> 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.
On the the other hand, the readme quickstart section says
> 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).
Call me old-fashioned but I don't find this "easier". Maybe it's easier in the same way that Kubernetes itself is easier than managing VMs and container deployments at massive scale without such a tool. But there's a vast chasm between what this tool is being sold as and what it actually is.
I don't really like the oversubscription of agent pods though, as you can no longer trust the k8s pod identity as being from a singular workload. Haven't seen a solution to this for ax yet and it is a barrier to adoption for us.
lmao found the dev who's only ever worked on the dev side of things.
I'd prefer a K8s Operator or plugin (like Istio) to get all the pack.
Google operates at such a scale with a wide surface area of serious production considerations that even “ergonomic” solutions internally feel extremely heavyweight externally.
Source: I’m an Xoogler
I've been still just like, making VM's with proxmox, then putting my agent in the machine and letting it run free (with my dotfiles setup script making dev env pretty much free, though I could also just make a VM snapshot). What's wrong with that? Is that not the scalable solution for enterprise rn?
While I feel like I have a decent understanding of the model landscape I'm feeling a bit lost at which agentic harness to leverage for local models. Hermes, Cline, Aider, Qwen Code, Goose, Pi, OpenCode, something else? I live in the terminal so Desktop UX is a bonus but not a must have.
Can I modify the antigravity settings/program to point to a local model? Where should I spend my energy?
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.
>A Model is not a model. It is a named model configuration: ...
Remember kids, a model is not a model.
I'm planning to buy a whole linux mini-PC to run my agents/code servers for more isolation. Codex/Claude Code let you run prompts on code over ssh (same with most IDEs) even on the desktop apps.
I wonder if that's going to be the new standard practice. You get a work laptop and an isolated agent box.
Running access control and network whitelists is always a maintenance challenge and it's easy to make mistakes.
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.
If an agent is treated like nothing but a call to an external service (...which it is), everything fits in the existing programming paradigms. But I guess that's not very exciting. Only pragmatic.
The project seems like an open-source initiative born out of the experience of some Googlers but not being used at Google. So, the title appears a bit misleading - people will be misled.
Genuinely not knowledgeable here
Next we also need an instruction style guide and CoC. It's important to treat your agents with respect. I almost forgot, the YAML template meta-language to YAML the YAML. Then we will need a foundation employing 12 FTEs to maintain it all and of course to run the certification process. You are certified, right? Statistics show a 10x increased chance of an agent going rogue and hacking competitors if it has been mistreated or been run in an unvalidated sandbox. It goes without saying the sandbox certification process is separate and must be repeated yearly by a trusted third party auditing company.
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.
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For a generic swarm, workflows aren't too useful which does away with the visibility, so I may give this a try instead.
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https://googlecloudplatform.github.io/scion/overview/
Scion wraps the harnesses (9x) we all use every day and is closer to OpenClaw on Kubernetes
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.
Because it's using `ax apple` instead of `kubectl apply` you can't use tools like argocd for managing resources.
It's built on agent substrate which is built on top of k8s CRDs, so I'm surprised.
Is the throughput of these objects too high for etcd?
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
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.
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Pick one.
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https://github.com/agent-substrate/substrate
(For context I built something very similar to this the past 2 weeks for my homelab, trying to solve many of these problems. This comment is an edited version of an unreleased blog post I wrote last week.)
- Run code in secure microVMs or gVisor. Docker is not good enough. Qemu is not good enough. A secure environment for running untrusted code is the bare minimum. I don't see Firecracker in the repo yet, but that's ok the idea is there.
- Fast resumption. In my homelab, time-to-first-message is around 11-12 seconds. That's half setting up the pod, and half resuming the CLI (e.g. `codex resume ..`). Why resuming? In my homelab agents are commonly blocked waiting for CI or waiting for me to approve an action, in this case I stop their container to keep resource usage low. Then for resumption, you definitely don't want to waste the agents time by giving a new ephemeral disk and forcing them to re-clone and re-build. For microVMs this is not actually straightforward, for example Firecracker only allows block devices, so re-attaching an agents disk workspace requires a custom storage interface
- Zero Trust. Codex CLI permissions for example are extremely broken. "Can I run this 500 line long command? or allow any command starting with first 100 chars always?" More reasonable grants are needed.
