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
The reality with releases like this is that I'm 90% sure most Google bigwigs have never heard of it, and it's misleading to label it as "Google's" in the title.
Yes, it was developed by Google employees, that does not imply it has the full backing of Google, or Deepmind, or GCP. Notably, the website doesn't seem to claim this either.
I have no insider knowledge but https://x.com/rakyll is working on it and she is tweeting about it and I got the impression there is a quite a team behind it. It looks like an effort in GCP.
It depends on where they decide to go with it, I guess.
Kubernetes, Go, Tensorflow, Chromium, gRPC are some examples that obviously went incredibly well.
I'm not sure why, exactly. But I don't pay any attention to news like this from Google. I don't know if there's some marketing which has me writing them off or if it's something else.
What I do know is that the Gemini integration into sheets is surprisingly incapable of performing basic tasks. This is where I expect Google to really shine. I expected Sheets + Gemini to be magical like Google Photos was. I hardly try anymore besides some basic math questions when I don't feel like inputting the formula myself.
The other thing I know is Google's propensity to sunset products. For many things, it's not a huge deal. And it may not be for this. But, why? When there are alternatives - both open and closed.
On your Sheets + Gemini integration point, I've genuinely tried to give the Gemini integration into Google Docs & Google Sheets a chance. It is so incompetent that it is fully useless to me. I have not gotten a single correct solution each time I tried to use it, even something I consider table stakes. I often write my work reports in Vim in Markdown format, but they need to go to the corporate Google space. No matter how hard I tried, no matter how many prompts I have, it was completely unable to manage the command to "convert the Markdown format markers into native Google Docs markers". And I want to note, this was 2 pages of extremely simple Markdown with no "advanced" patterns, like tables or quotes, I think all I used was heading-marks, bolding, italicizing, and code blocks. This is something I would expect even GPT 3.5 to succeed in, and even more so Luna, but somehow it destroyed the formatting throughout half the document. This leads me to believe that they apply the absolute cheapest model they have there, or they have the model a harness which can barely be considered working.
I found it absurd when I found out that they suddenly made this Gemini integration an additional paid plan recently, there's absolutely no way I can consider that in good faith.
I'd evaluated both Google's Agent Substrate (that underlies Ax) and their Scion project. I really enjoy how Scion operates with existing tools really well. Ax/Agent Substrate is much more a greenfield independent effort, it's own thing.
I think Scion has so much more mature a disosition: you could write OpenCode plugins that enhance the runner, and use that locally, and use it in Scion. With Ax/Agent Substrate, you are opting in to a pretty huge stack that is just Agent Substrate, that is their runners, their harness, their substrate. I do think their actor model is pretty neat! It's neat having the agent have such primacy! But it feels so much less integrative, is such it's own thing. Scion, to me, is much more interesting an effort, that similarly helps scale out agentic workloads.
> you are opting in to a pretty huge stack that is just Agent Substrate, that is their runners, their harness, their substrate
The website makes me think the contrary: It is described as “low opinion” and explicitly mentions that the running tasks don’t even have to be AI agents. Can you explain in what ways you’re more locked in than the website suggests?
Scion at the same time talks much more about concrete agents, giving me the opposite initial impression.
> You need a Kubernetes cluster, ko (brew install ko), a container registry your cluster can pull from, and a reachable Agent Substrate Control API (in-cluster default: api.ate-system.svc.cluster.local:443).
> make deploy AX_IMAGE_REPO=<your-registry>
> This deploys Redis, then builds and deploys the control plane images with ko. Everything lands in the ax-system namespace.
Yes, it was developed by Google employees, that does not imply it has the full backing of Google, or Deepmind, or GCP. Notably, the website doesn't seem to claim this either.
Genuinely not knowledgeable here
https://grapheneos.social/@GrapheneOS/117282080803799576
> Google should not be gatekeeping security patches to the standard Android platform code from Android OEMs but that's what they've started doing.
Would be about time we get benchmarks for these ... so these can also be gamified just like with the LLMs.
What I do know is that the Gemini integration into sheets is surprisingly incapable of performing basic tasks. This is where I expect Google to really shine. I expected Sheets + Gemini to be magical like Google Photos was. I hardly try anymore besides some basic math questions when I don't feel like inputting the formula myself.
The other thing I know is Google's propensity to sunset products. For many things, it's not a huge deal. And it may not be for this. But, why? When there are alternatives - both open and closed.
I think Scion has so much more mature a disosition: you could write OpenCode plugins that enhance the runner, and use that locally, and use it in Scion. With Ax/Agent Substrate, you are opting in to a pretty huge stack that is just Agent Substrate, that is their runners, their harness, their substrate. I do think their actor model is pretty neat! It's neat having the agent have such primacy! But it feels so much less integrative, is such it's own thing. Scion, to me, is much more interesting an effort, that similarly helps scale out agentic workloads.
https://github.com/googlecloudplatform/scion
The website makes me think the contrary: It is described as “low opinion” and explicitly mentions that the running tasks don’t even have to be AI agents. Can you explain in what ways you’re more locked in than the website suggests?
Scion at the same time talks much more about concrete agents, giving me the opposite initial impression.
> You need a Kubernetes cluster, ko (brew install ko), a container registry your cluster can pull from, and a reachable Agent Substrate Control API (in-cluster default: api.ate-system.svc.cluster.local:443).
> make deploy AX_IMAGE_REPO=<your-registry>
> This deploys Redis, then builds and deploys the control plane images with ko. Everything lands in the ax-system namespace.