From MCP to A2A: Advancing Agent Evaluation

As AI agents evolve from performing isolated tasks to collaborating within multi-agent ecosystems, evaluating their performance becomes increasingly complex. In this discussion, we explore the progression from the Model Context Protocol (MCP) to Agent-to-Agent (A2A) protocols, highlighting their architectures, functionalities, and key evaluation metrics.

Why Protocols

Communication between Agent and Tools

Using the Model Context Protocol (MCP), an AI agent can reliably invoke external tools with standardized, secure, and auditable requests. MCP streamlines integration, ensuring predictable data formats and facilitating error tracing, which minimizes integration complexity and improves system robustness.

Communication between Agents

The Agent-to-Agent (A2A) protocol empowers vendor-specific agents to communicate seamlessly. A2A enables dynamic task delegation and shared context, allowing heterogeneous agents to coordinate effectively. This standardized framework enhances interoperability and ensures efficient, synchronized team-level decision-making.

MCP: Structured and Standardized Tool Invocation

The Model Context Protocol (MCP) addresses the challenge of standardizing how large language models (LLMs) interact with external data sources and applications. It employs a client–server architecture: