Google's Open Agentic Orchestrator
Google open-sourced AX, a declarative orchestrator that sandboxes and scales agentic tasks to billions of concurrent sessions on its Agent Substrate runtime.
AX is an open, declarative control plane from Google for running agentic workloads, with tasks and workspaces defined in YAML (apiVersion ax.io/v1alpha1) and managed via a CLI (ax apply, ax watch, ax ssh, ax suspend/resume). Each task runs as a lightweight actor in a sandboxed workspace with fenced networking and git-backed environments, scaling to billions of concurrent agent sessions per cluster with sub-second suspend/resume and dense resource multiplexing. The platform adds generative features such as workspaces prepared from plain-English descriptions by an agent at first boot. AX builds on Google's Agent Substrate compute runtime and draws on agentic runtime research from Google DeepMind.
- Declarative YAML primitives define tasks, workspaces, network policies, and models for agent execution.
- Lightweight actors scale to billions of concurrent agent sessions with sub-second suspend, resume, and no cold start.
- Tasks run in sandboxed workspaces with fenced networks, git-backed repos, and interactive ax ssh debugging.
- Generative workspaces accept plain-English environment descriptions fulfilled by an agent on first boot.
- Built on Agent Substrate, targeting RL loops, trajectory collection, and agent evaluation at scale.
Full article763 words · extracted from agentexecutor.io · click to collapse
Declare an agentic task. AX runs it at scale.
AX sandboxes your task, wires up its workspace, fences its network, and helps you run billions of them per cluster. Either use a single task per agent, or compose as many as your agent needs.
$ cat task.yaml
apiVersion: ax.io/v1alpha1
kind: Workspace
metadata:
name: golang
spec:
git:
- repo: https://github.com/golang/go.git
branch: "my-fix"
---
apiVersion: ax.io/v1alpha1
kind: Task
metadata:
name: test
spec:
workspaces:
- name: golang
goal: "Ensure that Go tool chain is available and is built from source"
debug: true
$ ax apply -f task.yaml
workspace.ax.io/golang created
task.ax.io/test created
$ ax watch task test
Watching task default/test...
[10:42:01] Phase: Pending Actor: test WorkerIP:
[10:42:05] Phase: Running Actor: test WorkerIP: 10.20.3.67
Task reached terminal phase "Running".
$ ax get tasks
NAME ATESPACE PHASE ACTOR WORKER-IP AGE
test default Running test 10.20.3.67 5s
$ ax ssh test -- ls /workspace
go
$ ax ssh test -- cd /workspace/go && go build ./...
$ ax ssh test -- ps -o pid,cmd
PID CMD
1 /usr/local/bin/ax-task-runner
12 go build ./...
$ ax ssh test -- touch notes.txt
$ ax suspend task test
task.ax.io/test suspended
$ ax resume task test
task.ax.io/test resumed
$ ax ssh test -- ls notes.txt
notes.txt
$ ax suspend task test
task.ax.io/test suspended
$ ax delete task test
task.ax.io/test deleted
Why AX
Agents are a new kind of workload.
They are neither microservices nor batch jobs. They accumulate state, need strict isolation, call out to model APIs and tool servers, and can burn money in a loop if nobody is watching. AX gives you four small primitives that handle all of that declaratively.
How it works
Scales up to billions of tasks.
AX runs on top of Agent Substrate, a compute runtime designed from the ground up for massive density and fast stateful actor lifecycles.
Billions of tasks
Every task runs as a lightweight actor, allowing you to scale to billions of concurrent agent sessions per cluster without orchestrator limits.
Sub-second resumption
Idle agents waiting on model responses, external tool calls, or human responses are checkpointed, suspended, and brought back in under a second with zero cold-start delay.
Dense multiplexing
Dozens of tasks share worker resources, turning idle waiting time into spare compute capacity so you only pay when agents are actively thinking and running code.
Generative platform
Generative features built into the platform.
AX integrates generative AI directly into the platform. For example, if you want to set up a workspace just by explaining it in plain English, the environment is prepared automatically before your task starts.
task.yaml
apiVersion: ax.io/v1alpha1
kind: Task
metadata:
name: data-analysis
spec:
workspaces:
- name: python-env
goal: "Set up a Python 3 development environment"Generative workspaces
Describe what a ready environment looks like in plain English. AX hands that goal to an agent on first boot to install toolchains and verify dependencies.
Run anything and everything
Interactive coding agents, long-running agent servers, Jupyter notebooks, headless browser testing, and custom tool runtimes—you name it.
Perfect for research
Spin up massive number of reproducible sandboxes to collect trajectories, run reinforcement learning loops, and evaluate agents at scale.
For builders & researchers
Built to be the most friendly runtime for developers and researchers.
We want to make dealing with agentic infrastructure easier so you can focus on your work. AX is designed with an uncompromising focus on ergonomics, rapid iteration, and joyful workflows for both application developers and AI researchers.
We aim to keep the runtime minimal and lightweight, while tastefully adding the essential features everyone needs to build, evaluate, and scale agents.
About
Born from research, built for production.
AX was born at Google when agentic runtime systems research met frontier compute. Over years of building and operating agentic execution engines, teams across Google recognized that agentic workloads represent an entirely new computing paradigm: stateful, bursty, long-running actors that compute intensely for a minute and then wait for model responses, tool responses, or human approval. Traditional orchestrators built for stateless microservices or predictable batch jobs become cost-prohibitive when keeping idle sandboxes running, yet lack native support for sub-second suspend and resume.
Drawing on agentic runtime research from Google DeepMind alongside deep experience in large-scale isolation, resumption, and scheduling, AX is being built as an open, declarative control plane purpose-built for agent execution. It abstracts tasks, workspaces, network policies, and models into core primitives so developers and researchers can run massive fleets of agents without reinventing the underlying infrastructure. This project heavily relies on Agent Substrate but provides agentic abstractions and generative runtime components.