From A2A to MCP: How AI’s “Brains” are Connecting to “Arms and Legs”

One AI asks another to read 50 pages and summarize them. This isn't sci-fi—it's how multi-agent systems use protocols to solve complex problems together.

#1about 4 minutes

Understanding the core loop of AI agents

AI agents operate in a continuous loop where the LLM generates tool calls as JSON, which are then executed and their results are fed back as the next prompt.

#2about 4 minutes

How Model Context Protocol standardizes tool integration

Model Context Protocol (MCP) provides a standard for agents to connect with external tools, benefiting both agent and tool developers by creating a unified ecosystem.

#3about 6 minutes

A practical demo of an agent using multiple tools

A demonstration shows the Goose AI agent using multiple MCP-enabled tools like Databricks and a file editor to query data and build a web dashboard.

#4about 4 minutes

Using agent recursion for better context management

Multi-agent systems use recursion, where a parent agent delegates tasks to sub-agents, primarily to manage and reduce the context window for improved performance and cost.

#5about 4 minutes

The role of the Agent-to-Agent (A2A) protocol

Google's Agent-to-Agent (A2A) protocol enables communication across different agents and organizations, with potential use cases in specialized tasks and front-end communication.

#6about 1 minute

The future of AI protocols and agent ecosystems

AI development is being shaped by protocols like the established MCP for tools and the emerging A2A for multi-agent systems, which will define future agent collaboration.