Automating Market Research with CrewAI: A Practical Case Study

In the era of Agentic AI, the fundamental unit of work is shifting from a single prompt to a coordinated “crew” of specialized agents. This case study explores how we automated a weekly market research process that previously took 12 hours of manual work, reducing it to a 5-minute autonomous execution.

The Problem: Information Overload

Our product team needed weekly updates on the “Agentic AI” landscape.

• Manual Task: Searching news, reading GitHub repos, summarizing Twitter threads, and writing a formatted PDF report.
• The Bottleneck: Human researchers were becoming a bottleneck, often delivering reports two days late.

The Solution: A Three-Agent Research Crew

We used CrewAI to orchestrate three distinct agents, each with its own role, goal, and tools.

1. The Researcher

• Role: Senior Market Analyst
• Goal: Find the latest breakthroughs in Agentic AI frameworks from the past 7 days.
• Tools: DuckDuckGo Search, ScrapeWebsiteTool.

2. The Technical Writer

• Role: Technical Content Strategist
• Goal: Compile the researcher’s findings into a professional, markdown-formatted report.

3. The Quality Manager

• Role: Chief Editor
• Goal: Ensure the report is factually accurate, follows the brand voice, and is free of hallucinations.

Implementation Code (Python)

from crewai import Agent, Task, Crew, Process
from langchain.tools import DuckDuckGoSearchRun

search_tool = DuckDuckGoSearchRun()

# Define Agents
researcher = Agent(
  role='Market Researcher',
  goal='Identify emerging trends in {topic}',
  backstory='Expert in tech trends with 10 years experience.',
  tools=[search_tool],
  verbose=True
)

writer = Agent(
  role='Technical Writer',
  goal='Write a compelling report on {topic}',
  backstory='Specialized in making complex tech easy to understand.',
  verbose=True
)

# Define Tasks
task1 = Task(description='Search for news on {topic}', agent=researcher)
task2 = Task(description='Write the report based on search results', agent=writer)

# Coordinate the Crew
crew = Crew(
  agents=[researcher, writer],
  tasks=[task1, task2],
  process=Process.sequential
)

result = crew.kickoff(inputs={'topic': 'Agentic AI Frameworks 2024'})
print(result)

Results & Impact

Conclusion

CrewAI allowed us to move beyond simple chatbots and build a robust, repeatable pipeline. By treating AI agents as team members with specific roles, we unlocked a level of productivity that was previously impossible.