Market research report generation using large language models (LLMs) has become increasingly viable as these models continue to evolve. Learn more about LLM applications in various industries. However, orchestrating such intricate tasks requires a well-designed agentic workflow. In this blog post, we’ll explore how to design an agentic workflow with specialized agents, establish communication protocols between them, and set up mechanisms for task delegation and escalation in high-stakes scenarios. We’ll also provide code demonstrations using popular Python frameworks where applicable.
Table of Contents
- Introduction
-
Agentic Workflow Design
- Agent Specialization
- Communication Protocols
- Task Delegation and Escalation
-
Implementation
- Technologies Used
- Setting Up Specialized Agents
- Establishing Communication Protocols
- Task Delegation Mechanisms
-
Code Demonstration
- Defining Agents
- Agent Communication
- Task Delegation and Escalation
- Conclusion
Introduction
Generating a full market research report is a complex task that involves data collection, analysis, interpretation, and presentation. Leveraging LLMs for such tasks requires an orchestrated approach where different specialized agents work collaboratively to achieve the end goal. An agentic workflow facilitates this by assigning specific roles to agents, defining how they communicate, and establishing protocols for task management.Agentic Workflow Design
Designing an agentic workflow involves several key components:Agent Specialization
Specializing agents allows for modularity and efficiency. Each agent focuses on a specific aspect of the task, such as:- Data Collection Agent: Gathers relevant market data from various sources.
- Data Processing Agent: Cleans and preprocesses the collected data.
- Analysis Agent: Performs data analysis to extract insights.
- Report Generation Agent: Compiles the findings into a structured report.
- Quality Assurance Agent: Reviews the report for accuracy and coherence.
Communication Protocols
Establishing robust communication protocols ensures agents can interact effectively:- Message Passing: Agents send and receive messages containing task-related information.
- Shared Memory or Databases: Use a shared data store where agents can read and write data.
- Event-Driven Architecture: Agents trigger events that other agents listen to and act upon.
Task Delegation and Escalation
In high-stakes scenarios, it’s crucial to have mechanisms for:- Task Delegation: Assigning tasks to the most suitable agent based on expertise.
- Escalation Protocols: Escalating issues to supervisory agents or human operators when agents encounter problems they can’t solve.
Implementation
Technologies Used
- Python: The primary programming language.
- LangChain: A framework for developing applications powered by LLMs.
- AsyncIO: For asynchronous agent communication.
- SQLite or Redis: For shared data storage (depending on scalability needs).
Setting Up Specialized Agents
Each agent is designed as a class or module with specific methods:- DataCollectorAgent
- DataProcessorAgent
- AnalysisAgent
- ReportGeneratorAgent
- QualityAssuranceAgent
Establishing Communication Protocols
Agents communicate through:- AsyncIO Queues: For message passing.
- Shared Database: For storing intermediate data and results.
Task Delegation Mechanisms
Implementing a TaskManager agent or module that:- Assigns tasks to agents based on a predefined workflow.
- Monitors agent performance and handles escalations.
Code Demonstration
Let’s dive into some code examples to illustrate how this can be implemented.Defining Agents
First, we’ll define base classes for our agents.
import asyncio
class BaseAgent:
def __init__(self, name, task_queue, result_queue):
self.name = name
self.task_queue = task_queue
self.result_queue = result_queue
async def run(self):
while True:
task = await self.task_queue.get()
if task is None:
break
result = await self.handle_task(task)
await self.result_queue.put(result)
async def handle_task(self, task):
raise NotImplementedError("This method should be overridden by subclasses.")
