Proposing a New Design Pattern for Inter-Agent Collaboration: Financial Risk and Compliance Manager with A2A Protocol and MCP Integration 🌟
As Google's Agent-to-Agent (A2A) protocol remains in its early stages, I’m pleased to present a project that investigates a cutting-edge approach to inter-agent collaboration in AI systems. Introducing the Financial Risk and Compliance Manager—a FinTech solution that leverages a simulated A2A Protocol for dynamic, multimodal communication between agents, integrated with the Model Context Protocol (MCP) for streamlined access to tools and resources. 🚀
🔍 Why Focus on A2A and MCP?
The A2A Protocol enables agents (like Profile Checker, Risk Reporter, and Compliance Querier) to collaborate seamlessly without sharing memory, resources, or tools—ensuring modularity and independence. Meanwhile, the MCP Server acts as a universal, open context-oriented protocol, connecting these agents to Generative AI tools (e.g., Retrieval-Augmented Generation with LangChain/FAISS, LLMs), APIs, and resources with structured inputs/outputs. Together, they create a powerful framework for building scalable, flexible LLM applications.
🛠️ What This Project Demonstrates:
- Inter-Agent Collaboration: Agents communicate dynamically using a simulated A2A Protocol style, delegating tasks (e.g., risk reporting, compliance querying) without shared dependencies.
- MCP Integration: The MCP Server provides universal accessibility to tools and resources, acting as an "external brain" for agents to retrieve regulatory context and generate insights.
- New Design Pattern: By combining A2A-style communication with MCP’s standardized resource access, this project proposes a streamlined design pattern that enhances modularity, scalability, flexibility, and maintainability for LLM-driven applications.
- Simple Prototype: As a basic prototype, the application uses very simple RAG services to showcase how inter-agent communication integrates with the MCP architecture, focusing on demonstrating the potential of this design.
💡 Why It Matters:
This approach addresses the interoperability challenges of diverse agent systems, offering a blueprint for future AI applications in financial compliance and beyond. It’s a step toward more efficient, collaborative, and scalable systems in FinTech.
🎥 See it in action with the Video Demo! For a deeper dive, check out the detailed technical report and explore the codebase on GitHub.
What are your thoughts on the future of inter-agent collaboration in AI? Let’s discuss in the comments! 👇