Protocols governing agentic AI are gaining traction, notably Anthropic's Model Context Protocol (MCP) and Google's Agent-to-Agent (A2A) protocol. MCP facilitates connections to external data stores, while A2A, introduced at Google I/O, shares a similar architecture. Both protocols have garnered industry support but are designed to address distinct problems. The confusion between them often arises from the vague definitions surrounding AI agents, which usually consist of models interpreting data and making decisions with access to specific functions and tools for task execution.
MCP and A2A address very different problems, with much of the confusion surrounding them rooted in the often vague definition of what constitutes an AI agent.
Agents generally feature a model responsible for interpreting information and making decisions, potentially accessing various functions or tools for executing tasks.
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