Declare an agentic task. AX runs it at scale.

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.