Cross Cloud A2A Agent Benchmarking

A developer benchmarked A2A (Agent2Agent) protocol agents across Google Cloud, Azure, and AWS by deploying Rust-based agents using the Antigravity CLI. The cross-cloud test measured native language execution time for Mersenne Prime calculations, comparing performance of agents implemented in different languages.

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. 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 https://medium.com/google-cloud/cross-language-a2a-agent-benchmarking-with-gemini-3-and-gemini-cli-930eb3fd8507 In this updated version, the Antigravity CLI is used to push Rust Agents cross-cloud and co-ordinate Mersenne Prime Calculations. 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. 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. 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: The official ADK for Python, GO, and Java provide built-in support for working with the A2A protocol. For other programming languages like JS, Rust, and .NET — 3rd party libraries are available to add support for the protocol. The main source for A2A Language support is the GitHub repo: This article targets a local, GCP, Azure, and AWS environment. The build tools for each language and cloud environment need to be in place. For building with Rust, the Rust tool chain is required. For GCP- this article provides some directions: Deploying a Rust A2A Agent to Google Cloud Run https://medium.com/google-cloud/deploying-a-rust-a2a-agent-to-google-cloud-run-b4767a441d3c For Azure — this article provides some directions: Deploying a Rust MCP Server to Azure ACA https://xbill999.medium.com/deploying-a-rust-mcp-server-to-azure-aca-3665889dff1c For AWS — this article provides some directions: Deploying a Rust A2A Agent to AWS Lightsail https://blog.stackademic.com/deploying-a-rust-a2a-agent-to-aws-lightsail-26348f2b65da Antigravity CLI is the follow-on successor to Gemini CLI- the terminal driven, agent assisted coding tool. Full details on installing Antigravity CLI are here: Getting Started with Antigravity CLI https://medium.com/google-cloud/getting-started-with-antigravity-cli-26c5da90951f Once you have all the tools in place- you can test the startup of Antigravity CLI. You will need to authenticate with a Google Cloud Project or your Google Account: agy This will start the interface: Verify that all the prerequisite packages and compilers are installed — and clone the sample Github repo: git clone https://github.com/xbill9/a2a-multicloud cd a2a-multicloud Once you have your Google Cloud Project and preferred authentication method — run the init.sh script to validate the setup: bash xbill@penguin:~/a2a-multicloud$ source init.sh WARNING: Your active project does not match the quota project in your local Application Default Credentials file. This might result in unexpected quota issues. To update your Application Default Credentials quota project, use the gcloud auth application-default set-quota-project command. environment: Development Updated property core/project . ADC is valid. Environment setup GOOGLE GENAI USE VERTEXAI=1 GOOGLE CLOUD PROJECT=comglitn GOOGLE CLOUD LOCATION=us-central1 IMAGEN MODEL="imagen-3.0-fast-generate-001" GENAI MODEL="gemini-2.5-flash" Cloud Login Credentialed Accounts ACTIVE ACCOUNT xbill@glitnir.com To set the active account, run: $ gcloud config set account ACCOUNT The set env.sh script is provided to set common ADK environment variables: bash xbill@penguin:~/a2a-multicloud$ source set env.sh Current Environment export GOOGLE GENAI USE VERTEXAI=1 export GOOGLE CLOUD PROJECT=comglitn export GOOGLE CLOUD LOCATION=us-central1 export IMAGEN MODEL="imagen-3.0-fast-generate-001" export GENAI MODEL="gemini-2.5-flash" export GCP AGENT URL="https://bench-rust-289270257791.us-central1.run.app" export AWS AGENT URL="https://a2a-lightsail-rust-aws.6wpv8vensby5c.us-east-1.cs.amazonlightsail.com" export AZURE AGENT URL="https://a2a-app-penguin.icyplant-a768d75c.westus2.azurecontainerapps.io" Cloud Login Credentialed Accounts ACTIVE ACCOUNT xbill@glitnir.com To set the active account, run: $ gcloud config set account ACCOUNT ADK Version adk, version 2.1.0 If your application