Must-see! The most popular MCP and A2A in the entire network, explained in plain language, exclusively teach you how to enter the game accurately | A guide to avoid pitfalls

1. Explain mcp and A2A in plain language

• MCP (Agent Internal Communication Protocol):

It is like a "Feishu" inside an agent (intelligent body).

Responsible for connecting various internal tools such as maps and browsers to enable them to collaborate efficiently.

• A2A (Agent-to-Agent Communication Protocol):

When different agents need to collaborate across individuals (for example, your secretary agent needs to connect to the shopping agent)

A communication protocol similar to “Enterprise WeChat” is needed, which is A2A - it solves the “cross-organizational” communication problem between agents.

Additional professional perspective:

A single agent needs to integrate multiple services such as maps, search, and calculators. These services use the MCP protocol to achieve internal data flow and functional collaboration.

As the Agent ecosystem expands (e.g. users have secretary Agents, financial agents, shopping agents, etc. with different divisions of labor)

The need for cross-individual collaboration between them gave rise to the A2A protocol, which is similar to the system connection between enterprises through standardized interfaces.

2. The core bottleneck of Agent not reaching the outbreak node:

In a word: Token cost ceiling.

The core obstacle facing the current implementation of Agents is the economic feasibility of a single task:

• Cost pain point: Taking GPT-4 as an example, the cost of a single call for a complex task may be as high as several dollars (or more than US$10 in extreme scenarios).

This means that only a few high-value scenarios (such as enterprise-level financial analysis and medical diagnosis) can cover the costs, and C-end users find it difficult to accept paying high prices for daily conversations or simple services.

• Breaking conditions:

Either the model efficiency is improved to reduce token consumption by 50%-90%,

Either the unit price of tokens will drop significantly with technological iteration or market competition (the current trend is continuous price reduction, but it has not yet reached the acceptable threshold for C-end users).

Before this, large-scale applications on the C-end were difficult to implement.

3. Entry threshold for different players:

• Small teams/individual developers: high initial costs lead to limited room for trial and error, making it difficult to support continuous iteration;

• Financing companies: They can acquire customers at low prices through capital subsidies, which may trigger a “price war” in the short term. The essence of this is a competition for cash flow rather than technology.

The technical threshold changes dynamically: The improvement of the ecosystem (such as open source frameworks and low-code tools) is gradually lowering the development threshold, but at this stage it is still higher than ordinary AI applications and is more suitable for teams with resource reserves.

Why not invest directly in Agent development?

Since 2024, the Agent track has changed from "implicit consensus" to "open card". Early competition focuses on cost control and scale expansion, while small and medium-sized teams lack capital moats and find it difficult to survive in the money-burning war.

Strategic choice: Looking at Agent ecosystem opportunities from the perspective of AGI.

Core logic shift: Break away from single agent development and find entry points from the perspective of AGI ecosystem construction.

4. What opportunity can we wait for?

Three major directions of surrounding opportunities:

1. Lightweight tool layer:

◦ Develop “Agent Template Library” (provide industry-wide common workflow templates);

◦ Build an “Agent Navigation Platform” (aggregate and classify Agent applications, similar to the early App Store);

◦ Organize a “selected case library” (lowering the threshold for use by enterprises/individuals).

2) Vertical scene connection:

Targeting niche areas (such as legal document processing, cross-border e-commerce operations);

The existing Agent capabilities are connected in series through the A2A protocol to form a "shell solution" that focuses on process automation rather than underlying development.

3) Long-term layout: vertical agent ecology

After the ecosystem matures (cost controllable, protocol standardized), we will design “fixed process agents” for specific industries (such as educational tutoring and supply chain management).

Meet deep needs through modular combination and avoid reinventing the wheel.

I will wait until the ecosystem is complete, and then various agents will be established. I will wrap a layer and string together a fixed process to meet a vertical scenario, and then make a "vertical agent".