Part 3 of 5 · Agentic Era Series
Beneath the GenAI hype cycle, a fundamental transformation is unfolding.
While the market remains focused on prompts, tokens, benchmark scores, and the theoretical path to AGI, a more consequential shift is taking place at the architectural level. This evolution in how AI systems connect and collaborate will likely determine which organizations capture lasting value in the coming decade.
The architectural revolution is redefining:
How defensible moats are built when everyone has access to the same models
How SaaS businesses maintain margins when basic AI capabilities become commoditized
How traditional enterprises can extract asymmetric returns from AI investments
Which metrics matter when evaluating both AI-native and AI-enhanced companies
Two weeks ago, I kicked off this Agentic Era series to analyze this strategic inflection point and its implications for building durable businesses:
In this third installment, I examine how AI agent protocols such as MCP and the more recent A2A create the necessary infrastructure for agents to work together and scale effectively. These emerging standards go beyond merely enhancing existing systems to enabling entirely new organizational forms of intelligence. The protocol layer represents the invisible operating system of the autonomous future, the critical connective tissue transforming isolated capabilities into coherent, scalable systems.
For investors and operators, understanding these architectural shifts transcends technical curiosity. It provides the lens through which to identify a durable competitive advantage in an increasingly homogenized AI landscape.
We're witnessing the construction of a new digital nervous system for artificial intelligence.
Imagine the autonomous future as a vast city of intelligent agents. Until now, these agents have been like buildings with no roads between them, isolated islands of capability. Today, we're witnessing the emergence of the infrastructure that will connect them all.
This invisible operating system is built on two emerging protocols:
Anthropic's Model Context Protocol (MCP) — launched in November 2024
Google's Agent-to-Agent (A2A) protocol — launched in April 2025
These protocols represent a discontinuity in the AI landscape, one that is less abrupt than what we observed with DeepSeek, but potentially as powerful. This represents a structural shift from isolated agent capabilities toward standardized communication frameworks that enable collaborative intelligence, as identified in recent research.
Their emergence will define which companies capture value in the next phase of AI development. Just as TCP/IP created the foundation for internet fortunes, these protocols will determine tomorrow's technology winners and losers.
Let's decode what they are, why they matter, and what strategic implications they hold for investors and operators.
MCP (The Model Context Protocol) and A2A: What They Are and How They Work
What is MCP?
MCP is a universal and open context-oriented protocol designed to connect LLM agents to external resources consisting of data, tools, and services more simply and reliably. It follows a client-server architecture with four distinct components:
Host: LLM agents responsible for user interaction, reasoning, and strategic context requests
Client: Provides descriptions of available resources and connects to servers
Server: Connects to resources and provides required context back to clients
Resource: External data, tools, or services provided locally or remotely
Below is Willowtree's insightful analysis of the Model Context Protocol (MCP) architecture. Their diagram illustrates how MCP processes user queries through a structured workflow:
When a user asks a question, the application returns the results of the user's query based on the server with which it interacted.
MCP addresses fragmentation in the LLM ecosystem by introducing a standardized invocation protocol that decouples tool usage from the interfaces of specific base LLM providers and context providers. Its architecture enhances interoperability, scalability, and privacy.
Why does MCP matter?
MCP standardizes external integrations, eliminating one-off API scripts that create technical debt. It enables secure, context-rich communication with tools while dramatically reducing development effort for AI agents.
Think of it as the operating layer your AI stack always needed but lacked. It allows agentic applications to function with the data and context they need to be truly useful.
The market has responded with enthusiasm. Though specific adoption metrics are not publicly verified, the protocol has gained significant attention since its November 2024 launch. Early implementations suggest substantial improvements in agent development efficiency by standardizing the connection between models and external resources.
Google's Agent-to-Agent (A2A) Protocol
What is A2A?
Google's A2A protocol is designed to enable seamless agent collaboration regardless of underlying frameworks and vendor implementations. Unlike MCP, which focuses on context acquisition, A2A specifically enables complex inter-agent collaboration with enterprise-grade features. Its key principles include:
Simplicity: Reusing existing standards like HTTP(S), JSON-RPC 2.0, and Server-Sent Events
Enterprise Readiness: Built-in considerations for authentication, authorization, and security
Async-First Architecture: Support for long-running asynchronous workflows
Modality Agnostic: Native support for text, files, forms, and media formats
Opaque Execution: Preserving implementation privacy while sharing task-related metadata
A2A facilitates communication between client agents (which formulate tasks) and remote agents (which execute those tasks), using structures like Agent Cards, Tasks, Artifacts, Messages, and Parts to organize collaborative workflows.
In both cases, development, adoption, and deployment are in the earliest of early stages.
Why Do MCP and A2A Matter for the Agentic Era?
By enabling seamless communication and context staging, these protocols form the backbone of future autonomous systems. They are the new OS of the Agentic Era, much the way TCP/IP served as the foundational communication protocol of the internet.
To appreciate the significance, recall how TCP/IP (Transmission Control Protocol/Internet Protocol) established the common language for computers to communicate across networks, forming the foundation of the internet. Without this standardized protocol, the internet as we know it would not exist. Similarly, MCP and A2A are creating communication standards that will allow AI systems to interact effectively at scale.
AI agents are only as good as their protocols. MCP and A2A together enable structured context exchange and autonomous coordination at scale. They make possible persistent memory across interactions, tool interoperability that expands agent capabilities, and sophisticated multi-agent workflows. These are the essential ingredients for truly autonomous systems.
What Is the Business Model Behind MCP and A2A?
A critical aspect to understand is the business model behind these protocols. Both MCP and A2A are open-source specifications, not directly monetized themselves. This follows a familiar pattern in technology: create an open standard that enables an ecosystem, then capture value through complementary proprietary offerings. For companies like Anthropic and Google, the value comes not from the protocols but from the foundation models and services built around them. By controlling the protocols, they ensure their LLMs remain central to the emerging ecosystem.
The capability stack for autonomous agents consists of three distinct layers:
Foundation models provide the reasoning engine (where Anthropic, Google, and others directly monetize their offerings)
Protocol layers (MCP and A2A) enable context sharing and agent collaboration (open-source, not directly monetized)
Orchestration systems are essentially applications that direct agent activities toward specific business outcomes (where startups and enterprises can build proprietary value)
The protocol layer has been the critical missing piece. Its emergence now unlocks the full potential of foundation models for autonomous work.