Having said that, some components need to live outside the sandbox (otherwise, who creates the sandbox?). Longer term, I see it as a dedicated security layer, not part of the harness. This probably has yet to emerge fully but it's more like a hypervisor type layer that sits outside of everything and authorises access based on context, human user, etc and can apply policy including mediate the human intervention for decision points when needed.
Having the harness in one VM, and tool use applied to user data in another, is about as safe as you can be at present. You can mount filesystem fragments from the data VM into the harness VM, but tool execution remains painful.
Having all authorisation and access control exist outside of the harness layer is essential. It should only have narrowly scoped and time limited credentials that are bound to its IP, and even then that is problematic.
"Lethal trifecta" is basically describing phishing but in a way more palatable to people who would rather die before allowing themselves to anthropomorphize LLMs even a little bit. It's not a problem you can fix with better coding, like some SQL injection. You can only manage risk around it (for which sandboxing is one of many solutions that can help).
So on one hand, I agree with you - you need to be mindful of what you're actually dealing with. On the other hand, you always have this, and need this, for the agent to be able to do anything useful.
I should have made it more clear that the article is about agent / harness building (not about running third party agents).
> I barely trust the harness more than the LLM
Since we built it, I trust it just as much as I trust our API server :)
The latter gets untrusted inputs from the internet, while the former gets untrusted inputs from the LLM
I run a single-node k3d cluster on each of my MacBooks which uses Agent Sandbox[0] to keep harnesses isolated. Harnesses access models through LiteLLM only. I have aliases for `kubectl exec`ing into whatever harness I need.
What does this even mean. The only capability of an LLM is generate text.
My brain can only generate electrical signals. My hand responds to electrical signals and can interact with the real world. The two together can do more than just what my brain alone can do.
If you don't trust a particular brain, don't put a gun in the hand which is connected to it. If you don't trust a LLM, don't connect it to a harness which has access to your production database and only recent backups (https://www.theregister.com/2026/04/27/cursoropus_agent_snuf...).
Manus rebuilt its harness five times in six months. The model stayed the same, but the architecture changed five times.
LangChain re-architected Deep Research four times in one year.
Anthropic also ripped out Claude Code’s agent harness whenever the model improved.
Ever since Mitchell Hashimoto mentioned the harness in February, people have been trying to claim that concept. Eventually, someone will probably sell a book called Harness Engineering. I will buy it, of course. Then I will write a blog post about it that nobody reads, with a link that will be buried under ShowDead as soon as I submit it to HN.
And by that point, IT companies will start asking:
“You’re a new grad, right? You know harness engineering, don’t you?”
In my opinion, the main driver here is how fast models have evolved in the past 12 months. It makes the architecture of everything around them obsolete, very fast.
We went from using models as a building block, wrapping them in heavy workflow code, to now models being smart enough to drive their own workflows and planning.
One thing I wonder about is whether path routing alone is enough.
If `/workspace` goes to the sandbox and `/memory` or `/skills` goes to the database, the path tells you where to send the request. But it does not tell you whether this user, session, or agent is allowed to access it.
When I built something similar with an MCP filesystem, I found that I needed a scope check before actually running the operation. In my case, I was using GPT dev mode through a Cloudflare tunnel to control my local environment/model, so this kind of boundary became important.
So I like the path-routing idea, but I wonder if it eventually needs a scope or permission layer as well.
Regarding scoping: In our case, the agent loop runs in the same way as our API server does (as in, it’s a multi tenant service running in a container somewhere). And we solve scoping in the same way.
To put it in other words, whether it’s the API receiving “GET /memories/id” or the LLM requesting “Read(/memories/id)” we do pretty much the same thing (check authN/authZ, scope the db request, etc).
Basically the LLM is just another API client using a slightly different format for inputs and outputs, but sharing the same permission layer.
What. The idea is as old as anyone can remember, and wrt. LLMs, it was known to be important since at least as early as ChatGPT being first released.
But I think the term started being used closer to its current meaning around this point:
https://www.softwareimprovementgroup.com/blog/what-is-harnes...
In a way, the sequence was something like:
prompt engineering(23~4) -> context engineering(25) ->harness engineering(26)
At first, it was mostly understood as a correction or extension of prompt engineering. But the idea of “harness” as the layer that corrects, constrains, and operationalizes agents seems to have emerged much more clearly around 2026.
So yes, there is definitely some terminological confusion in the early phase. That is normal. New technical fields often begin with several competing names for almost the same layer, and only later does one term become stable.
The word harness brings the truth of LLMs back down to Earth.
it really felt like between 2018 and 2022ish like LLMs had this magical aura, like the orchestration layer was intelligent, maybe even recursive, beyond what simple functions could do. It was assumed that this was a solved problem. The word "orchestration" denoted it, the words we used were full of optimism. When you lift the veil, it really is just regex, and cool tricks sure, but it's a harness it's a utility, there's no magic here, there's realism.