I don't understand yet how they will surface Zero Trust notifications. In my homelab it's a Forgejo comment linking to an auth service, and a ntfy.sh iOS notification which opens up the auth service.
I don't get why they to restore the RAM of the agent env. Maybe to fully optimize resumption. Idk, I don't have that much RAM in my homelab, my agents use a ton, testing stuff in Chromium making screenshots for me. I can't keep RAM for 100 workspaces from the past 24 hours in RAM.
MITM gateway is very cool.
I'm curious how they will integrate with microVMs. I just wrote yesterday[1] about how there are NO GOOD OPTIONS for this atm. Kata is decent but the attack surface it introduces makes me uncomfortable.
[1]: https://srcreigh.ca/posts/auditable-kata/
But anyway, even if this project is abandoned out of the gate by Google, we should be happy, it sets the bar where it should be. I'm excited to learn how they solved these problems differently than I did.
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Would be about time we get benchmarks for these ... so these can also be gamified just like with the LLMs.
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Essentially there is no out of the box solution about orchestrating agents and increasing LLMs sandboxing. That's why everyone and their grandmother are re-inventing the wheel.
At the same time, it's an incredibly complicated problem, with a variable perimeter (OS support, sandboxing primitives support).
I am quite happy about my own solution (because it supports my use case!) but I hope something with a decent dev UX will appear one day. AX definitely is NOT.
You need a Kubernetes cluster"
LOL. Bye!
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What I don't see enough of is observability that's useful after the run — not just the final trace, but per-step latency, token cost, and which tool calls actually contributed to the answer. That's where the gap between "demo on the README" and "runs unattended for a week" usually lives.
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(This is work in flight, but it will land within a few weeks)
I believe the substrate egress-gateway needs to handover the internal SPIFFE one to an external system (e.g. Entra Agent ID). Not sure if that should be part of substrate or kagent/ax/..
That said, I think Google's ADK ecosystem and this new AX platform is promising--I would expect Google to maintain this and other tooling around this for years to come.
To the Googlers out there: is Google using this at any capacity for internal projects?
The same Google that pulls plugs on a whim?
btw its the same google that has already killed its "gemini cli" and re-introduced it in the form of "antigravity cli"
Do you see what you wrote?
guilty as charged
"Gosh, that Italian family at the next table sure is quiet"
First time?
IMO, you want the flexibility to create either: (a) permanent devbox VMs, and (b) per-task VMs
Agent sandbox platforms tend to be tuned for the latter, which sometimes involves VMM hackery for fast boot, snapshotting VM filesystem and RAM, etc.
Some workflows are a lot simpler if the multiple agents share a VM. These are workflows where agents must share state. A simple one we have: making related changes in our public OSS repo and our private repo, and then testing the change.
And other times you want to split up the tasks onto isolated VMs so they don't interfere with each other (ie run two dev servers without database or port collisions).
I tweeted a bit about this (https://x.com/dbmikus/status/2099264325231771878) and had a little debate with folks about ephemeral vs persistent VMs for agents
That said, most of the time, I only want an agent to work in one repo. I could give it multiple repos at once and instruct it to work in just one, but that risks it forgetting my instructions and it can load more context into the agent's window.
I think my main point was to have flexibility about the topology of VMs, repos, and agents.
Now the stuff people are coming up with is: how do you do authorization in this model? do you need a full sandbox all the time or can it be a workflow? how do you specify an agent is it a prompt or does it have some kind of control flow structure? How do you coordinate among many running agents?
I would say thats where we are now is there’s loads of people all solving the same problems a bit like when CoreOS, Kube etc. were all competing.
