When AI learns to "team up to fight monsters": How MCP and A2A reconstruct business automation

Understanding MCP and A2A: How Two Protocols Catalyze an Exciting Agent Era

Multi-agent systems demonstrate enormous potential in solving complex tasks by invoking specialized AI models with collaborative, coordinative, and communication capabilities. However, historically lacking standardized protocols have hindered system interoperability, making it difficult for agents to collaboratively solve real-world problems. Recently, two major protocols—Anthropic's Model Context Protocol (MCP) and Google's Agent-to-Agent Protocol (A2A)—have emerged. They address challenges from different perspectives but share a common vision: enhancing agent utility through improved connectivity capabilities.

This article comprehensively explores MCP and A2A, clarifying their significance and analyzing potential impacts. We will demonstrate how these protocols expand capabilities on existing AI architectures by introducing multi-agent interoperability, structured communication, and context exchange standards, thereby driving the next wave of advanced real-world scenario AI applications.

1. Evolution of AI Agents and Multi-Agent Systems
2. In recent years, artificial intelligence (AI) has made significant progress driven by the rise of large language models (LLM). Many organizations have begun experimenting with agent-based AI—systems where specialized agents with different capabilities collaborate or compete to solve problems. These tasks may encompass diverse scenarios such as supply chain prediction, customer service automation, or business intelligence-oriented data analysis.

Previously, building multi-agent solutions typically required custom, ad-hoc integration between AI components, data sources, and external tools. This approach presented substantial development barriers:

Complex Communication: Each pair of interacting AI subsystems required a dedicated integration solution

Inconsistent Context Provisioning: Agents often lacked reliable, unified context data or result sharing mechanisms

High Maintenance Costs: Modifications to a single subsystem could potentially disrupt the entire agent ecosystem

In other words, imagine managing a multinational enterprise with the following configuration:

• Customer service robot

• Pricing engine

• Python script for processing logistics data

• Knowledge base search tool

Without a universal standard, you would face:

N×M integration problem—each new tool (M) requires manually writing adapters for all agents (N)

Hidden context—lack of shared semantic for "tool functionality" or "agent knowledge"

Security risks—scripts having full file system access while auditors can only view opaque logs

Slow iterations—replacing GPT-4 with Claude Sonnet means rewriting half the underlying code

Result? 60-80% of engineering time is spent on underlying integration rather than innovation.

Anthropic's Model Context Protocol (MCP) and Google's Agent-to-Agent Protocol (A2A), two new open standards, aim to solve these issues by defining universal communication and context sharing protocols. These protocols enable AI agents to:

• Discover each other's capabilities and domain expertise

• Negotiate task division and delegation methods

• Exchange intermediate results, structured prompts, or relevant data

• Collaborate safely and efficiently to achieve complex multi-step goals

For enterprises, these advances open new opportunities—building more robust AI-driven workflows that integrate capabilities from different vendors and technologies without exorbitant costs.

Significance for AI Practitioners

1. Seamless Multi-Agent Collaboration: Developers can combine multiple specialized agent services instead of building a single massive system

2. Long-Duration Task Support: Agents can track tasks lasting hours or even days, providing status updates as progress continues (such as R&D, long-term data analysis)

3. Reduced Complexity: A universal "agent handshake" mechanism means less coding overhead when connecting different AI services

Impact on Enterprises

• Productivity Enhancement: Multiple AI solutions can "communicate" with each other, automating cross-departmental workflows (such as HR agent collaborating with compensation agent)

• Cross-Platform Ecosystem: By adopting A2A standards, enterprises can invest in specialized AI providers while ensuring interoperability of all solutions

• Future Adaptability: When new AI capabilities emerge, enterprises can more seamlessly integrate, with "plug-and-play" compatibility with existing agents

Typical Application Scenarios

• Supporting robots discovering complex problems, calling diagnostic agents, then having automation robots complete repairs

• Travel planning agents coordinating flights, hotels, and translation agents in the background

• HR Scenarios: Agent chain for resume screening → interview scheduling → candidate Q&A

• IT Operations: New employee onboarding process across HR, procurement, and permission management agents

4. MCP vs A2A Comparison

While Anthropic's MCP and Google's A2A both promote multi-agent or multi-service interoperability, they target different layers of the AI technology stack:

Theoretically, protocols can operate independently, but they are often complementarily used. LLM agents can be integrated with new data/tools via MCP, then collaborate through A2A with a second agent specializing in data analysis, knowledge retrieval, or enterprise resource planning. By combining these two protocols, enterprises can build robust multi-layered AI ecosystems with minimal customization.