The Rise of the Agentic Web: From GenAI Applications to Inter-Agent Protocols

The advent of Generative AI (GenAI) marked a significant shift in application development. Initially, we focused on building applications powered by large language models, enabling tasks like content generation, summarization, and code assistance. These applications were primarily monolithic, relying on single models to perform complex tasks.

As the field matured, we recognized the limitations of this approach. Complex tasks often require a combination of skills and knowledge domains, which a single model might not efficiently handle. This realization led to the emergence of AI agents—autonomous entities capable of performing specific tasks and collaborating with other agents to achieve broader objectives. The orchestration of these agents gave rise to agentic workflows, where multiple agents interact, each contributing its expertise to complete complex tasks.

To streamline the integration of agents with data sources and tools, Model Context Protocol (MCP) was introduced. MCP standardized how agents access and utilize external data and tools, ensuring consistency and interoperability across different systems. This standardization was crucial in enabling agents to function effectively within diverse environments.

The Need for Agent-to-Agent Communication

While MCP addressed the integration of agents with data sources, it did not standardize how agents communicate with each other. As agentic workflows became more prevalent, the need for a standardized protocol for inter-agent communication became apparent. Enter Agent-to-Agent (A2A) protocol.

A2A provides a standardized method for agents to discover, communicate, and collaborate with each other. It defines a common language and set of rules that agents can use to exchange information and delegate tasks. This standardization is essential for building scalable and interoperable multi-agent systems.

Why Not Use Existing Standards?

One might wonder why a new protocol like A2A is necessary when existing standards like HTTP, REST, and service discovery tools like Consul are available. While these technologies are robust and widely adopted, they are primarily designed for human-to-service interactions and lack the semantics required for autonomous agent communication.

A2A, while utilizing HTTP for transport, introduces a layer of semantics tailored for agent interactions. It defines how agents describe their capabilities, discover other agents, and invoke actions. This semantic layer is crucial for enabling agents to understand and interact with each other autonomously, without human intervention.

Could A2A Eventually Obsolete MCP?

It’s worth entertaining a question that surfaces naturally once you start working with both protocols: if A2A lets agents discover and call one another, and if many tools can be modeled as agents, could we skip MCP entirely?

In theory, yes. You could imagine an architecture where tools are exposed not via MCP but as standalone A2A agents. Instead of a model calling a tool directly using MCP, it could delegate the task to another agent over A2A — that agent just happens to wrap the tool. The consumer doesn’t know (or care) whether the task is handled locally by a plugin or remotely by another autonomous agent.

This delegation pattern becomes even more interesting in agentic environments with constrained models. A lightweight “coordinator” agent could offload execution to remote skill agents, each exposing a set of tool-like capabilities over A2A. The lines between tools and agents start to blur.

But in practice, MCP and A2A are solving very different problems at different layers. MCP is optimized for embedding tools within the model’s context — with structured inputs and outputs, minimal overhead, and zero network hops. It’s about giving the model access to internal functions and plugins. A2A, by contrast, is about external communication, coordination, and discovery across independently hosted agents.

So no, A2A doesn’t obsolete MCP. But it does let you abstract over it. And in a future where agents are orchestrating other agents (some of which just wrap tools), the two protocols might not compete — they might converge.

Industry Adoption and the Path Forward The significance of A2A and MCP is underscored by recent industry developments. According to a VentureBeat article, Microsoft CEO Satya Nadella has publicly endorsed both A2A and MCP, highlighting their importance in building an open and interoperable agentic web. Nadella emphasized that “open protocols like A2A and MCP are key to enabling the agentic web,” signaling a shift towards standardized, cross-platform AI agent collaboration.

This endorsement indicates a broader industry movement towards embracing open standards for AI interoperability. By adopting protocols like A2A and MCP, organizations can build flexible, scalable, and interoperable AI systems, avoiding vendor lock-in and fostering innovation.

Conclusion

The evolution from GenAI applications to agentic workflows, and now to standardized inter-agent communication, marks the beginning of the Agentic Web. Protocols like A2A and MCP are foundational in this transformation, enabling autonomous agents to collaborate seamlessly across diverse systems. As the industry continues to embrace these standards, we can anticipate a future where AI agents work together, dynamically and efficiently, to solve complex problems across various domains.