Code execution (usually TS/JS or Python) is useful most when you are dealing with truly open ended problems. It's the opposite of the use cases of most enterprise SaaS.
What's the actual realistic threat model for median developer or median user here?
By realistic, I mean that leaking your grandma's recipes or your SSN or your million dollar idea to some pastebin is neither likely nor going to meaningfully make things worse for you, or be useful for any malicious actor. Surely this is not what everyone is worried about?
I've been working on https://lullabot.github.io/sandbar/latest/ which works with Proxmox for VMs (and lima for locals or regular linux hosts over ssh). There's a diagram in https://lullabot.github.io/sandbar/latest/why/#recommended-w... with what we're currently recommending. Though, after some feedback, I'm in the process of integrating a colleague's web-based review tool as it turns out many preferred fully reviewing locally instead of using draft PRs.
It's got some opinions in terms of default tools for our team and industry so it may not fit yours. Forgive some of the AI-isms in the docs, I want to get the UX and feature set to a solid place before doing a full review.
Yes, but, wrong layer here. Giving the agent a computer use (a la bash) is what folks are after. A temporary sandbox with lots of control knobs and security bits is how you do that in (as you noted) an enterprise.
A little git log, show, diff almost always in another terminal.
A customer of mine wants to standardize his developers on a Jetbrains IDE for python but I think that he is late by one year. Furthermore he is using the subsidized plans for Claude, not paying for token, so it makes sense to keep using the Claude TUI.
Not sure what all the other folks are doing, but the industry/ecosystem tends to over-engineer every single thing instead of just working on the thing, while I just want proper code, proper design/architecture, and proper high-quality results.
The problem is that you'll end up wanting to run 2 or 3 (or 20, 100, 10,000) agents at once and that gets very hard with a single VM.
There's also an argument that you should be using a separate sandbox for each code operation a LLM performs (or at least each set of related operations). That's even harder to do with conventional VMs.
VMs are better for personal assistant work, GUI clicktesting, investigating bugs in your personal dogfooding dev instance and anything you haven‘t yet made repeatable and fast to set up.
Sandboxes are better when you need resource isolation or security and have a graph of tasks to work through. My agents often starve each other on one VM, so if they don‘t need any of the above it‘s just easier to isolate them.
Everyone is working in this area, including me [0], but either option really isn‘t that convenient to use yet. It‘s a bit of a „isn‘t Dropbox just FTP on a VM“ moment right now.
Since this is not the first mention of Dropbox I've seen in HN threads in the last 48 hours:
Let's not forget that Dropbox was at its best when it was "just" a streamlined ftpd over sshfs or whatever - when it was just "a folder that syncs". That didn't last long, the downfall started with them killing their most useful accidental feature[0], which started them on a path of enshittification[1], which they followed swiftly and diligently into complete irrelevancy they enjoy today.
So if the agentic tooling is now enjoying its "Dropbox moment", I implore people working on these tools, don't overdo it.
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[0] - The "Public" folder initially supported direct linking, meaning you could publish static web sites by simply putting them in Dropbox/Public/, you could update the files there and changes were immediately "live". Notably, this was the heyday of phpBB and similar discussion boards, back between the rise and subsequent fall of free image hosting - so the ability to put images in your Dropbox/Public/ and hotlink them in a discussion was extremely useful and popular way to use the service.
[1] - They didn't just kill direct links, they replaced them with what I consider to be OG enshittification pattern - captive page that asks you to press a button to download. Yes, same one every "synced drive" service offers now, to enable various functionality that's 99% harmful to the user with the link.
Either way, this was the peak of Dropbox; after shutting down direct Public/ links, it was still useful for its main job as seamless cross-machine, cross-platform "folder that syncs", but gradually lost market share as OneDrive and Google Drive became more broadly useful (and had the advantage of being first-party on their respective platforms), and then Dropbox the company itself lost focus and tried a bunch of failed pivots in the direction towards cloudification, away from "just syncing files".
