Understanding the nuances and relationships between agentic design patterns is key to effective system architecture. While distinct, these patterns often exhibit synergies and form layered structures within complex agentic systems.
Layering and Hierarchy shows how agentic patterns like Workflow, Planning, and Multi-Agent Collaboration can be organized in a clear structure. Higher-level agents coordinate specialized agents, making systems modular, scalable, and easier to manage. This section highlights how these patterns work together in layered agentic systems.
# Python Pseudocode for Layering and Hierarchy
class PlanningAgent:
def create_plan(self, task):
# Use LLM to break down the task into steps
return ["data_processing", "report_generation"]
def synthesize_results(self, results):
# Combine results from specialized agents
return f"Final result: {results}"
class SpecializedAgent:
def __init__(self, capability):
self.capability = capability
def execute(self, step):
# Perform the step (could call a tool, API, or LLM)
return f"{self.capability} done for {step}"
# System setup
planning_agent = PlanningAgent()
specialized_agents = {
"data_processing": SpecializedAgent("Data Processing"),
"report_generation": SpecializedAgent("Report Generation")
}
def execute_task(task):
plan = planning_agent.create_plan(task)
results = {}
for step in plan:
agent = specialized_agents[step]
results[step] = agent.execute(step)
return planning_agent.synthesize_results(results)
# Example usage
result = execute_task("Generate Quarterly Report")
print(result)
Interdependencies focus on how agentic patterns connect and support each other. Most systems use several patterns together—like Planning, Tool Use, and Reflection—to build more capable agents. This section explains how combining patterns leads to stronger, more flexible agentic systems.
# Python Pseudocode for Pattern Interdependencies
class Pattern:
def __init__(self, id):
self.id = id
def execute(self, context):
# Use LLM/tool to process context
return {f"{self.id}_output": f"processed_{context.get('input_data', 'default')}"}
class PatternOrchestrator:
def __init__(self):
self.patterns = {}
self.dependencies = {}
def add_pattern(self, pattern_id, pattern, dependencies=None):
self.patterns[pattern_id] = pattern
self.dependencies[pattern_id] = dependencies or []
def execute_pattern(self, pattern_id, context=None):
context = context or {}
dep_results = {}
for dep_id in self.dependencies.get(pattern_id, []):
dep_results.update(self.execute_pattern(dep_id, context))
current_context = {**context, **dep_results}
return self.patterns[pattern_id].execute(current_context)
# Example usage
orchestrator = PatternOrchestrator()
orchestrator.add_pattern('data_preparation', Pattern('data_preparation'))
orchestrator.add_pattern('data_analysis', Pattern('data_analysis'), ['data_preparation'])
orchestrator.add_pattern('report_generation', Pattern('report_generation'), ['data_analysis'])
result = orchestrator.execute_pattern('report_generation', {'input_data': 'raw_sales_records'})
print(result)
Core Reasoning compares key decision-making patterns in agentic systems, such as ReAct and Plan-and-Execute. Each approach has its strengths and best use cases. This section shows how reasoning patterns can be chosen or combined to help agents solve problems more effectively.
# Python Pseudocode for Core Reasoning
class ReasoningAgent:
def __init__(self, strategy):
self.strategy = strategy
self.history = []
def think(self, input):
self.history.append({"input": input})
if self.strategy == "react":
return self.react_reasoning(input)
elif self.strategy == "plan-and-execute":
return self.plan_and_execute(input)
else:
raise ValueError("Unknown strategy")
def react_reasoning(self, input):
# Loop: generate thought, select action, execute, observe
for _ in range(3):
thought = f"Thinking about {input}"
action = f"Act on {thought}"
observation = f"Observed result for {action}"
self.history.append({"thought": thought, "action": action, "observation": observation})
return "final answer"
def plan_and_execute(self, input):
plan = [f"Step 1 for {input}", f"Step 2 for {input}"]
self.history.append({"plan": plan})
result = input
for step in plan:
result = f"Executed {step}"
self.history.append({"step": step, "result": result})
return result
# Example usage
agent = ReasoningAgent("react")
result = agent.think("How to make coffee?")
print(result)
Flexibility vs Structure explores how agentic systems balance adaptability with predictability. By comparing flexible and structured patterns, this section shows how agents can be designed to handle both change and routine tasks, using the right mix for each situation.
# Python Pseudocode for Flexibility vs Structure
class AdaptiveSystem:
def __init__(self, flexibility_level, structure_level, threshold=0.5):
self.flexibility_level = flexibility_level
self.structure_level = structure_level
self.threshold = threshold
def handle_request(self, request):
if request["uncertainty"] > self.threshold:
return self.flexible_handler(request)
else:
return self.structured_handler(request)
def flexible_handler(self, request):
# Use LLM for dynamic problem-solving
return {"status": "flexible_handled", "result": f"Dynamically handled: {request['query']}"}
def structured_handler(self, request):
# Use rules or fixed pipeline
return {"status": "structured_handled", "result": f"Structuredly handled: {request['query']}"}
# Example usage
system = AdaptiveSystem(0.7, 0.3, threshold=0.6)
result = system.handle_request({"query": "Solve ambiguous problem", "uncertainty": 0.8})
print(result)