_outcome", True)
self.knowledge_base.add_rule("If positive_outcome then favor similar_actions")
print("LearningComponent: Learned from positive outcome!")
elif last_exp["rew"] < 0 and "negative_outcome" not in self.knowledge_base.facts:
self.knowledge_base.update_fact("negative_outcome", True)
self.knowledge_base.add_rule("If negative_outcome then avoid similar_actions")
print("LearningComponent: Learned from negative outcome!")
class AdaptiveAgent:
def __init__(self, agent_id):
self.agent_id = agent_id
self.knowledge_base = KnowledgeBase()
self.learning_comp = LearningComponent(self.knowledge_base)
self.current_state = {}
def perceive(self, environment_data):
self.current_state["env_data"] = environment_data
print(f"Agent {self.agent_id} perceived: {environment_data}")
def decide(self):
# Decision influenced by learned knowledge
if self.knowledge_base.facts.get("positive_outcome"):
print("Agent {self.agent_id} favoring actions due to past positive outcome.")
return "perform_optimized_action"
elif self.knowledge_base.facts.get("negative_outcome"):
print("Agent {self.agent_id} avoiding actions due to past negative outcome.")
return "perform_safe_action"
return "perform_default_action"
def act(self, action):
print(f"Agent {self.agent_id} performing action: {action}")
# Simulate action and get reward/feedback
reward = 0
if action == "perform_optimized_action":
reward = 1 # Positive outcome
elif action == "perform_safe_action":
reward = -1 # Negative outcome avoided
else:
reward = 0.1 # Neutral
self.learning_comp.record_experience(self.current_state["env_data"], action, reward)
self.learning_comp.learn_from_experience()
return reward
def run_cycle(self, env_data):
self.perceive(env_data)
action = self.decide()
self.act(action)
print("-" * 20)
# Example Usage:
agent = AdaptiveAgent("AdaptiveBot")
agent.run_cycle("initial_state")
agent.run_cycle("another_state") # Agent might learn from previous cycles
agent.run_cycle("final_state")
Participants:
The core participants are the Learning Component, which implements the learning algorithms; the Experience/Data Store, which collects observations, actions, and feedback; and the Agent's Knowledge Base or Policy, which is updated by the learning process and influences future decisions.
Consequences:
This pattern allows agents to adapt and improve their performance over time, making them more robust in dynamic environments. It reduces the need for explicit programming of all behaviors. However, implementing effective learning mechanisms can be complex and computationally intensive. Learning can also lead to unpredictable or undesirable emergent behaviors if not properly constrained or monitored.
Environment Interface Pattern
Context:
Agents, by definition, interact with an environment. This environment can be physical, simulated, or purely digital. To maintain modularity and allow agents to be deployed in different environments without significant code changes, a clear separation between the agent's internal logic and its interaction with the external world is necessary. The Environment Interface pattern addresses this need.
Solution:
The Environment Interface pattern introduces a dedicated layer or set of components that mediate all interactions between an agent and its external environment. This interface typically consists of "sensors" that abstract the process of perceiving environmental data and "actuators" that abstract the execution of actions within the environment. The agent's core logic interacts only with this abstract interface, decoupling it from the specific details of the underlying environment (e.g., a robotic arm vs. a software API).
A conceptual environment interface:
class Sensor:
def __init__(self, name):
self.name = name
def get_data(self):
# Simulate reading sensor data
return f"data_from_{self.name}"
class Actuator:
def __init__(self, name):
self.name = name
def perform_action(self, action_data):
# Simulate performing an action
print(f"Actuator {self.name} performing: {action_data}")
class EnvironmentProxy:
def __init__(self):
self.sensors = {"camera": Sensor("Camera"), "microphone": Sensor("Microphone")}
self.actuators = {"gripper": Actuator("Gripper"), "speaker": Actuator("Speaker")}
def get_perceptions(self):
perceptions = {}
for name, sensor in self.sensors.items():
perceptions[name] = sensor.get_data()
return perceptions
def execute_action(self, action_type, action_data):
if action_type in self.actuators:
self.actuators[action_type].perform_action(action_data)
else:
print(f"Error: Unknown actuator type {action_type}")
class EnvironmentalAgent:
def __init__(self, agent_id, env_proxy):
self.agent_id = agent_id
self.env_proxy = env_proxy
def observe_environment(self):
perceptions = self.env_proxy.get_perceptions()
print(f"Agent {self.agent_id} observed: {perceptions}")
return perceptions
def take_action(self, action_type, action_data):
print(f"Agent {self.agent_id} requesting action: {action_type} with {action_data}")
self.env_proxy.execute_action(action_type, action_data)
def run_logic(self):
perceptions = self.observe_environment()
if "data_from_Camera" in perceptions.get("camera", ""):
self.take_action("gripper", "grasp_object")
elif "data_from_Microphone" in perceptions.get("microphone", ""):
self.take_action("speaker", "say_hello")
else:
print(f"Agent {self.agent_id} has no specific action for current perceptions.")
# Example Usage:
env_proxy = EnvironmentProxy()
robot_agent = EnvironmentalAgent("RobotArm", env_proxy)
robot_agent.run_logic()
Participants:
The primary participants are the Environment Proxy, which serves as the agent's gateway to the external world; various Sensor components, which gather specific types of data; and various Actuator components, which perform specific actions.
Consequences:
This pattern significantly improves the portability and reusability of agent code by decoupling the agent's core intelligence from environmental specifics. It simplifies testing, as different environments (e.g., simulation, real-world) can be swapped out easily. However, designing a comprehensive and flexible environment interface that covers all necessary perceptions and actions can be challenging, and it introduces a layer of abstraction that might add slight overhead.
Conclusion
The development of Agentic AI and Multi-AI Agent systems is a frontier in software engineering, demanding sophisticated architectural and design considerations. The patterns outlined in this article – from high-level architectural structures like Orchestrator-Agent, Decentralized Multi-Agent Systems, and Hierarchical Agent Systems, to more granular design patterns such as the Perception-Reasoning-Action Loop, Goal-Oriented Agent, Agent Role, Shared Blackboard, Mediator Agent, Agent Communication Language, Capability-Based Agent, Negotiation, and Learning Agent – provide a comprehensive pattern language. These patterns offer proven solutions to common challenges, helping engineers build more robust, scalable, and maintainable agent-based applications. By combining these patterns, software engineers can systematically address the complexities of agent internal structure, inter-agent communication, coordination, adaptation, and environmental interaction, thereby streamlining the implementation of sophisticated autonomous systems. As this field continues to evolve, new patterns will undoubtedly emerge, reflecting deeper insights into the complexities of intelligent, autonomous systems. Thoughtful application and adaptation of these patterns will be key to unlocking the full potential of agentic AI.
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