End result for end users? We now have zero options for bullshit-free, file-first, seamless "folder that syncs" experience for non-tech users (techies that like fiddling with things have Syncthing). Only cloud-first options remain, and they're full of footguns and enshittified to the core (which becomes apparent the moment you want to share a file outside of the vendor's cloud ecosystem).
I outgrew this when I wanted to bring different sets of skills and templates to different machines, wanted to be able to share a small number of credentials, different agents in different machines, different egress rules etc. I wrote https://github.com/pjlsergeant/byre which gives you a TUI and some machinery for doing this easily on top of Docker or Podman.
We started building that but it quickly turned out to be too narrow. Often we want agents to do task that have no input ticket and often the output is not a code change (Slack bot, incident investigatior, scheduled daily tasks, ...)
No, giving the agent access to every single command on the system is not minimalist. It is actively detrimental if you want to do more than just attended coding with the agent.
Nothing at all. You'll know when you've outgrown it.
> what the general workflow is now that people are converging to?
Graph-based workflows where agents pick up work as it becomes available, structured output, while you manage the work queue and outcomes. Maybe? IDK really, it's all moving quite fast.
Where are the revolutionary software products?
Revolutionary products depends on revolutionary ideas, not faster execution.
Works pretty well for me but I haven't put any effort into promoting it
- Do multiple tasks in the same context window / session, conflate different changes into the same prompt
- Repo mixed with old markdown files from previous tasks, excel and word docs and 300 playwright screenshots
- 5 tools all calling each other, test and deployment scripts are all markdown skills
Personally I prefer a ticketing system and isolated work trees
Probably we'll converge on a virtualised IO / Storage layer running microVMs beneath for isolation and security. Keep the network and storage layer separate for compatibility running a variety of stuff and a second security boundary.
As a test, I built a sandbox with only the host-side filtering proxy allowed for networking. 99% of traffic was HTTP. No QUIC at all.
npm, pip, apt, go, curl and git-over-HTTPS all worked on the standard proxy environment variables alone. No mirrors or other coaxing needed.
DNS is disallowed through the chokepoint, but that's no problem because the proxy resolves host-side anyway.
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Only complaint is that connecting the agent harness to my local model took more work getting configured right than I'd like, but that's been true of most harnesses I've tried as well. Most assume you're using a cloud model and local model configuration is a bit of an afterthought.
In case of omp, not sure if it's already at the node module package but you can just grab it from the links I shared and set it up.
* commands run by me (! prefix) are also sandbox blocked
* agent has no way to _request_ unsandboxed execution (e.g. if `kubectl whatever` is rejected by the sandbox, the model should have the chance to request permission)
* does not understand shell composition patterns (e.g. if `git status` is allowed and `git log` is allowed, then `git status && git log` should be allowed automatically)
* sandbox only supported on mac or linux. not both
All of that can be fixed by yourself. That's certainly the spirit of pi. But if you want strong defaults and batteries included (like omp promises) then that's just annoying.
- instruct model to write a markdown file with a phased plan to implement whatever feature or change I want
- start a new context, instruct model to implement one phase of the file
- review changes manually, then start a new context and have it do the next phase
- repeat as needed
I've never seen omp touch a file outside of the directory I start it up in, and the few times where I've been unhappy with a change git has been there to revert.
This could easily be a case of survivor bias but I've not had an issue with letting it go yolo yet.
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I'm not a fan of open-core apps.
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Always Pi.
A random example E.g https://github.com/google/filament#disclaimer
This is not an officially supported Google product.
https://cloud.google.com/blog/products/ai-machine-learning/a...
"Effort in GCP" is a red flag. (See Gemini CLI, which was shut down in favor of Antigravity CLI.)
AX is a layer that is closer to job orchestration, Agent Substrate. It's NOT an agentic framework. We use Antigravity for a few generative features but are abstracting away some of these components so anyone can bring their own. AX is trying to solve some tedious things everyone has to deal with. Layering execution with the underlying stateful worker, wiring up the network correctly, providing sub "task" identity, provisioning the right environment, proving stateful branching, auto discovery.