default credentials expires or your Google Cloud Authentication expires you will get an error. The workaround is to re-authenticate: gcloud auth login gcloud auth application-default login Another common error is that the environment variables are not set correctly. Go the the root directory and re-run the set env.sh to set the variables: cd ~/a2a-multicloud source set env.sh The A2A Inspector is a standalone tool that provides low level visibility into the A2A protocol. The GitHub is available here: GitHub - a2aproject/a2a-inspector: Validation Tools for A2A Agents https://github.com/a2aproject/a2a-inspector A summary of the features of the A2A inspector can be found here: To install the A2A Inspector: cd ~ git clone https://github.com/a2aproject/a2a-inspector Then follow the build instructions — you need uv , and a recent version of a2a-inspector/README.md at main · a2aproject/a2a-inspector https://github.com/a2aproject/a2a-inspector/blob/main/README.md Once the A2A inspector has been installed- you can validate the installation by using this URL: In mathematics https://en.wikipedia.org/wiki/Mathematics , a Mersenne prime is a prime number https://en.wikipedia.org/wiki/Prime number that is one less than a power of two https://en.wikipedia.org/wiki/Power of two . As of 2025, 52 Mersenne primes https://en.wikipedia.org/wiki/List of Mersenne primes and perfect numbers are known. An interesting thing about Mersenne primes is that they are the easiest natural numbers to prove to be primes, so they make up the largest category on the list of known prime numbers. This example was chosen as a good problem as the computation is CPU bound and gets exponentially longer for each higher number. The a2a-multicloud Repo has sample scripts for running the agents across cloud environments. Each agent implements a Mersenne Prime Number generator with only basic optimization. The root directory of the a2a-multicloud directory contains several common agent development languages. These include: This agent provides a basic Agent that generates Mersenne prime numbers. The A2A inspector can be used to validate the Agent: The agent can be reviewed: bash xbill@penguin:~/a2a-multicloud/benchmark-rust$ make start Building the Rust project... Finished dev profile unoptimized + debuginfo target s in 0.05s Starting local agent on port 8104 in the background... Local agent started successfully PID: 87753 . xbill@penguin:~/a2a-multicloud/benchmark-rust$ make status Local agent is running PID: 87753 . Listening on port: 8104 Testing endpoint health... OK and the agent card: bash xbill@penguin:~/a2a-multicloud/benchmark-rust$ make card Fetching local benchmark agent card from http://localhost:8104... { "name": "Mersenne Prime Agent Rust", "description": "A rust agent that builds a list of the first n Mersenne primes and reports the elapsed time. Configured with model: Not specified.", "protocolVersion": "0.3.0", "version": "0.1.0", "url": "http://0.0.0.0:8104", "skills": { "id": "find-mersenne-rust", "name": "Find Mersenne Primes in rust", "description": "Finds the list of the first n Mersenne primes in Rust, or calculates a specific Mersenne prime for a given exponent parameter.", "tags": "math", "benchmark" }, { "id": "check-status-rust", "name": "Check calculation status in Rust", "description": "Returns 'ready' if no calculation is active, and 'not ready' if a calculation is live.", "tags": "status", "health" } , "capabilities": {}, "defaultInputModes": , "defaultOutputModes": } and local A2A call: bash xbill@penguin:~/a2a-multicloud/benchmark-rust$ make a2a Sending A2A status request to http://localhost:8104/... { "id": 1, "jsonrpc": "2.0", "result": { "contextId": "status-context-id", "kind": "message", "messageId": "971ff86d-7bfc-4046-858a-16e7857ba9d0", "parts": { "kind": "text", "text": "ready" } , "role": "agent" } } This agent provides Rust agent that implements the Mersenne generation algorithm Hosted on GCP. xbill@penguin:~/a2a-multicloud/benchmark-rust-gcp make deploy xbill@penguin:~/a2a-multicloud/benchmark-rust-gcp$ make endpoint https://bench-rust-fgasxpwzoq-uc.a.run.app Then get the Agent Card: bash xbill@penguin:~/a2a-multicloud/benchmark-rust-gcp$ make status Checking Google Cloud Run service status for bench-rust... NAME STATUS URL bench-rust True https://bench-rust-fgasxpwzoq-uc.a.run.app Checking deployed agent health at https://bench-rust-fgasxpwzoq-uc.a.run.app/.well-known/agent.json ... Agent health check: PASSED HTTP 200 To check the Agent Card: bash xbill@penguin:~/a2a-multicloud/benchmark-rust-gcp$ make card Fetching agent card from https://bench-rust-fgasxpwzoq-uc.a.run.app/.well-known/agent-card.json ... { "name": "Mersenne Prime Agent Rust", "description": "A rust agent that builds a list of the first n Mersenne primes and reports the elapsed time. Configured with model: Not specified.", "protocolVersion": "0.3.0", "version": "0.1.0", "url": "http://0.0.0.0:8080", "skills": { "id": "find-mersenne-rust", "name": "Find Mersenne Primes in rust", "description": "Finds the list of the first n Mersenne primes in Rust, or calculates a specific Mersenne prime for a given exponent parameter.", "tags": "math", "benchmark" }, { "id": "check-status-rust", "name": "Check calculation status in Rust", "description": "Returns 'ready' if no calculation is active, and 'not ready' if a calculation is live.", "tags": "status", "health" } , "capabilities": {}, "defaultInputModes": , "defaultOutputModes": } To check that that remote agent can support A2A: bash xbill@penguin:~/a2a-multicloud/benchmark-rust-gcp$ make a2a Sending A2A status request to https://bench-rust-fgasxpwzoq-uc.a.run.app/ ... { "id": 1, "jsonrpc": "2.0", "result": { "contextId": "status-context-id", "kind": "message", "messageId": "108d0f7c-c2e6-4d1e-8306-843a6dcb999c", "parts": { "kind": "text", "text": "ready" } , "role": "agent" } } This agent provides Rust agent that implements the Mersenne generation algorithm Hosted on Azure: bash xbill@penguin:~/a2a-multicloud/benchmark-rust-azure$ make deploy All requirements met. Building the Docker image... xbill@penguin:~/a2a-multicloud/benchmark-rust-azure$ make endpoint https://a2a-app-penguin.icyplant-a768d75c.westus2.azurecontainerapps.io Then get the Agent Card: bash xbill@penguin:~/a2a-multicloud/benchmark-rust-azure$ make status mcp-aca-rust-azure is not running locally. Checking Azure Container App status for a2a-app-penguin... Name ProvisioningState FQDN --------------- ------------------- --------------------------------------------------------------- a2a-app-penguin Succeeded a2a-app-penguin.icyplant-a768d75c.westus2.azurecontainerapps.io Checking deployed agent health at https://a2a-app-penguin.icyplant-a768d75c.westus2.azurecontainerapps.io/.well-known/agent.json ... Agent health check: PASSED HTTP 200 To check the Agent Card: bash xbill@penguin:~/a2a-multicloud/benchmark-rust-azure$ make card Fetching agent card from https://a2a-app-penguin.icyplant-a768d75c.westus2.azurecontainerapps.io/.well-known/agent-card.json ... { "name": "Mersenne Prime Agent Rust", "description": "A rust agent that builds a list of the first n Mersenne primes and reports the elapsed time. Configured with model: Not specified.", "protocolVersion": "0.3.0", "version": "0.1.0", "url": "http://0.0.0.0:8080", "skills": { "id": "find-mersenne-rust", "name": "Find Mersenne Primes in rust", "description": "Finds the list of the first n Mersenne primes in Rust, or calculates a specific Mersenne prime for a given exponent parameter.", "tags": "math", "benchmark" }, { "id": "check-status-rust", "name": "Check calculation status in Rust", "description": "Returns 'ready' if no calculation is active, and 'not ready' if a calculation is live.", "tags": "status", "health" } , "capabilities": {}, "defaultInputModes": , "defaultOutputModes": } To check that that remote agent can support A2A: bash xbill@penguin:~/a2a-multicloud/benchmark-rust-azure$ make a2a Sending A2A status request to https://a2a-app-penguin.icyplant-a768d75c.westus2.azurecontainerapps.io/... { "id": 1, "jsonrpc": "2.0", "result": { "contextId": "status-context-id", "kind": "message", "messageId": "5d6605f6-9926-481f-b807-8598b81b8cc1", "parts": { "kind": "text", "text": "ready" } , "role": "agent" } } This agent provides Rust agent that implements the Mersenne generation algorithm Hosted on AWS: bash xbill@penguin:~/a2a-multicloud/benchmark-rust-aws$ make deploy Building the Docker image... + Building 3.8s 10/13 xbill@penguin:~/a2a-multicloud/benchmark-rust-aws$ make endpoint https://a2a-lightsail-rust-aws.6wpv8vensby5c.us-east-1.cs.amazonlightsail.com/ Then get the Agent Card: bash xbill@penguin:~/a2a-multicloud/benchmark-rust-aws$ make status Checking AWS Lightsail service status for a2a-lightsail-rust-aws... ------------------------------------------------------------------------------------------------------------------------ | GetContainerServices | +------------+--------+------------+-----------------------------------------------------------------------------------+ | Deployment | Power | State | URL | +------------+--------+------------+-----------------------------------------------------------------------------------+ | ACTIVE | nano | DEPLOYING | https://a2a-lightsail-rust-aws.6wpv8vensby5c.us-east-1.cs.amazonlightsail.com/ | +------------+--------+------------+-----------------------------------------------------------------------------------+ Checking endpoint health at https://a2a-lightsail-rust-aws.6wpv8vensby5c.us-east-1.cs.amazonlightsail.com/health... Health status: HEALTHY HTTP 200 To check the Agent Card: bash xbill@penguin:~/a2a-multicloud/benchmark-rust-aws$ make card Fetching agent card from https://a2a-lightsail-rust-aws.6wpv8vensby5c.us-east-1.cs.amazonlightsail.com/.well-known/agent-card.json ... { "name": "Mersenne Prime Agent Rust", "description": "A rust agent that builds a list of the first n Mersenne primes and reports the elapsed time. Configured with model: Not specified.", "protocolVersion": "0.3.0", "version": "0.1.0", "url": "http://0.0.0.0:8080", "skills": { "id": "find-mersenne-rust", "name": "Find Mersenne Primes in rust", "description": "Finds the list of the first n Mersenne primes in Rust, or calculates a specific Mersenne prime for a given exponent parameter.", "tags": "math", "benchmark" }, { "id": "check-status-rust", "name": "Check calculation status in Rust", "description": "Returns 'ready' if no calculation is active, and 'not ready' if a calculation is live.", "tags": "status", "health" } , "capabilities": {}, "defaultInputModes": , "defaultOutputModes": } To check that that remote agent can support A2A: bash xbill@penguin:~/a2a-multicloud/benchmark-rust-aws$ make a2a Sending A2A status request to https://a2a-lightsail-rust-aws.6wpv8vensby5c.us-east-1.cs.amazonlightsail.com/... { "id": 1, "jsonrpc": "2.0", "result": { "contextId": "status-context-id", "kind": "message", "messageId": "1245ea8c-e8d3-436a-8e27-0b25e90a8a42", "parts": { "kind": "text", "text": "ready" } , "role": "agent" } } The final agent follows a slightly different pattern. It provides a minimal agent with several functions Tools/Skills . To build- bash make xbill@penguin:~/a2a-multicloud/rust-master$ make lint Linting the Rust project... Finished dev profile unoptimized + debuginfo target s in 0.09s To get the local endpoint: bash xbill@penguin:~/a2a-multicloud/rust-master$ make endpoint http://localhost:8100 Then use the A2A inspector: and check the A2A Agent card: bash xbill@penguin:~/a2a-multicloud/rust-master$ make card Fetching master agent card from http://localhost:8100... { "name": "master agent", "description": "Master A2A Agent that coordinates prime agents including rust agent, gcp agent, aws agent, and azure agent using model: gemini-2.5-flash.", "protocolVersion": "0.3.0", "version": "0.1.0", "url": "http://0.0.0.0:8100/", "skills": { "id": "coordinate-agents", "name": "Coordinate sub-agents", "description": "Delegates requests to appropriate sub-agents for weather, time, events, prime calculations, and general greetings.", "tags": "coordinator", "a2a" }, { "id": "calculate-mersenne-prime", "name": "Calculate Mersenne Prime", "description": "Calculates Mersenne prime of n using connected sub-agents.", "tags": "mersenne", "prime", "a2a" } , "capabilities": {}, "defaultInputModes": , "defaultOutputModes": } and A2A status: bash xbill@penguin:~/a2a-multicloud/rust-master$ make a2a Sending A2A status request to http://localhost:8100/... The Master Agent starts a FastMCP server interface. This allows Antigravity CLI to be used as a MCP client: