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A2A and MCP: Seamless Communication for AI Agents
Read How Open Protocols are Breaking Barriers for Multi-Agents
AI Agents are built on top of LLM to perform tasks autonomously. They solve problems, make decisions through reasoning, and take action using tools with little to no human intervention. They also learn and adapt based on feedback.
The most common scenario that exists in organizations today is where AI Agents are developed by multiple teams using different agentic frameworks, as some teams need control over state management. Hence, they prefer LangGraph, another team-built AI agent using CrewAI, as it comes with simple abstractions so that you can focus on designing the task rather than trying to develop complex code or use cloud platforms like Google or AWS, as these cloud platforms host organizational data and services. Using the strength of the agentic platform is good, but this siloed approach to AI Agent raises questions on
How to enable communication and collabration between different AI Agents?
Can the AI Agents developed on different framework share the tools and resources like databases, API reliably, safely and securely?
Model Context Protocol(MCP) and Agent-to-Agent (A2A) are the open communication protocols that complement each other to address…