We want to keep Agent Substrate free of generative features and still need a layer above for some integrations that require agentic concepts and identity.
Google compute services are under GCP and I work on Kubernetes. So the comparison with Gemini CLI is not relevant here.
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Google has around 200,000 employees. They probably haven't heard of most things Google releases.
> This is not an officially supported Google product. This project is not eligible for the Google Open Source Software Vulnerability Rewards Program.
We have used a few of Google's (smaller) open source projects, and in the last 2-3 years most of them are getting fewer updates if any updates at all. Some became very bad tech debt and we had to spend a lot of time migrating them.
Of course, that is the nature of open source projects (written in the license terms), and there is nothing to complain. But it's important to point out these days Google's open source project are not any more trustworthy than a one man's project in terms of support and maintainability. Personally I would stay away from them as far as possible. Especially if you look at what happened to Android, Gemini CLI etc.
(To be honest, even if it were officially supported by Google, that barely means anything. https://killedbygoogle.com/)
We met quite a large number of customers in the last few months, and several teams inside Google, who are asking for a stack that runs on any cluster.
There are several reasons for a layer like this.
(a) Today, it's extremely hard to deliver large agentic applications to someone else's compute, and it kills adoption for products that need to run closer on customer data plane, e.g. large scale code scanning product.
(b) It's tedious to build systems that set up the environment, deal with networking policies, provide integrations for session storage, memory curation, skills retrieval and mounting, etc while all you want to do is to focus on building applications.
(c) People want a truly transparent stack for compliance/auditing, and all other non functional capabilities most people don't usually think about until they have to.
The web page presents ax as a typical developer tool, but it's actually not for developers.
I think you could get by with 1 computer, but it’ll have to have a pretty decent machine.
Between agents running tests, CI, docker image builds, an average $400 mini PC won’t cut it.
Don’t forget also many older mini PCs don’t support KVM. Some newer ones don’t support AVX/ mongodb.
It’s not so easy to buy any old hardware sadly.
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A standalone machine is nice if you need more compute resources or if you want an always-on machine you can connect to from your laptop, phone, etc.
It doesn't look like Google's AX is quite the plug-and-play fit for running agents on a computer you own, since it requires setting up a K8S cluster, etc.
I think what's needed is something like a zero-setup combo of Tailscale and Firecracker
I'm trying to work towards that with my startup (https://github.com/gofixpoint/amika) but the bring-your-own-computer part doesn't work quite yet.
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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.
What's nice about Scion is that it runs existing systems. It runs Claude, it runs Code, it runs Pi, it runs OpenCode. By contrast, "low opinion" means build something new, from scratch, atop this brand new platform.
Note that both of these are designed to work at some scale. Agent Substrate specifically is somewhat coupled to Kubernetes, is my impression, but honestly that's fine with me. Scion can run on Docker, Podman, Apple Container, Kubernetes, or Cloud Run. It's good that we be able to run these relatively quickly, but (especially with LLM assistance) the idea of running some substantial dependencies / services to run these things does not seem like a bad thing. If anything, I'd prefer having some well known services underfoot to these all being recreated afresh.
> 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.
In addition to the ones you listed, I'd add the V8 runtime, Jax, Protobuf. Even some of their projects that wound up declining in market share (Angular, Tensorflow--both losing share to projects that wound up at Meta, ironically) are still actively maintained and pushed.
But I'm sure there's also a huge graveyard of open source projects they abandoned that just never hit my radar. Still, at least with their open source stuff, you can fork in the worst case.
or Android Open Source Project?
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.
Then put a “sandbox” around these agents, that word has many meanings. In this case they fence the network traffic, so likely some kind of allowlist for network requests so that the agent doesn’t exfil crap to random websites. They also limit the resource limits of the sandbox, so that is beneficial to the cost of running these agents.
must look cool for people who want to solve every problem with k8s
it starts with interesting misnomers like "Task" which is not a work item but a sandbox.