bash xbill@penguin:~/a2a-multicloud/.agents$ more mcp config { "mcpServers": { "rust-master": { "command": "cargo", "args": "run", "--manifest-path", "/home/xbill/a2a-multicloud/rust-master/Cargo.toml", "--quiet", "--", "--stdio" , "env": { "GOOGLE GENAI USE VERTEXAI": "1", "GOOGLE CLOUD PROJECT": "aisprint-491218", "GOOGLE CLOUD LOCATION": "us-central1", "GENAI MODEL": "gemini-2.5-flash", "MODEL NAME": "gemini-2.5-flash" } } } } The MCP tools can be reviewed: MCP Servers Plugins ~/.gemini/antigravity-cli/plugins ✓ google-dev-knowledge Tools: search documents, answer query, get documents ✓ rust-master Tools: ask master agent, calculate mersenne prime, check agents status The agent status can be checked with a MCP call to the Master Agent: ● rust-master/check agents status Call check agents status MCP tool ctrl+o to expand I ran the check agents status tool from the rust-master MCP server. The check succeeded, confirming that all sub- agents are healthy and online: • AWS Remote : ✅ ONLINE HTTP 200 • GCP Remote : ✅ ONLINE HTTP 200 • Azure Remote : ✅ ONLINE HTTP 200 • Rust Local : ✅ ONLINE HTTP 200 Summary of Work: 1. Inspected Makefile & Scripts: Reviewed target definitions make status , make a2a and script configurations. 2. Checked Endpoint Health: Verified the agent URL parameters and credentials from .env . 3. Executed MCP Status Tool: Invoked the lazy-loaded MCP tool check agents status from the rust-master coordinator, confirming all remote and local calculation sub-agents are online and ready to accept tasks. and the agent can be verified: ask master agent what do you do ● rust-master/ask master agent Call ask master agent MCP tool ctrl+o to expand I called the ask master agent tool with the query "what do you do". Here is the response from the Master Agent: │ "I am a Master A2A Agent. I can delegate tasks to specialized sub-agents Rust, GCP, AWS, or Azure agents to │ calculate Mersenne primes or check exponents." Summary of Work: 1. Invoked the MCP Tool: Called ask master agent on the rust-master server with the prompt what do you do . 2. Presented the Results: Relayed the master agent's self-description as a coordinator for distributed multi-cloud Mersenne prime calculations. All of the setup and configuration allows you to start debugging and visualizing Agent flows using the A2A protocol. The remote Agents haven been validated using the A2A Inspector. The Master Agent acts as a co-ordinator to delegate Mersenne Prime Calculations to remote A2A agents cross cloud. Now that the individual Agents have been validated — open antigravity and use the MCP tool to start the session: what does calculate mersenne prime do? ● Search Grep search run a2a benchmark ctrl+o to expand I will view the definition of run a2a benchmark which implements calculate mersenne prime in rust- master/src/main.rs . ● Read /home/xbill/a2a-multicloud/rust-master/src/main.rs ctrl+o to expand The calculate mersenne prime n tool executes a distributed benchmark to find and verify Mersenne primes 2ᵖ - 1 for exponents from 1 to n. How it Works: 1. Assigns Tasks Round-Robin: It iterates through the exponents 1 ≤ i ≤ n and checks the readiness of the connected sub-agents GCP, AWS, Azure, and Local using a round-robin schedule. 2. Delegates Computation: Once a ready agent is found, the master agent sends the exponent i to it via the A2A protocol. The sub-agent calculates whether 2ⁱ - 1 is prime. 3. Aggregates Timings & Results: It measures the ready check time and the calculation time for each exponent. 4. Saves Metrics: The benchmark results, including timing data and calculated primes, are compiled into a summary markdown table and persisted to disk e.g., in benchmark results.json . Summary of Work: 1. Source Code Review: Inspected the main.rs function in rust-master/src/main.rs to extract the exact program flow and role of each agent. 