Multi-Agent Communication Protocol (MCP) - Architecture and Communication

The Multi-Agent Communication Protocol (MCP) is a framework that enables structured communication and collaboration between multiple AI agents. This document explains its architecture, communication mechanisms, and applications in the financial domain.

🧩 Components of MCP Architecture

1. Orchestrator

• Central control unit that manages workflow.
• Coordinates agent activities and assigns tasks.
• Makes high-level decisions about task routing.
• Synthesizes final outputs for user consumption.

2. Agents

• Specialized AI models with specific expertise domains.
• Process assigned tasks within their specialty area.
• Communicate outcomes to orchestrator and other agents.
• Request information when needed to complete tasks.

3. Shared Knowledge Base

• Centralized repository accessible to all agents.
• Stores shared information and maintains workflow state.
• Preserves history of agent interactions and decisions.
• Enables asynchronous information sharing.

4. External Data Sources

• APIs, databases, and real-time feeds.
• Provide up-to-date information for decision-making.
• May include market data, regulatory info, etc.

🔄 MCP Communication Steps

1. Query Initiation
   User submits a request; orchestrator plans execution.

2. Task Decomposition
   Orchestrator splits the query into manageable subtasks.

3. Task Assignment
   Tasks are assigned to appropriate agents.

4. Information Exchange
   Agents use a shared knowledge base and may communicate with one another.

5. Result Consolidation
   Orchestrator collects all results and prepares the final output for the user.

💼 Financial Industry Applications of MCP

✅ Example: AI-Powered Investment Advisory System

💡 Real-World Use Cases

1. Algorithmic Trading

• Multiple agents monitor signals.
• Risk agent manages exposure.
• Execution agent times trades.
• Compliance agent checks adherence to regulations.

2. Fraud Detection

• Detects suspicious activity in transactions.
• Contextualizes with customer history.
• Scores transactions for fraud risk.
• Prioritizes alerts for review.

3. Credit Underwriting

• Gathers applicant data.
• Assesses credit score and pricing risk.
• Generates documentation automatically.

4. Portfolio Management

• Optimizes asset allocation.
• Rebalances portfolios as needed.
• Applies tax-efficient strategies.
• Produces investment reports.