class DataCollectorAgent(BaseAgent):
async def handle_task(self, task):
print(f"{self.name} collecting data for {task['topic']}")
# Simulate data collection
await asyncio.sleep(1)
data = f"Data about {task['topic']}"
return {'agent': self.name, 'data': data}
class DataProcessorAgent(BaseAgent):
async def handle_task(self, task):
print(f"{self.name} processing data")
# Simulate data processing
await asyncio.sleep(1)
processed_data = task['data'].upper()
return {'agent': self.name, 'processed_data': processed_data}
class AnalysisAgent(BaseAgent):
async def handle_task(self, task):
print(f"{self.name} analyzing data")
# Simulate data analysis
await asyncio.sleep(1)
insights = f"Insights from {task['processed_data']}"
return {'agent': self.name, 'insights': insights}
class ReportGeneratorAgent(BaseAgent):
async def handle_task(self, task):
print(f"{self.name} generating report")
# Simulate report generation
await asyncio.sleep(1)
report = f"Market Research Report: {task['insights']}"
return {'agent': self.name, 'report': report}
class QualityAssuranceAgent(BaseAgent):
async def handle_task(self, task):
print(f"{self.name} reviewing report")
# Simulate quality assurance
await asyncio.sleep(1)
approved = True # Placeholder for actual QA logic
return {'agent': self.name, 'approved': approved, 'report': task['report']}
Agent Communication
Set up the queues and initialize the agents.
async def main():
# Queues for communication
task_queue_dc_dp = asyncio.Queue()
task_queue_dp_an = asyncio.Queue()
task_queue_an_rg = asyncio.Queue()
task_queue_rg_qa = asyncio.Queue()
result_queue = asyncio.Queue()
# Initialize agents
data_collector = DataCollectorAgent("DataCollector", task_queue_dc_dp, task_queue_dp_an)
data_processor = DataProcessorAgent("DataProcessor", task_queue_dp_an, task_queue_an_rg)
analysis_agent = AnalysisAgent("AnalysisAgent", task_queue_an_rg, task_queue_rg_qa)
report_generator = ReportGeneratorAgent("ReportGenerator", task_queue_rg_qa, result_queue)
qa_agent = QualityAssuranceAgent("QualityAssurance", result_queue, asyncio.Queue())
# Create tasks
tasks = [
asyncio.create_task(data_collector.run()),
asyncio.create_task(data_processor.run()),
asyncio.create_task(analysis_agent.run()),
asyncio.create_task(report_generator.run()),
asyncio.create_task(qa_agent.run()),
]
# Start the workflow
await task_queue_dc_dp.put({'topic': 'Artificial Intelligence Market'})
# Signal agents to stop after processing
await task_queue_dc_dp.put(None)
await task_queue_dp_an.put(None)
await task_queue_an_rg.put(None)
await task_queue_rg_qa.put(None)
await result_queue.put(None)
# Wait for all agents to complete
await asyncio.gather(*tasks)
# Retrieve final result
final_result = await qa_agent.result_queue.get()
if final_result['approved']:
print("Final Report Approved:")
print(final_result['report'])
else:
print("Report was not approved.")
# Run the main function
asyncio.run(main())
Task Delegation and Escalation
Implementing a TaskManager for delegation and an escalation mechanism.
class TaskManager:
def __init__(self):
self.task_queue = asyncio.Queue()
self.result_queue = asyncio.Queue()
self.agents = []
def add_agent(self, agent):
self.agents.append(agent)
async def run(self):
tasks = [asyncio.create_task(agent.run()) for agent in self.agents]
# Wait for agents to complete
await asyncio.gather(*tasks)
async def delegate_task(self, task):
await self.task_queue.put(task)
async def escalate_issue(self, issue):
print(f"Escalating issue: {issue}")
# Implement escalation logic (e.g., notify a supervisor or human operator)
# Modify agents to accept TaskManager's queues
# ...
# In main function, initialize TaskManager
task_manager = TaskManager()
# Add agents to TaskManager
task_manager.add_agent(data_collector)
task_manager.add_agent(data_processor)
# ...
# Use TaskManager to delegate tasks
await task_manager.delegate_task({'topic': 'Artificial Intelligence Market'})
# Run TaskManager
await task_manager.run()
Conclusion
Designing an agentic workflow for complex LLM applications like market research report generation involves careful planning of agent roles, communication protocols, and task management mechanisms. By specializing agents, establishing robust communication channels, and implementing task delegation and escalation protocols, we can create efficient and reliable systems capable of handling high-stakes tasks. The code demonstrations provided offer a foundational framework that can be expanded and customized to fit specific application needs.
Note: The code examples are simplified for Tutorial purposes Alone. In a production environment, additional considerations such as error handling, logging, security, and scalability would need to be addressed thoroughly.
