I built a multi-agent financial analysis system using CrewAI to explore how specialized AI agents can collaborate like a small trading and investment team. Instead of asking one model to do everything, I split the workflow into roles with clearer responsibilities.

The system follows a hierarchical CrewAI workflow where a manager LLM coordinates the agents, delegates tasks, and combines their outputs into one structured analysis process.

The agent crew:

• Data Analyst Agent for market data, trends, opportunities, and threats
• Trading Strategy Developer for strategy generation based on risk tolerance and preferences
• Trade Advisor for execution timing, pricing, and market conditions
• Risk Advisor for risk review and mitigation recommendations

For the demo, I used AAPL with medium risk tolerance and a day-trading preference. The result was a complete flow: market context, strategy proposal, execution planning, and a risk-focused review.

The biggest lesson was that agentic AI is not only about automation. The interesting part is orchestration: making multiple focused systems work together toward a decision process. This project is a technical demonstration, not financial advice.

More info on LinkedIn: CrewAI financial analysis post