Brad Axen
From A2A to MCP: How AI’s “Brains” are Connecting to “Arms and Legs”
#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.
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01:43 MIN
Exploring AI agents and the Model Context Protocol
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02:00 MIN
Understanding core AI concepts like LLMs, agents, and MCPs
aa
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Using the Model Context Protocol for AI agent integration
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Building AI agents using the Model Context Protocol
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Introducing key protocols for agent communication
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How MCP enables modern AI agent mashups
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01:42 MIN
Standardizing agent-tool communication with MCP
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