---
type: source
title: "Google is Quietly Revolutionizing AI Agents (This is HUGE)"
description: "Google's A2A (Agent-to-Agent) protocol is an open standard that lets independently-built agents discover and communicate with each other at runtime, and paired with MCP it forms the backend of scalable multi-agent AI systems, though real production challenges remain."
youtube_id: ywMWpmOOaSo
url: https://www.youtube.com/watch?v=ywMWpmOOaSo
slug: google-is-quietly-revolutionizing-ai-agents-this-is-huge
published: 2025-04-16
duration: "0:23:22"
recency_rank: 99
raw: "../raw/google-is-quietly-revolutionizing-ai-agents-this-is-huge.md"
tags: [agent-protocols, a2a, mcp, multi-agent]
created: 2026-07-21
updated: 2026-07-21
---

# Google is Quietly Revolutionizing AI Agents (This is HUGE)

Cole introduces Google's Agent-to-Agent (A2A) protocol, framing it as the agent-to-agent complement to MCP's agent-to-tool role. Both are open standards that were under-hyped at launch; A2A already has heavy industry backing (Salesforce, MongoDB, Oracle, LangChain, and others), which Cole reads as a strong signal it will follow MCP's slow-burn path to wide adoption. The core value of A2A is that it standardizes and makes accessible something engineers could always do by hand: connecting specialized agents together. Because agents can be built with different frameworks and hosted by different vendors, a shared protocol lets them interoperate seamlessly.

The protocol's key mechanism is the agent card, a single metadata file describing an agent's capabilities, how to reach it, and its auth requirements. Agents run as HTTP servers (like microservices) and are consumed by clients via tasks: a client fetches the agent card, generates a task ID, sends a JSON payload request, and gets a structured result back, with optional push notifications for real-time updates. This enables agent discovery, where a calling agent learns another agent's capabilities at runtime rather than hard-coding a brittle integration that breaks on every update. A2A is an architecture, not a pip-installable tool, so the transferable skill is learning how agents should communicate. Cole demonstrates a minimal Python server/client (using Pydantic AI + the Brave MCP server) that follows the spec without importing any A2A library.

He closes honestly with the downsides of distributed, protocol-based agent systems: testing complexity and microservice-style edge-case explosion, compounded by non-deterministic LLM nodes; expanded security surface area and data flowing to more third parties; authentication propagation across sub-agents and tools; and hidden complexity that makes debugging and error attribution hard. He argues these are solvable engineering problems (as they were for databases and microservices) that Google and Anthropic are actively addressing, and predicts A2A will become a real standard over the next year or two.

## Concepts covered

- [Agent Protocols](../concepts/agent-protocols.md)
- [Agent Teams](../concepts/agent-teams.md)
- [Model Context Protocol (MCP)](../concepts/mcp.md)
- [Agentic Workflow Engineering](../concepts/agentic-workflow-engineering.md)
- [The Problems With MCP Servers](../concepts/mcp-problems.md)
- [The Evolution of AI Coding](../concepts/the-evolution-of-ai-coding.md)

## Entities

- [Google](../entities/organizations/google.md)
- [Anthropic](../entities/organizations/anthropic.md)
- [MCP (Model Context Protocol)](../entities/tools/mcp.md)
- [Pydantic AI](../entities/tools/pydantic-ai.md)
- [Lovable](../entities/tools/lovable.md)
- [Dynamous](../entities/organizations/dynamous.md)
- [Cole Medin](../entities/people/cole-medin.md)

## Key moments

- **[0:00:00]** Introduces A2A as an agent-to-agent protocol, parallel to MCP as agent-to-tool
- **[0:01:29]** Shows Google's announcement post and the large roster of partner companies backing A2A
- **[0:03:35]** Crash-course example: a sales agent delegating to finance/data-analytics agents
- **[0:05:45]** Agent discovery: sales agent learns finance agent's capabilities at runtime, reducing brittle integrations
- **[0:06:59]** A2A GitHub repo overview and the agent card concept
- **[0:07:36]** Server/client (microservice-style) architecture and task-based communication with push notifications
- **[0:10:44]** A2A is an architecture, not a tool you pip install
- **[0:10:57]** How MCP and A2A compose: A2A at the agent layer, MCP at the tool layer
- **[0:12:04]** A2A + MCP = full backend; Lovable sponsor segment covers the frontend layer
- **[0:13:34]** Minimal Python A2A server/client demo using Pydantic AI + Brave MCP
- **[0:18:35]** The concerns: testing complexity, security, auth, hidden complexity, error attribution
- **[0:21:44]** These are solvable engineering problems; Google and Anthropic are working on them

## Transcript

[Raw transcript](../raw/google-is-quietly-revolutionizing-ai-agents-this-is-huge.md)