2. Explained Mechanics: Highlighted the round-robin task assignment, remote A2A calls, and metrics compilation features. Once all the agents have been started — the Master agent on port 8100 is connected over MCP with Antigravity CLI: run the calculate mersenne prime with 100 I will execute the calculation of Mersenne primes up to exponent 100 by querying the Master Agent's HTTP MCP route. ● Bash curl -s -X POST -H "Content-Type: application/json" -d '{"jsonrpc": "2.0", "id": 1, "method": ... ctrl+o to expand I will wait for the distributed calculation up to exponent 100 to complete. The distributed calculation up to exponent n = 100 has completed successfully Distributed Mersenne Prime Generation Report • Timestamp: 2026-06-02T13:40:04-04:00 • Cumulative Duration: 55,625.57 ms ~55.6 seconds Generated Mersenne Primes • 2² - 1 = 3 via AWS Lightsail • 2³ - 1 = 7 via Azure Container Apps • 2⁵ - 1 = 31 via GCP Cloud Run • 2⁷ - 1 = 127 via Azure Container Apps • 2¹³ - 1 = 8,191 via GCP Cloud Run • 2¹⁷ - 1 = 131,071 via GCP Cloud Run • 2¹⁹ - 1 = 524,287 via Azure Container Apps • 2³¹ - 1 = 2,147,483,647 via Azure Container Apps • 2⁶¹ - 1 = 2,305,843,009,213,693,951 via GCP Cloud Run • 2⁸⁹ - 1 = 618,970,019,642,690,137,449,562,111 via GCP Cloud Run ────── Coordinated Exponent Assignments Exponent | Assigned Agent | Ready Check Time | Calc Time | Result --------------------|------------------------|----------------------|----------------------|---------------------- 1 | GCP Cloud Run | 662.32 ms | 658.00 ms | not prime 2 | AWS Lightsail | 418.86 ms | 117.14 ms | 3 3 | Azure Container Apps | 330.12 ms | 366.37 ms | 7 4 | Rust Local | 30.34 ms | 12.07 ms | not prime 5 | GCP Cloud Run | 651.16 ms | 667.50 ms | 31 6 | AWS Lightsail | 119.91 ms | 103.80 ms | not prime 7 | Azure Container Apps | 337.03 ms | 338.40 ms | 127 8 | Rust Local | 30.31 ms | 13.20 ms | not prime 9 | GCP Cloud Run | 628.36 ms | 621.74 ms | not prime 10 | AWS Lightsail | 108.49 ms | 113.11 ms | not prime 11 | Azure Container Apps | 336.13 ms | 324.22 ms | not prime 12 | Rust Local | 21.45 ms | 15.00 ms | not prime 13 | GCP Cloud Run | 629.91 ms | 662.56 ms | 8191 14 | AWS Lightsail | 111.40 ms | 119.59 ms | not prime 15 | Azure Container Apps | 325.95 ms | 336.56 ms | not prime 16 | Rust Local | 36.22 ms | 13.74 ms | not prime 17 | GCP Cloud Run | 678.16 ms | 635.27 ms | 131071 18 | AWS Lightsail | 111.02 ms | 115.05 ms | not prime 19 | Azure Container Apps | 343.99 ms | 306.91 ms | 524287 20 | Rust Local | 24.83 ms | 25.15 ms | not prime 21 | GCP Cloud Run | 612.20 ms | 610.21 ms | not prime 22 | AWS Lightsail | 103.64 ms | 121.31 ms | not prime 23 | Azure Container Apps | 362.88 ms | 361.34 ms | not prime 24 | Rust Local | 25.12 ms | 14.09 ms | not prime 25 | GCP Cloud Run | 646.72 ms | 641.75 ms | not prime 26 | AWS Lightsail | 98.67 ms | 109.74 ms | not prime 27 | Azure Container Apps | 380.92 ms | 319.36 ms | not prime 28 | Rust Local | 24.17 ms | 17.71 ms | not prime 29 | GCP Cloud Run | 596.21 ms | 615.23 ms | not prime 30 | AWS Lightsail | 110.32 ms | 102.89 ms | not prime 31 | Azure Container Apps | 393.16 ms | 325.54 ms | 2147483647 32 | Rust Local | 20.81 ms | 14.46 ms | not prime 33 | GCP Cloud Run | 616.02 ms | 610.53 ms | not prime 34 | AWS Lightsail | 98.09 ms | 113.49 ms | not prime 35 | Azure Container Apps | 331.80 ms | 396.37 ms | not prime 36 | Rust Local | 27.76 ms | 26.81 ms | not prime 37 | GCP Cloud Run | 626.58 ms | 641.85 ms | not prime 38 | AWS Lightsail | 108.36 ms | 110.98 ms | not prime 39 | Azure Container Apps | 343.18 ms | 341.17 ms | not prime 40 | Rust Local | 30.80 ms | 21.23 ms | not prime 41 | GCP Cloud Run | 606.28 ms | 637.02 ms | not prime 42 | AWS Lightsail | 106.25 ms | 116.14 ms | not prime 43 | Azure Container Apps | 339.94 ms | 318.64 ms | not prime 44 | Rust Local | 21.90 ms | 12.41 ms | not prime 45 | GCP Cloud Run | 628.11 ms | 640.00 ms | not prime 46 | AWS Lightsail | 100.35 ms | 112.84 ms | not prime 47 | Azure Container Apps | 343.33 ms | 323.55 ms | not prime 48 | Rust Local | 19.90 ms | 28.04 ms | not prime 49 | GCP Cloud Run | 643.14 ms | 664.11 ms | not prime 50 | AWS Lightsail | 113.88 ms | 103.43 ms | not prime 51 | Azure Container Apps | 364.66 ms | 317.36 ms | not prime 52 | Rust Local | 22.84 ms | 14.70 ms | not prime 53 | GCP Cloud Run | 617.28 ms | 651.10 ms | not prime 54 | AWS Lightsail | 103.60 ms | 114.45 ms | not prime 55 | Azure Container Apps | 340.59 ms | 323.87 ms | not prime 56 | Rust Local | 26.55 ms | 11.62 ms | not prime 57 | GCP Cloud Run | 635.30 ms | 625.28 ms | not prime 58 | AWS Lightsail | 106.18 ms | 117.21 ms | not prime 59 | Azure Container Apps | 328.18 ms | 336.98 ms | not prime 60 | Rust Local | 16.18 ms | 17.11 ms | not prime 61 | GCP Cloud Run | 621.01 ms | 642.30 ms | 2305843009213693951 62 | AWS Lightsail | 106.05 ms | 115.97 ms | not prime 63 | Azure Container Apps | 322.90 ms | 346.61 ms | not prime 64 | Rust Local | 25.02 ms | 13.26 ms | not prime 65 | GCP Cloud Run | 610.11 ms | 627.14 ms | not prime 66 | AWS Lightsail | 105.60 ms | 107.45 ms | not prime 67 | Azure Container Apps | 337.70 ms | 323.28 ms | not prime 68 | Rust Local | 15.38 ms | 20.14 ms | not prime 69 | GCP Cloud Run | 625.35 ms | 643.10 ms | not prime 70 | AWS Lightsail | 108.00 ms | 108.02 ms | not prime 71 | Azure Container Apps | 323.01 ms | 331.95 ms | not prime 72 | Rust Local | 31.18 ms | 28.03 ms | not prime 73 | GCP Cloud Run | 647.98 ms | 627.10 ms | not prime 74 | AWS Lightsail | 107.88 ms | 112.41 ms | not prime 75 | Azure Container Apps | 351.71 ms | 340.42 ms | not prime 76 | Rust Local | 21.00 ms | 25.80 ms | not prime 77 | GCP Cloud Run | 624.00 ms | 647.06 ms | not prime 78 | AWS Lightsail | 97.09 ms | 111.97 ms | not prime 79 | Azure Container Apps | 337.90 ms | 346.54 ms | not prime 80 | Rust Local | 24.42 ms | 13.95 ms | not prime 81 | GCP Cloud Run | 624.80 ms | 631.11 ms | not prime 82 | AWS Lightsail | 108.87 ms | 107.81 ms | not prime 83 | Azure Container Apps | 332.57 ms | 322.07 ms | not prime 84 | Rust Local | 19.99 ms | 16.02 ms | not prime 85 | GCP Cloud Run | 622.47 ms | 651.63 ms | not prime 86 | AWS Lightsail | 112.12 ms | 107.24 ms | not prime 87 | Azure Container Apps | 332.57 ms | 340.05 ms | not prime 88 | Rust Local | 24.79 ms | 29.77 ms | not prime 89 | GCP Cloud Run | 646.21 ms | 667.60 ms | 618970019642690137449562111 90 | AWS Lightsail | 102.15 ms | 113.82 ms | not prime 91 | Azure Container Apps | 343.99 ms | 342.82 ms | not prime 92 | Rust Local | 29.97 ms | 13.91 ms | not prime 93 | GCP Cloud Run | 661.59 ms | 662.92 ms | not prime 94 | AWS Lightsail | 112.50 ms | 112.80 ms | not prime 95 | Azure Container Apps | 328.13 ms | 337.41 ms | not prime 96 | Rust Local | 26.83 ms | 24.57 ms | not prime 97 | GCP Cloud Run | 636.67 ms | 601.39 ms | not prime 98 | AWS Lightsail | 105.62 ms | 112.77 ms | not prime 99 | Azure Container Apps | 327.66 ms | 327.75 ms | not prime 100 | Rust Local | 31.73 ms | 18.09 ms | not prime ────── Summary of Work: 1. Invoked Calculation via HTTP Endpoint: Executed the calculate mersenne prime call with n = 100 against the Master Agent's HTTP POST interface. 2. Aggregated Results: Processed the response containing two additional large Mersenne primes 2⁶¹ - 1 and 2⁸⁹ - 1 and the complete assignment distribution. The results of the benchmark are graphed: Antigravity CLI summarized the results of the Agent Benchmarking: summarize key results cross cloud ▸ Thought Process Analyzing Cross-Cloud Results Here is a cross-cloud summary of the key findings, focusing on architecture, network latency, authentication overhead, and calculation behaviors: 1. Latency & Network Transit Comparison The total round-trip time RTT for a single round of checks and calculation varies heavily by cloud service model: • AWS Lightsail : ~232 ms RTT 120ms Ready Check + 112ms Calculation • Strengths: Lightsail container instances behave very similarly to standard VMs, offering quick, persistent HTTP response times and low network hops. • Azure Container Apps : ~679 ms RTT 341ms Ready Check + 338ms Calculation • Characteristics: Azure Container Apps run inside a managed Kubernetes framework K8s/Envoy ingress , adding slight container routing and warm-up latency. • GCP Cloud Run : ~1,275 ms RTT 635ms Ready Check + 640ms Calculation • Characteristics: Serverless scaling latency, combined with authentication checks, leads to the highest round- trip time. ────── 2. The Authentication Overhead The "Why" There is a massive latency difference between GCP and the other clouds, caused by security configurations: • GCP Cloud Run: Requires secure OIDC identity authentication. For every single network call, the master agent calls gcloud auth