Best A2A Agents for DevOps & Infrastructure in 2026

DevOps is where A2A gets interesting fast. Instead of one monolithic automation tool, you can have specialized agents — one monitoring your telemetry, another deploying to Cloud Run, another watching Kubernetes costs — all communicating through a standard protocol. No shared codebase, no vendor lock-in, just HTTP and JSON-RPC.

Here are the agents worth evaluating right now.

Quick Comparison

Agent	Framework	Language	Best For
A2A Telemetry	Custom (Official)	Python	OpenTelemetry tracing for A2A calls
ADK Cloud Run	Google ADK	Python	Deploy agents to Google Cloud Run
Agentic Frameworks on AWS	LangGraph	Python	Multi-framework agents on AWS (ECS, Lambda)
K8s CostGuard AI	Google ADK	Python	Kubernetes cost analysis and optimization
AI Mocks	LangChain	TypeScript	Mock A2A agents for testing pipelines

A2A Telemetry (Official Sample)

Repo: a2aproject/a2a-samples/a2a-telemetry

This is the observability agent you plug into your A2A deployment from day one. It instruments A2A calls with OpenTelemetry traces, so you can see the full request path across agents in Jaeger, Grafana, or any OTel-compatible backend.

Why it matters: once you have more than two agents talking to each other, you need distributed tracing. Without it, debugging a failed task becomes guesswork. This agent is an official sample, so it tracks spec changes and serves as the reference for how to instrument A2A.

When to use it: Any production A2A deployment with 2+ agents. Add it early — retrofitting observability is painful.

ADK Cloud Run (Official Sample)

Repo: a2aproject/a2a-samples/adk-cloud-run

If you are on Google Cloud, this is your deployment reference. It wraps a Google ADK agent with Cloud Run's serverless container model: auto-scaling, zero to N, pay-per-request. The sample includes the Dockerfile, the service YAML, and IAM configuration.

This is not a complex agent — it is a deployment pattern. Fork it, swap out the agent logic, and you have a production-ready A2A endpoint on Cloud Run in under an hour.

When to use it: You want to deploy A2A agents on GCP without managing servers. Pairs well with Google ADK agents.

Agentic Frameworks on AWS

Repo: aws-samples/sample-agentic-frameworks-on-aws

Amazon's answer to the "how do I run A2A agents on AWS?" question. This sample shows how to deploy agents built with LangGraph, CrewAI, and other frameworks on AWS infrastructure — ECS, Lambda, and Bedrock. It includes CDK templates for infrastructure-as-code deployment.

The value is in the patterns, not the agent logic. You get a working example of A2A on AWS with proper VPC configuration, IAM roles, and service discovery. Fork and adapt.

When to use it: Your infrastructure is on AWS and you need a battle-tested deployment template. Check out our guide on deploying A2A agents to production for the full walkthrough.

K8s CostGuard AI

Repo: k8s-costguard-ai

A Google ADK agent that connects to your Kubernetes cluster and analyzes resource usage versus requests. It identifies over-provisioned pods, suggests right-sizing, and estimates cost savings. Expose it via A2A and other agents can query it: "What is our monthly Kubernetes spend?" or "Which namespaces are over-provisioned?"

Still early-stage (1 star), but the use case is solid. Kubernetes cost optimization is a real problem, and having an agent that any orchestrator can query via A2A is more composable than a standalone dashboard.

When to use it: You run Kubernetes and want automated cost analysis that other agents in your pipeline can consume.

AI Mocks

Repo: ai-mocks

Not a DevOps agent in the traditional sense, but critical DevOps infrastructure. AI Mocks lets you create mock A2A agents for testing. Instead of hitting real agents in your CI/CD pipeline (slow, flaky, expensive), you point your tests at mock endpoints that return predictable responses.

Built with LangChain and TypeScript, it supports configurable response patterns and latency simulation. Essential for any team doing integration testing with A2A agents.

When to use it: CI/CD pipelines that test A2A integrations. Pair it with the A2A Telemetry agent for full observability in your test environment.

What is Missing

The DevOps A2A space is early. There are clear gaps:

• CI/CD pipeline agents — no A2A agent yet that triggers GitHub Actions or GitLab CI based on agent decisions
• Incident response — PagerDuty/Opsgenie integration via A2A would let agents escalate automatically
• Database migration agents — schema changes coordinated between agents
• Security scanning — check our security agents for what is available today

If you are building in this space, the infrastructure category tracks every new agent as it ships.

Getting Started

The fastest path to an A2A DevOps setup:

1. Pick your cloud: Cloud Run sample for GCP, AWS sample for AWS
2. Add observability with the telemetry agent
3. Set up test mocks with AI Mocks
4. Browse all DevOps agents and infrastructure agents

For framework guidance, read our comparison of CrewAI vs LangGraph or start with the A2A protocol tutorial.