Maybe the labs even had a part to play in this as well; attempting to make themselves look magical. I mean just look at the choice of name for "Mythos", it's about bringing back that feeling of myth and magic after we saw under the veil.
The reality is that the labs have produced magical models yes, but are locking them into ecosystems that leave a lot to be desired, and are easily reproducible, and essentially are cron jobs, regex.. things we've seen in traditional cloud for decades. It feels like an attempt to create a moat where there is none.
Maybe I'm wrong but this has been my impression
"harness engineering" is the term claimed by that article to have originated in February. It does seem obvious in retrospect and I don't remember an origination point, but there's at least one hn comment predating that in December[2] and it doesn't treat it as novel.
I will admit that my bias is against any self congratulatory buzzword fads (I'm still not over "MCP is the USB of LLMs" or whatever and that's been a year now too). "Who coined the term harness engineering?" -> who cares? It was already widely being done.
[1] https://www.lesswrong.com/posts/7mqp8uRnnPdbBzJZE/is-gemini-...
A lot of this post presents false dichotomies. It assumes the existence of a sandbox that is by definition ephemeral or "cattle-like". Why? There are reasons to do that and reasons not to do that. You can have a durable computer with a network identity and full connectivity, and you can have that computer spin down and stop billing when not in use.
There are a zillion different shapes for addressing these problems, and I'm twitchy because I think people are super path-dependent right now, and it's causing them to miss a lot of valuable options.
[1]: https://fly.io/blog/tokenized-tokens/ (I work at Fly.io but the thing this post talks about is open source).
Giving the whole machine also doesn't answer the question for how the agent can hook into actions that eventually require more perms, and even if you "airgap" those via things like output queues that humans need to approve, that still feels "harnessey" to me.
I feel a bit guilty of debating semantics here, especially as I can't/don't intend to convey any confidence in a "right answer", but my reason for being pedantic is that I do think there are interesting tradeoffs between "P(jailbreak or unexpected capability use|time)" and "increasing power/available capability set", as well as interesting primitives emerging in terms of the components you'd need regardless of where you drew that line (ala paragraph 2.)
[0] - https://www-cdn.anthropic.com/3edfc1a7f947aa81841cf88305cb51... (specifically section 5.5.2.4)
The "Your credentials stay out of the sandbox" problem, to quote them, is what I see your "require your perms system to enforce it" as implicitly solving for.
(Their "sandbox as cattle" discussion had less bearing on the "which pattern" question to me, since I tend to treat most parts of my agent stack as cattle-like, potentially out of a bias towards that architecture broadly, as I find it's much easier to reason about when as much as possible is disposable/idempotent/eventually consistent. The durable execution point also assumed aspects of the agent scaffold ala prompts don't have to be turned over in deploy, or conversely, can't finish their tasks and then migrate incrementally, and while I might cynically raise an eyebrow at the focus on 25ms for sandbox calls given the dev loops I currently experience, I'd argue there are other ways to solve that problem in both an in or outside of container sandbox pattern.)
I'd even agree with their final point "Consistency is the part we haven't answered" but in a different angle than they intended, as to why my focus was on "how do you _constrain_ agent behavior" since that has been, in my experience, the biggest bottleneck to letting agents do more.
This is an interesting and novel field, so I’m not pretending I know the answers, but this is what worked for us :)
At the end of the day, and oversimplifying things: why would I want to spawn a for loop that calls an API (LLM) into its own dedicated sandbox/computer?
When the model wants to run a command, it’ll tell you so. Doesn’t need to be a local exec, you can run it anywhere, the model won’t know the difference.
The agent loop itself doesn’t need sandboxing. In many cases, most tool calls don’t require sandboxing either. For the tools that do require a computer, you can route those requests there when needed, rather than running the whole software in that sandbox.
To me running the agent loop in the sandbox itself feels like “you should run your API in your DB container because it’ll talk to it at some point”.
Because the moment you use k8s, you have to assume that, apparently. Or so Im told by all the infrastructure people I speak with. Getting these pods to not disappear just because one process ran out of memory has been an herculean task.
I wish our standard deploy processes produce durable computers that dont break our bank but that hasn't been an easy requirement with simple infra teams.
I've heard many claims that because LLMs are tuned to specific harnesses, we should expect worse performance with novel architectures. That seems to make people reluctant to try to put effort into inventing them.
I’m worried about the same (models tuned for specific harnesses).
We actually work around that by respecting the “contract”. For instance, our harness’ Bash signature is exactly the same as Claude’s. We do our sandboxing stuff and respond using the same format.
In the “eyes” of the model there’s no difference between what Claude does and what we do (even though the implementation is completely different).
We basically use Claude’s tools as API contract
I'm building an agent sandboxing system for a client atm, and was about to start working on a system of ephemeral, short lived, derived secrets for the agent to use.