print-identity-token as a local subprocess. Spawning this CLI subprocess on every check and call introduces an extra ~300ms to ~500ms of local delay. • AWS & Azure: These sub-agents are deployed with publicly accessible HTTP endpoints that do not require OIDC tokens, avoiding any local CLI token generation overhead. ────── 3. Latency Symmetry Network vs. Compute Across all cloud providers, the Ready Check Time and Calculation Time are nearly identical: • AWS: ~120 ms vs ~112 ms • Azure: ~341 ms vs ~338 ms • GCP: ~635 ms vs ~640 ms Insight: This symmetry proves that network transport and handshake/auth overhead ping and TLS/IAM checks constitute over 95% of the latency. The actual math workload calculating exponent primality is extremely fast and has negligible latency impact at these exponent sizes. ────── 4. Cross-Cloud Protocol Success • The master agent successfully coordinated with all three major cloud providers GCP, AWS, Azure and the local runner. • Multi-cloud work distribution successfully validated Mersenne prime candidates, returning identical results regardless of which cloud hosting provider did the execution. Summary of Work: 1. Analyzed Authentication Mechanics: Explored the main.rs subprocess overhead in the coordinator codebase. 2. Evaluated Latency Patterns: Compared ready checks vs. calculation times to deduce that network transit and security handshakes dominate overall duration. To improve the GCP numbers- the Auth token is preset: export GCP ID TOKEN=$ gcloud auth print-identity-token Then the benchmark is re-run. There is a slight variance on each run: Here is a summary of the performance and distribution metrics comparing the cloud providers and the local environment from the Mersenne prime calculation run exponents 1 to 100 : Overall Performance Metrics Environment | Tasks Ass… | Cold Start Ready… | Avg Warm Ready T… | Avg Calculation T… | Compute Perform… ------------------------|------------|-------------------|-------------------|--------------------|------------------ Rust Local | 25 | 12.23 ms | 21.63 ms | 11.37 ms | 1 Fastest AWS Lightsail | 25 | 544.26 ms | 107.94 ms | 110.79 ms | 2 GCP Cloud Run | 25 | 484.59 ms | 127.31 ms | 135.49 ms | 3 Azure Container Apps | 25 | 20,711.57 ms | 348.39 ms | 354.26 ms | 4 Slowest ────── Key Findings 1. Cold Starts & Overhead Ready Time : • Azure Container Apps experienced a significant initial cold start latency of ~20.7 seconds on its very first assignment exponent 3 . Even once warm, its ready time overhead ~348 ms remained roughly 3x slower than AWS and GCP. • AWS Lightsail and GCP Cloud Run showed excellent and comparable warm ready times of 108 ms and 127 ms respectively, with minor startup penalties ~500 ms on their first task execution. 2. Compute Performance Calculation Time : • Local Rust is the fastest ~11.4 ms average because it runs bare metal without any network traversal, agent orchestration, or cloud virtualization overhead. • AWS outperformed both GCP and Azure, running calculations in 110.79 ms average. • GCP followed closely behind AWS at 135.49 ms average. • Azure was the slowest cloud computing node, taking 354.26 ms average—roughly 3x slower than AWS and 2.6x slower than GCP. 3. Mersenne Primes Discovered by Agent: • GCP Cloud Run : Discovered 5 primes Exponents: 5, 13, 17, 61, 89 • Azure Container Apps : Discovered 4 primes Exponents: 3, 7, 19, 31 • AWS Lightsail : Discovered 1 prime Exponent: 2 • Rust Local : Discovered 0 primes Since tasks were assigned in a round-robin format, the local agent was assigned exponents that are multiples of 4 4,8,12,… , none of which are prime . After adjusting the OIDC token for GCP- the benchmark was re-run: The goal of the demo/article was to get basic Agents implemented across multiple clouds and benchmark the Agent performance finding Mersenne primes. The key to the solution was the A2A Agent to Agent protocol. One local and three remote agents were presented — covering various combinations of deployment clouds. Finally — a Master/Orchestrator agent was started to connect and delegate to the other agents via the A2A protocol. Antigravity CLI was used to connect to the Master Agent over MCP and execute the benchmarks.