"billions of tasks" is a "solution" to problem nobody has (maybe some RL labs? but they solve it other way and with orders of magnitude better optimizations).
freezes design too early – unless they'll actually focus on developing it and make tons of breaking changes it looks shit.
shared state in the same workspace, identity, authority, etc – stuff like that needs to be solved
After about 3 messages in any given chat, a follow-up to "rewrite that with a more friendly tone" will result in a letter for a completely different job from another chat within the notebook.
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Yes (perhaps not etcd, but the combination of kube-apiserver and etcd)
Substrate does not use CRDs for anything on the hot path of actor scheduling or resumption.
* 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.
tens, hundreds, maybe a couple thousand at a time? absolutely.
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.
Even if you confine yourself to a dev workstation, having 5 agents concurrently building testing deploying code makes your computer loud and/or hot.
EDIT: if I HAD to use YAML, I'd prefer KYAML: https://dev.to/mechcloud_academy/goodbye-yaml-hell-meet-kyam...
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.
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.
But for most things, I find resuming with memory is more trouble than it's worth. If you always resume from memory, you lose the ability to control the state of a VM. It's much easier to define which services should run than to define which active RAM state should be purged
Similar to why "did you try turning it on and off again?" is good for system reliability.
This is going to be decomposed. I believe the plan is to offer resumption with disk state only as well (and the RAM snapshots will need to be discarded from time to time, if you update the underlying code of the agent, or switch CPU types).
By the way, the RAM snapshots are not kept in RAM, they are serialized to disk, or uploaded to object storage.
For further isolation, I like to use nono inside a smolvm instance.
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Gmail for Your Domain/Google Apps for Your Domain/Google Apps/Google Apps Premier Edition/Google Apps for Business/Google Apps for Work/G Suite/Google Workspace
I used two agents: One with network access to collect the evidence, and one with everything except the model endpoint cut off, which did the analysis.
The second agent's entire input was attacker-authored. So PHP droppers, obfuscated loaders, database rows, filenames, blah blah.
In this case I'm more worried about hostile input attacking the agent, and I need to contain the damage. My sandboxing solution does that by restricting access to the source data, making it read-only. The work dir can only transfer data via patch and apply (like a git workflow), so even my workspace can't be modified until I approve each change. And then restricted network means that any compromise ain't going noplace.
The second agent couldn't even install PHP or contact any CVE site to check if it was looking at a known attack, and that was by design. All it could do is write up a report about what it observed, not make assumptions about what it is. I could then take its (much smaller) clean output and pass that to a third agent with network access.
This is forensic work, so of course not your median dev's bread & butter. But the attack surface is only just starting to be plumbed. Compromising input can turn your agent into their agent, planting things as easily as planting worms was back in the early internet days when people connected without a firewall.
You only really need to sandbox when you provide access to tools that are almost impossible to filter correctly, such as a bash tool or a tool for arbitrary code execution.
Especially with autonomous agents, it's the only way to sanity.
We might need new OS abstractions.
I think that if they're unable to sell their idea well enough to find a co-founder odds are the idea would die on the vine whether or not they're able to implement it.
Plant many flowers, keep the ones that bloom and stop watering the ones that don't.
And anyone who bothers to just do a side-by-side feature comparison can immediately see how many features antigravity is still missing compared to Gemini CLI even today.
I wouldn’t dismiss smolvm so fast. It brings together many ideas that make the whole very interesting.
https://blog.trailofbits.com/2026/08/26/vms-wont-contain-cyb...
Why? Firecracker mounts very few host systems into the VM, exposing minimal host code to malicious guests. Qemu and smolvm expose much more.
So yeah, smolvm is more like a docker or qemu alternative, definitely useful but NOT relevant to the discussion of sandboxing malicious code
It’s better than Docker, but it can’t be compared to Firecracker at all. Firecracker actually minimizes the attack surface whereas smolvm does not