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Posted by blazarquasar 1 day ago

AX – Google’s Open Agentic Orchestrator(agentexecutor.io)
648 points | 296 commentspage 3
mmq 1 day ago|
We have built similar abstractions directly on top of Kubernetes [1]

I was looking at this project a couple of months ago, and I did not understand why not use Kubernetes instead of rebuilding the abstractions. The reason is that Kubernetes already provides other abstractions to run services and batch job, gang scheduling, gpu and other accelerators enabled workflow.

[1]: https://polyaxon.com/docs/sandboxes/overview/

prng2021 1 day ago||
Can someone clarify the use case for this? What's the benefit over this: https://openai.com/index/introducing-the-agents-api/
verdverm 1 day ago|
you can run it yourself, it's open source, you can use any harness (req. custom image), you can use any token vendor (config)
je42 1 day ago||
I am wondering how this is related to https://agent-sandbox.sigs.k8s.io/ ? Since there is also https://docs.cloud.google.com/kubernetes-engine/docs/concept...
jcw90210 1 day ago|
IIUC agent-sandbox and agent-substrate (the one ax builds upon) are similar. Agent-sandbox is more k8s-native, while agent-substrate is less so.

Personally I think that this kind of workload is better off not being tied too much into kubernetes. I've worked with crossplane and other controller who put a lot of load on the k8s-apiserver and etcd and can easily slow the whole machinery down / grind them to a halt.

btw, agent-substrate is in the process of being moved to CNCF: https://github.com/cncf/sandbox/issues/523

ahmedtd 22 hours ago||
Agent Substrate was built to provide a few (important) things over Agent Sandbox:

* More efficient usage of compute by timeslicing agents (Substrate Actors), which requires fast suspend and resume (using gVisor or cloud-hypervisor snapshots), as well as keeping the K8s control plane out of the critical path (so agents can't be stored as resources in the K8s database).

* Deep inspection of outgoing requests using an egress gateway

* Minimizing the exposure of credentials to unpredictable agent control (so they can't upload access tokens to pastebin).

Achieving those goals ultimately required a significantly different design from Agent Sandbox.

link89 11 hours ago||
Going with a Wasm-based approach is definitely the more lightweight and better option.
nullbio 1 day ago||
People can afford to run billions of concurrent agents?
dbmikus 1 day ago||
Not sure about billions, but companies doing evals or RL or training will create really big bursty agent workloads. I think they are the best fit for AX, as opposed to individual dev teams building software, etc.
agentdev001 1 day ago||
An example of this, Moonshot (kimi) open sourced this: https://kvcache-ai.github.io/AgentENV/latest/getting-started...
_zoltan_ 1 day ago||
billions? who is running BILLIONS of agents?

tens, hundreds, maybe a couple thousand at a time? absolutely.

yoz-y 1 day ago||
One thing I’d say, is that I find it progressively more interesting/fun to rollout your “everything”. I mean… hello security, but by the time I’ve read somebody’s documentation I’ve already halfway done making thing exactly how I want it.
sarjann 1 day ago||
> We want to make dealing with agentic infrastructure easier > Kubernetes

Pick one.

kundi 1 day ago||
Why kubernetes? Seems like an overload
prescriptivist 1 day ago||
Google already has gVisor running in Kubernetes as a product (GKE Sandbox), which provides the security guarantees necessary for secure sandboxes (regular k8s isn't great in this respect). They also have pod snapshots running at scale (which run on gVisor), so you can spin up process(es) and snapshot the memory and fs of a pod at a point in time, ship it to a blob in GCS, and then rehydrate those snapshots very quickly (or fork into new instances), which allows for the fast/cheap startup and suspend times and the instant scaling they advertise here. One of these snapshots can be created in one cluster and spun up in another.

Not sure if this is an extension of tech they already have had in their systems, but I've experimenting with it to build my own orchestrator and it's been a pretty neat set of tools and abstractions so far.

chrismarlow9 1 day ago|||
Future of platforms is operators in k8s to abstract the developer need to the underlying systems. On local it maps to kvm, on gke it maps to their stuff, on AWS to RDS. It's "interfaces" on a platform level so devs can just ask for a thing.

