Agentic Era Part 3: How MCP and A2A Form the Invisible Operating System of the Autonomous AI Future
These two transformative protocols are rewiring how intelligent agents interact. That's redefining AI's value, reshaping competitive moats, and creating a new architecture for intelligent automation.
Welcome to Part 3 of the Agentic Era mini-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 and A2A: What They Are and How They Work
The Model Context Protocol (MCP)
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.
MCP and A2A: Why They Matter
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.
The Protocol Economics
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.
The Cohabitation Question: MCP, A2A, and OpenAI
An interesting dynamic is already emerging among these protocols. While OpenAI initially backed Anthropic's MCP, both companies are conspicuously absent from Google's growing roster of partners supporting A2A. This raises fundamental questions about how these technologies will coexist.
In theory, these protocols serve different functions.MCP primarily handles context exchange with tools, while A2A focuses on inter-agent communication. However, MCP already contained mechanisms for agent-to-agent coordination, suggesting potential overlap.
Three cohabitation scenarios appear possible:
Complementary adoption: Enterprises implement both protocols for different use cases
Protocol consolidation: Market forces drive convergence toward a unified standard
Ecosystem fragmentation: AI vendors maintain incompatible protocols to preserve competitive moats
OpenAI's approach reveals perhaps a different strategy, with a focus on ecosystem control, not protocols. The recent $3B acquisition of code editor Windsurf suggests it is prioritizing owning the application layer (the tools developers use) over developing or adopting a specific protocol for AI integration. If it were to build its own protocol, OpenAI’s history of prioritizing proprietary systems suggests it’s more likely to double down on a walled-garden approach than to champion open protocols.
This massive bet, coupled with strategic executive appointments, signals OpenAI's conviction that enduring value lies in specialized applications rather than infrastructure protocols. By building domain-specific solutions that connect via standard protocols, OpenAI positions itself to capture value regardless of which protocol ultimately dominates -- a strategy that prioritizes vertical-specific moats over horizontal protocol plays.
Value Implications Across the GenAI Technology Stack
The emergence of these protocols reshapes value creation across every layer of the GenAI stack.
At the infrastructure level, MCP and A2A have significant implications for compute requirements. By enabling efficient context sharing and reducing redundant reasoning, these protocols could mitigate the anticipated inference bottleneck. Initial implementations suggest significant efficiency improvements for complex multi-step tasks when using standardized protocols compared to traditional approaches, though specific benchmarks are still emerging.
For infrastructure providers, this presents a double-edged sword: reduced consumption per task, but dramatically increased agent deployment due to lower development barriers. The net effect favors providers who can scale efficiently.
In the data infrastructure and MLOps domains, MCP and A2A will transform how enterprises handle context management. Traditional vector databases and retrieval-augmented generation (RAG) architectures are being complemented or partially replaced by context-aware systems built around these protocols.
Companies like Langchain, Pinecone, and LlamaIndex are at a strategic inflection point, where adapting their architectures to leverage these protocols could present both opportunities and challenges.
Data platform providers like Databricks and Snowflake also face adaptation requirements. Their current architectures, optimized primarily for human-centric analytics workflows, will need to evolve to better support agent-driven interactions, including agent-to-agent communication and more distributed authentication models.
At the application level, two dynamics are already emerging: accelerated deployment of applications with higher customer value (improving revenue quality metrics) and reduced development costs as complexity decreases.
Early adopters of MCPs report significantly reduced time-to-market for agent-based applications, with some estimates suggesting up to 40% faster development compared to custom integration approaches, though exact figures vary by platform and use case. Simple agent-based apps (e.g., customer service bots) may see larger gains from MCPs than complex systems requiring deep customization.
However, both protocols create new challenges that represent opportunities for startups to address. For enterprises, direct data access introduces security vulnerabilities that necessitate governance frameworks.
Beyond Tech: The “Captain of Agents” as the New Moat for Material World Companies
Perhaps the most profound implication of these protocols extends beyond the technology sector. Agent protocols like the MCP enable the emergence of a new competitive advantage for traditional enterprises in the “material world”, which I refer to as the “Captain of Agents” role.
The “captain of agents” is the central orchestrating intelligence that coordinates autonomous agents across physical operations, supply chains, customer interactions, and business processes. The role aligns with what researchers describe as “layered protocol architectures”.
Before MCP, this captain role was practically impossible for material world companies. Context remained siloed across different systems. Workflows were hard-coded and inflexible. Semantic intent could not propagate from digital systems to physical operations. Organizations functioned as systems of coordination rather than systems of agentic collaboration.
MCP fundamentally changes this equation. It introduces a universal mechanism to capture and transmit shared context, maintain a memory of goals and local agent states, and allow agents to interoperate with autonomy. A2A extends this capability by enabling direct agent-to-agent negotiation and task delegation. Together, they make possible what was previously unattainable: a cohesive system where digital intent can seamlessly translate into physical action.
The strategic implication is profound: being the "captain of agents" in the material world is not about performing every task but about owning the protocol layer that governs intent translation, controlling mission logic, and extracting value from orchestration across both digital and physical realms. As I outlined in Part 2 of this series, architectural control points create disproportionate value in AI systems. The captain of agents represents the ultimate control point where digital intelligence meets physical execution.
Enterprises that establish themselves as captains of agent ecosystems will develop new moats based on orchestration capability rather than traditional advantages like scale, brand, or proprietary technology. This represents a fundamental shift in how competitive advantage is constructed in traditional industries, potentially redistributing value from pure technology players to material world companies that effectively harness these protocols.
Conclusion: A Protocol-Driven Future
As we have explored so far in this mini-series on the Agentic Era, architectural choices matter profoundly. MCP and A2A represent architectural decisions with far-reaching implications for the entire technology ecosystem.
Beyond MCP and A2A, research has identified several other emerging protocols that may play important roles in the agent ecosystem, including the Agent Network Protocol (ANP), which focuses on cross-domain agent communications; the Agent Interaction & Transaction Protocol (AITP), which enables secure transactions between agents; and Agora, which facilitates natural language protocol generation. Together, these innovations are creating what researchers describe as the foundational infrastructure for a new era of distributed, collaborative intelligence that could reshape how intelligence is shared, coordinated, and amplified across systems.
For investors, these protocols create new categories of opportunity, from protocol security and governance to context management platforms. For operators, they necessitate strategic choices about which protocol ecosystems to join and how to position within the emerging value chain.
The invisible OS for the autonomous future is being written now, one protocol at a time. Those who understand its architecture will be best positioned to capture the value it creates.
This article is the third installment in our mini-series on the Agentic Era. For previous articles in this series, visit Decoding Discontinuity.