Why Your AI Agent Stack Needs MCP, A2A, and Pilot Together

AI development has reached a critical inflection point where standalone tools are no longer enough. Today’s most advanced systems rely on interconnected agents, each specializing in a specific function. Yet the protocols enabling this collaboration—MCP, A2A, and Pilot—are frequently misunderstood as rival technologies. The reality is far simpler: they operate at different layers of the same stack, much like the foundational TCP/IP protocols that power the internet.

MCP: Bridging Agents to Tools

The Model Context Protocol (MCP) serves as the intermediary between an AI agent and the tools it needs to execute tasks. Developed by Anthropic, MCP standardizes how agents interact with external resources such as databases, APIs, or document parsers. Think of it as a universal plugin system where agents don’t need to understand the intricacies of each tool.

• An MCP client embedded in the agent initiates requests.
• MCP servers expose tools through a consistent JSON-RPC interface.
• The agent receives structured responses without needing to handle low-level details.

This protocol is vertical in scope, focusing solely on the agent-tool relationship. It doesn’t address communication between agents or network-level concerns—it’s purely about enabling seamless tool integration.

A2A: Enabling Agent-to-Agent Delegation

Google’s Agent-to-Agent (A2A) protocol addresses a different challenge: how one agent can delegate complex tasks to another. Introduced in mid-2025 and subsequently donated to the Linux Foundation, A2A has rapidly gained traction with over 150 supporting organizations. Its purpose is to define the structured workflow between agents when one lacks the specialized capabilities required for a task.

The protocol establishes a formal contract between agents:

• Agent A advertises its capabilities and tasks Agent B with a specific job.
• Agent B accepts the assignment, processes the request, and returns structured results.
• Agent A tracks progress and integrates the output into its workflow.

Unlike MCP, A2A operates at the application layer and assumes agents can already communicate. It doesn’t specify how they discover each other or establish connections across networks—it’s purely about task delegation and status tracking.

Pilot Protocol: The Glue for Agent Networks

Where MCP and A2A handle semantic and functional layers, Pilot Protocol operates at the network infrastructure level. Developed by the Pilot Protocol Network, it ensures agents can find and securely communicate with each other, regardless of their physical location or network restrictions.

Each agent runs a local daemon that:

• Assigns a stable virtual address derived from an Ed25519 cryptographic keypair.
• Handles NAT traversal to establish direct connections between agents behind firewalls.
• Encrypts all traffic using X25519 for key exchange and AES-256-GCM for data security.

Pilot’s role is analogous to an overlay network for agents. Just as the public internet routes packets via IP addresses, Pilot routes messages using virtual addresses that remain consistent even if the underlying hardware changes. This eliminates the need for agents to manage complex network configurations.

A Real-World Stack in Action

Consider a research pipeline with three specialized agents: a coordinator, a web researcher, and a document analyst. Each protocol plays a distinct role in making this system functional.

1. Coordinator Agent: Uses MCP to connect to a search tool and file system, retrieving raw data without handling its complexities.

1. Task Delegation: When the coordinator needs to analyze a document, it uses A2A to send the document to the document analyst, specifying the required output format.

1. Network Connectivity: The A2A message travels over an encrypted Pilot tunnel. If the document analyst is running in a cloud environment behind NAT, Pilot’s daemon ensures the connection succeeds transparently.

In this setup, MCP handles tool integration, A2A manages inter-agent task delegation, and Pilot guarantees reliable, secure communication. None of these protocols overlap in functionality—they’re complementary layers that together enable a cohesive multi-agent system.

Common Misconceptions and Why They Persist

The confusion often stems from superficial similarities in how these protocols handle messages. All three involve request-response patterns or message exchanges, leading some to assume they’re interchangeable. The key distinction lies in their purpose and scope:

• MCP is asymmetric, with agents acting as clients and tools as servers.
• A2A is peer-to-peer but focused on task delegation, not network transport.
• Pilot is agnostic to message content, prioritizing delivery, encryption, and identity verification.

Another source of misunderstanding is the assumption that any single protocol could handle all three roles. Just as TCP and HTTP serve fundamentally different purposes in networking, MCP, A2A, and Pilot are designed for distinct layers of the agent ecosystem. Attempting to replace one with another would create critical gaps in functionality.

Building the Future of Agentic Systems

The emergence of these protocols marks a significant evolution in AI infrastructure. While early agent systems relied on tightly coupled designs, modern architectures demand modularity, scalability, and interoperability. MCP, A2A, and Pilot together provide the foundation for building agentic systems that can dynamically adapt to new tools, delegate tasks intelligently, and operate across distributed environments.

As the field matures, we’re likely to see these protocols evolve further, with new standards emerging to address emerging challenges like cost optimization, latency reduction, and multi-agent orchestration. For now, the clearest path forward is to embrace all three—not as competitors, but as essential components of a unified agent stack.