标签：#a2aprotocol

This article provides a step-by-step guide to building and testing a cross-cloud currency agent. A coordinator built with Strands Agents and hosted on Amazon Bedrock AgentCore Runtime (in AWS us-east-1 ) discovers and delegates to a Google ADK agent (on GCP Cloud Run in us-central1 ) over A2A v1.0 , cross-checks results against an MCP exchange-rate tool , and measures what independent cross-cloud verification costs in latency, reliability, and overhead. What is This Project Trying to Do? Most Agent-to-Agent (A2A) protocol demos stop at "look, the HTTP 200 OK request succeeded." That is a smoke test, not an interoperability benchmark. This project goes further: an Amazon Bedrock AgentCore-hosted Strands Agents coordinator discovers and delegates to a Google ADK agent running on GCP Cloud Run, comparing the results against a local MCP stdio exchange-rate tool backed by live Frankfurter daily reference rates. We also compare the performance, developer experience, and wire compatibility directly against our previous benchmark run hosted on Microsoft Foundry in Azure ( gpt-5-mini ), giving us a true cross-cloud benchmark across AWS, Azure, and GCP. The questions we answer with hard empirical data rather than vibes: Can an AgentCore-hosted Strands agent discover and invoke a Google ADK agent through an A2A agent card with no framework-specific glue? What latency and token overhead does remote-agent verification add? Does independently verifying an MCP tool result over A2A improve correctness or failure recovery enough to justify that overhead? How does AWS Bedrock AgentCore Runtime compare like-for-like with Microsoft Foundry on Azure? Reduce, Re-Use, Re-Cycle! This builds directly on the currency agent from the previous articles in this series: Getting Started with MCP, ADK and A2A | Google Codelabs GitHub - jackwotherspoon/currency-agent That agent — built with Google ADK, Gemini 2.5 Flash, and a FastMCP exchange-rate server backed by the free Frankfurter API — serves a

Building a Benchmarking Agent with A2A and MCP This tutorial aims to build and test benchmarking Agents using the A2A protocol across several mainstream Cloud providers. A Master Orchestrator Agent is exposed via MCP to allow Antigravity CLI to be used as a MCP client to co-ordinate the benchmarks. Deja Vu — What is Old is New! This paper is a re-visiting of the original benchmark series with Gemini CLI over Node, GO, and Python: Cross Language A2A Agent Benchmarking with Gemini 3 and Gemini CLI In this updated version, the Antigravity CLI is used to push Rust Agents cross-cloud and co-ordinate Mersenne Prime Calculations. Why would I need Multi-Cloud Support? And Rust? Can’t I just use Python? Most mature Agent development tools and libraries are Python based. Python allows for rapid prototyping and evaluation of approaches. Python is also an interpreted language- which has trade-offs in memory safety, and performance. Other languages like GO and Rust offer high performance and memory safe operations. With a language neutral communication protocol — the actual Agent implementation of each Agent can be coded in the most appropriate language. What is this Approach actually Benchmarking? The high level goal was to measure the actual time spent running an algorithm in the native language code inside the A2A agent. Each language had a slightly different implementation due to the language syntax. After running the algorithm- each Agent was instructed to calculate and return the elapsed time for cross cloud comparison. What is the A2A protocol? The Agent2Agent (A2A) protocol, an open communication standard for AI agents, was initially introduced by Google in April 2025. It is specifically engineered to facilitate seamless interoperability within multi-agent systems, enabling AI agents developed by diverse providers or built upon disparate AI agent frameworks to communicate and collaborate effectively. A good overview of the A2A protocol can be found here: A2A Protocol Lan