Introduction
In the rapidly evolving world of AI and multi-agent systems, I recently embarked on an exciting learning journey to explore CrewAI and Model Context Protocol (MCP). Today, I’m thrilled to share my experience building a Portfolio POC – a practical project that demonstrates how to create collaborative AI agents using these powerful frameworks.
While the end result is a portfolio planning system, the real goal was to learn how to design, implement, and orchestrate multiple AI agents that can work together on complex tasks. This project serves as a hands-on tutorial for understanding CrewAI’s multi-agent framework, MCP integration, and agent collaboration patterns.
The Learning Goal: Understanding Multi-Agent AI Systems
My primary objective was to learn how to build effective multi-agent AI systems using modern frameworks. I chose portfolio planning as a practical domain because it involves multiple distinct tasks that can be distributed across specialized agents:
- Data collection and analysis
- Expert opinion gathering
- Data validation and quality control
- Decision-making and recommendations
This domain provided the perfect opportunity to explore CrewAI’s capabilities and understand how to design agents that can collaborate effectively on complex workflows.
The Learning Approach: Multi-Agent AI System
To achieve my learning goals, I designed a system with five specialized AI agents, each demonstrating different aspects of agent design and collaboration:
🤖 Meet the AI Team
- PlannerAgent – The orchestrator who coordinates the entire process
- StockPerformanceAgent – Analyzes historical stock performance and trends
- AnalystOpinionAgent – Gathers expert analyst ratings and consensus
- ValidatorAgent – Ensures data quality and consistency
- PortfolioAdvisorAgent – Generates final investment recommendations
Technical Learning: Architecture Deep Dive
CrewAI Framework
Through this project, I learned how CrewAI provides a powerful foundation for building multi-agent systems. The framework enables agents to:
- Work sequentially or in parallel
- Share information and collaborate
- Use specialized tools and APIs
- Maintain context throughout the process
Model Context Protocol (MCP) Integration
One of the most valuable learning experiences was integrating MCP (Model Context Protocol). I created a custom MCP server to understand how to provide specialized tools to AI agents:
@mcp.tool()
def get_market_data(ticker: str) -> dict:
"""Return stock price and 1Y return for a given ticker."""
# Implementation details...
@mcp.tool()
def get_analyst_insights(ticker: str) -> dict:
"""Return analyst insights for a given stock ticker."""
# Implementation details...Agent Configuration with YAML
The agents are configured using YAML files, making the system highly maintainable and configurable:
PlannerAgent:
role: AI portfolio planner
goal: Decide which agents need to run based on user investment goals and budget
backstory: |
You're the orchestrator of the portfolio planning process...Learning Outcomes: How the System Works
Step 1: Initialization
I learned how to design an orchestrator agent (PlannerAgent) that can select and coordinate tasks. In this case, it selects a curated set of commonly recommended stocks (AAPL, MSFT, JNJ, PFE) as the starting point.
Step 2: Data Collection
- StockPerformanceAgent retrieves 1-year returns and current prices
- AnalystOpinionAgent gathers buy/hold/sell ratings and market consensus
Step 3: Validation
The ValidatorAgent ensures all collected data is complete, consistent, and useful for decision-making.
Step 4: Portfolio Generation
The PortfolioAdvisorAgent allocates a $5000 budget across validated stocks, prioritizing those with positive analyst ratings.
Step 5: Output
The system returns a detailed portfolio with:
- Share allocations for each stock
- Total investment cost
- Leftover cash
- Investment rationale
Key Learning Challenges and Solutions
Challenge 1: Agent Coordination
Learning Problem: Understanding how to ensure agents work together effectively without conflicts.
Solution Learned: Used CrewAI’s sequential process with clear task dependencies and structured outputs.
Challenge 2: Data Consistency
Learning Problem: Understanding how to maintain data quality across multiple agents and sources.
Solution Learned: Implemented a dedicated ValidatorAgent and structured data formats.
Challenge 3: Tool Integration
Learning Problem: Learning how to provide agents with access to external data and tools.
Solution Learned: Created a custom MCP server with specialized portfolio analysis tools.
Learning Results and Achievements
Through this project, I successfully learned how to:
- ✅ Design and implement multiple AI agents with specialized roles
- ✅ Integrate MCP servers to provide custom tools to agents
- ✅ Orchestrate agent collaboration using CrewAI’s framework
- ✅ Handle data validation and quality control across agents
- ✅ Structure complex workflows with clear task dependencies
Lessons Learned
1. Multi-Agent Design Patterns
Designing effective multi-agent systems requires careful consideration of:
- Agent specialization and role clarity
- Information flow between agents
- Error handling and validation
- Scalability and maintainability
2. CrewAI Best Practices
- Use YAML configuration for maintainability
- Implement proper error handling
- Structure agent outputs consistently
- Leverage CrewAI’s built-in features like task dependencies
3. MCP Integration
- MCP provides a clean interface for tool integration
- Custom servers can be powerful for domain-specific functionality
- Proper tool documentation is crucial for agent effectiveness
Future Enhancements
The project has significant potential for expansion:
🚀 Planned Features
- Real-time Market Data: Integration with live market APIs
- Risk Assessment: Advanced risk modeling algorithms
- Portfolio Rebalancing: Automated rebalancing recommendations
- Historical Tracking: Performance monitoring over time
- User Preference Learning: Personalized recommendations based on user behavior
🔧 Technical Improvements
- Multi-Asset Support: Bonds, ETFs, and other investment vehicles
- Advanced Analytics: Machine learning models for pattern recognition
- API Integration: Real-time data from financial services
- Web Interface: User-friendly dashboard for portfolio management
Conclusion
Building this multi-agent AI system has been an incredible learning experience. The combination of CrewAI’s framework with custom MCP tools has taught me how to design, implement, and orchestrate collaborative AI agents effectively.
This project demonstrated key concepts in multi-agent AI:
- Agent specialization and role design
- Inter-agent communication and coordination
- Tool integration and external service connectivity
- Workflow orchestration and task management
While the portfolio planning domain provided a practical application, the real value was in learning how to build scalable, maintainable multi-agent systems. These skills are transferable to any domain that requires complex, collaborative AI workflows.
Resources
- CrewAI Documentation
- Model Context Protocol
- Project Repository – Check out the full implementation and documentation
What are your thoughts on multi-agent AI systems? Have you worked with CrewAI or similar frameworks? I’d love to hear about your experiences in the comments below!
**Tags**: #AI #CrewAI #MultiAgentSystems #MCP #Learning #Tutorial #Python #AgentDesign