Overall I agree though, this is a bit of an abuse of that concept.

EDIT: I'm sure op is familiar with this workflow but I'm being overly verbose to clarify what I think they mean and my thoughts.

srcreigh 1 day ago|||
I can launch Astra to deploy changes to my homelab via creating Forgejo issues from my phone. That kind of system is pretty hard to set up without kubernetes.

Even if you confine yourself to a dev workstation, having 5 agents concurrently building testing deploying code makes your computer loud and/or hot.

somewhatrandom9 1 day ago||
Agree. Probably an unpopular opinion, but I strongly dislike YAML.

EDIT: if I HAD to use YAML, I'd prefer KYAML: https://dev.to/mechcloud_academy/goodbye-yaml-hell-meet-kyam...

surajrmal 1 day ago||
KYAML seems eerily close to json5.
DanMcInerney 1 day ago|
I really don't think any of these SOTA labs are doing agentic engineering correctly. Skills are the universal language of all agent harnesses. If you abstract the taste and prescription out of the skills and into guidance docs, then leave the skills as basically just workflow scaffolding, you can build task-specific workflows that work with any harness like Claude Code, Codex, Antigravity, etc. Technically, you only really need 2 skills, work and review, and with these you can build infinitely complex workflows including self-improving loops. I built this out and have been using it for months. It's been extremely nice. https://github.com/DanMcInerney/orchflows
cobolcomesback 1 day ago||
The OP is not really a workflow manager, it’s a workspace manager that facilitates creating controlled environments where your skills can run. Everything you said is compatible with (and complementary of) the OP project.

With that said, I’ll somewhat disagree with you. I’ve been down the path you’re talking about and while it is incredibly flexible and powerful, it became too difficult to maintain, and too inconsistent between workflow runs, and a pretty hefty waste of tokens to use AI on things that could instead be handled by deterministic scripts. I ended up creating an orchestrator for myself that uses skills as the primary way to tell agents how to execute a step in a workflow, but also directly orchestrates running scripts and managing state in a deterministic way rather than leaving it all up to agents.

handfuloflight 1 day ago|||
How does your criticism relate to the specifics of what OP posted? https://github.com/google/ax/blob/main/docs/concepts.md#work... This says it has skill registries.
DanMcInerney 1 day ago||
Overly complex; yaml files, heavy framework. Same mistake as Claude Code's Dynamic Workflows. Why not just use the dehydrated skills as the workflow skeleton and use custom guidance docs to hydrate the skills with taste and preference depending on the domain of the task? Now you can build a library of small workflows that compose into larger workflow, and you can export any workflow as a single skill to be used in other harnesses. For example, I have a code.md. It's really small, just a bit of taste preference. If I'm using it to hydrate orch-work for coding tasks, then maybe I want to create a code.api.md which hydrates for further specificity if the task is about creating APIs. Then when new models come out, I can just delete code.api.md and leave it as code.md for /orch-work to read from within a workflow because newer models won't need as much prescription.
verdverm 1 day ago||
Part of what's happening is this is running on Kubernetes, which is oft described as "Overly complex; yaml files, heavy framework" but has value regardless, as perceived by being an industry standard. All the things you describe are well and good, but do not address how one runs many of them reliably (from an infra stand point)
nl 1 day ago||
You really, really need different skills depending on the model.

If you are using Qwen 27B you need very prescriptive skills.

If you are using Astra you usually want very minimal skills (because it will follow them but be unnecessarily constrained in some contexts)

If you are using Fable then it depends - it will take the skills as general guidelines but ignore them a lot more than Astra does. Sometimes this is good, sometimes not at all.

DanMcInerney 1 day ago||
Right. That's what modular guidance documentation is for. You could have astra.code or qwen.code.api. All reusable in different workflows. Prescription doesn't belong in the skill itself.
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