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Update README based on the new controller implementation (#213)

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Jaana Dogan
2026-06-29 10:19:08 -07:00
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@@ -13,17 +13,17 @@
> widely soon. If you are interested in collaborating with us,
> please reach out to **ax-dev@google.com**!
AX, short for Agent eXecutor, is a distributed agent runtime. It provides a
runtime that coordinates agentic loops, manages executions with event logging,
and communicates with both local and remote actors.
AX, short for Agent Executor, is a distributed harness runtime.
It dynamically provisions isolated environments from suspendable/resumable
images to execute harnesses and agents.
AX is designed for reliability, with native support for recovery
and execution resumption, even in complex distributed setups.
and execution resumption, even in distributed setups.
## Features
- **Distributed Runtime**: Controller, skills, tools, and agents can execute in isolation
- **Distributed Runtime**: Harnesses, skills, tools, and agents can execute in isolation
- **Resumption**: Automatic recovery from failures or interruptions
- **Skills, Tools, Agents**: Support for skill, tool, and agent selection and execution
- **Built-in Harnesses**: Support for frontier harnesses and custom implementations
- **Auditing & Policy**: All user and agentic calls are coordinated by a common controller, easy to control and audit the overall execution and skill/tool/agent calls
- **Portability**: Runs anywhere, scales to small and large deployments
- **Customizability**: Agnostic of harness and model
@@ -45,15 +45,27 @@ Watch our demo to see AX works when deployed on [Agent Substrate](https://github
%%{init: {"flowchart": {"diagramPadding": 80}}}%%
graph LR
Client
Router["Router"]
Controller["AX Controller<br/>(executor, event log, registry)"]
Tool["Tool<br/>(MCP server)"]
Env["Environment with<br/>skills, built-in tools<br/>(isolated actor)"]
Client -->|resumable stream| Router
Router --> Controller
Controller --> Env
Controller --> Tool
subgraph Cluster[" "]
Server["AX Server<br/>(multi-tenant)"]
DB[("Event Log"<br/>Storage)]
ControlService["Control API"]
Actor["Harness Actor<br/>(stateful session-tenant)"]
end
SnapshotService["Snapshots"]
HarnessService["Harness or model<br/>service"]
MCPServer["MCP server"]
Client <-->|resumable stream| Server
Server <-->|scan/append| DB
Server --> ControlService
ControlService -->|resume/suspend| Actor
Server <-->|resumable stream| Actor
ControlService <-->|read/write| SnapshotService
Actor -.-> HarnessService
Actor --> MCPServer
Actor -.-> Environment
```
As agents evolve from simple assistants to autonomous long running workers,
@@ -66,11 +78,13 @@ workers becomes a necessity. AX provides the foundational layer to fill these ga
While compute-agnostic, AX is aiming to provide the best
experience on Kubernetes.
We expect every sophisticated agentic application will need the capabilities provided by AX.
We expect every sophisticated agentic application will need the
capabilities provided by AX.
We are building this layer as a widely available foundation,
enabling developers to focus on building their applications rather than infrastructure.
We decided to build this project in public to validate every design decision before
a stable release is cut. We highly encourage you to give us feedback.
enabling developers to focus on building their applications rather
than infrastructure. We decided to build this project in public to
validate every design decision before a stable release is cut.
We highly encourage you to give us feedback.
## Installation
@@ -97,7 +111,6 @@ AX is natively supported on
on Kubernetes and it's the recommended deployment option for production
use. For more details on setup and configuration, see the
[deployment guide](./manifests/README.md).
Read more about [this new layer](https://cloud.google.com/blog/products/containers-kubernetes/bringing-you-agent-sandbox-on-gke-and-agent-substrate)
that provides higher density to agentic workloads on Kubernetes.
@@ -249,11 +262,26 @@ export GOOGLE_GENAI_USE_VERTEXAI=True
## Extensions
### Harnesses
AX provides built-in harnesses (e.g. Antigravity) but you can bring your
own harness implementation by implementing `HarnessService`. On supported
compute services (e.g. Agent Substrate), AX automatically runs the
harness in isolation with automatic resumption and suspension.
Traditional agents (e.g. tool use or workflow agents), or
language models can be implemented as harnesses.
### Skills
AX harnesses like Antigravity includes built-in support for
Built-in harnesses like Antigravity includes built-in support for
Agent Skills. See [Skills](examples/skills) for more.
### MCP Tools
Built-in harnesses like Antigravity provides support for discovering
and making calls to MCP tools when they are configured.
## What AX is NOT?
* A managed service. AX is self-hosted and not a managed service.
We aim to make it easy for users to deploy and operate it on
@@ -270,12 +298,14 @@ Agent Skills. See [Skills](examples/skills) for more.
Below is an overview of our upcoming features and planned changes:
1. Antigravity as the built-in harness
1. Support for more frontier harnesses besides Antigravity
1. Support for BYOH (Bring Your Own Harness)
1. Support for tool call approvals from harnesses
1. Improvements to resumption protocols
1. Forking from event log and snapshots
1. Trajectory exposition
1. Better telemetry exposition
1. Integrations for policy, auditing, and more
## Contributing
@@ -286,6 +316,41 @@ We are currently undergoing a significant architectural redesign, and external c
However, in the meantime, we warmly encourage you to file bugs and
send feature requests.
## History
Over the years, teams across Google built and operated several
distributed execution engines. As these systems evolved, certain
architectural patterns consistently stood the test of time.
The teams realized they were repeatedly solving similar
orchestration problems, prompting the push to extract these
lessons into common runtime layer.
While this common layer was taking shape, the AI landscape underwent
a massive shift. Applications were transitioning from
statless tool use agents to autonomous, long-running, self improving
agents that often need isolated resumable execution environments.
Also, from an efficiency standpoint, agentic workloads are inherently bursty.
An agent might compute intensively for a minute, then sit idle for
hours or days awaiting human approval. Keeping a stateful actor active
during these long idle periods is highly inefficient and cost-prohibitive
at scale.
Over the last 10 years, Kubernetes has become the standard for
large scale job orchestration, but it was fundamentally designed for
stateless microservices or predictable batch jobs --
not for suspending and resuming stateful, sandboxed agent actors.
Driven by these dual challenges, we decided to build a robust, common
agentic orchestrator designed specifically for the new compute
layers we are developing on Kubernetes. Our goal is to ease
the productionization of agents, allowing developers
and researchers to focus on building and evaluating their
applications rather than dealing with underlying infrastructure.
AX is developed and maintained by the team actively working on
Google's internal runtime. Although the two projects operate
at different layers today, we are applying our knowledge
and insights to AX in the public domain every day.
## Acknowledgements
We thank Google DeepMind for their earlier work in distributed harnesses which