Lots of great thoughts to steal in this piece. Thanks again.
It's all tradeoffs, and picking the ones that work for what you want to do is what architecture is. The more informed you are about the tradeoffs, the better you can make your architecture.
I mostly use llms individually, so this is a real blind spot for me. But I might convinced to share a corpus of memories between developers if it ever becomes practical.
But for now aren’t context windows still sometimes smaller than the task at hand… so those “llm memories” take the form of literal documentation???
The closer llm memories get to native format, i.e: stored tokenized content, don’t we lose compatibility between setups anyhow. What if you’re using fp16, and I’m using nvfp4?
I don’t get it. Calling an API requires a sandbox in most cases. The others could be abused in service of an un-sandboxed agent with API access.
If the harness is outside the sandbox then it’s just an ambiguous and confusing security model and boundary.
I'm not following why this would this be the case? The purpose of calling the API is to get data or effect a state transition on some remote service, but I don't follow why the originating machine matters.
Or is your objection about auth?
I think the confusion is that “agent” is used for two very different things:
- building an agent
- an “agent” product/runtime (Claude Code, etc)
In the first case, the model never executes anything. It just outputs something like “call this API”. Your code is the one doing it, with whatever validation you want. There’s no need for a sandbox there because there’s no arbitrary execution.
To move forward, I suggest thinking less about the implementation details and more about the concepts around your approach in this system.
For example a database storing files accessible remotely by multiple users is really a file server and can be implemented in multiple ways. And that's not the problem you're trying to solve here.
Depending on where your sandboxes live, a bind or network mount and a gitwatcher outside of the sandbox would accomplish something very similar with less customization.
You mentioned not having a solution for concurrency. So think about that first, without limiting yourself to a single implementation.
Maybe the storage should not be per file, but be a knowledge graph that is presented as a file to the LLM in the sandbox. Concurrent mutations in knowledge graphs may be easier to solve, especially with the help of LLMs.
Or perhaps it starts to work well already by simply showing the git merge conflicts to an LLM and having it reconcile the separate writes. Maybe even let it "post feedback" to the LLMs in the container, when a concurrent write has happened to a memory or skill to tell it "hey while you were working I also learned this potentially related update".
The reason agents work is because they have access to stuff by default. The whole world is context engineering at this point, and this proposal is to intermediate the context with a bespoke access layer. I put the bare minimum into getting my dev instance into a state where I can develop, because doing stuff (and these days: getting my agent to do stuff) is the goal.
This makes slightly more sense if you're building a SaaS and trying to get others to give you access to their code, their documents, and the rest so you can run agents against it. But the easiest, most powerful way is to just hook the agents up to the place that's already set up.
[0]: https://www.vita-ai.net (AI agent for content creation & social media management)
But shouldn't there really be another sandbox where the agentic tool calls execute? This is to contain the damage of the tool execution when it goes wrong.
And, the agent harness itself should either implement or be contained in a third sandbox, which should contain the damage of the agent. There should be a firewall layer to limit what tool requests the agent can even make. This is to contain the damage of the agent when it formulates inappropriate requests.
The agent also should not possess credentials, so it cannot leak them to the LLM and allow them to be transformed into other content that might leak out via covert channels.
At the end of the day, it’s a “simple” loop that calls an external API (LLM) and receives requests to execute stuff on its behalf.
It’s not the agent running bash commands: you (the harness author) are, and you’re in full control of where and how those commands get executed.
In the article’s case, bash commands are forwarded to a sandbox, nothing ever runs on the harness itself (it physically can’t, local execution is not even implemented in the harness).
On exe.dev the agent (Shelley) runs in a Linux VM, which is the security boundary. All the conversations are saved to a sqlite database, and it knows how to read it, so you can refer to a previous conversation in the database. It's also handy for asking the AI to do random sysadmin stuff, since it can use sudo.
A downside is that there's nowhere in the VM where secrets are safe from possibly getting exfiltrated via an injection attack. But they have "integrations" where you can put secrets into an http proxy server instead of having them locally.
Also, you don't need to use AI at all. You can use the VM as a VM.
1) It's still assuming agents have CLIs. This is a very developer-centric concept of agents, and doesn't map well to either consumer or enterprise agents that aren't primarily working with files. Skills, plans, TODO lists, and memory are good, but don't have to be modeled as raw file access. Many harnesses have tools for them.
2) It's talking about a singular sandbox. That's not good enough for prompt injection prevention, secure credential management, and limiting the blast radius of attacks.
Sufficiently advanced desktop tools starts to want server capabilities like teleport, scheduled tasks, ci mode, shared sessions, etc. Web-based ones start here to begin with.
Pretty soon after you have a server, you also think about multitenancy isolation and task isolation. The article's sandboxing also matters for regular old non-LLM code escapes in a multitenant world. We have to assume malicious python by the attacker, whether AI or human, and cannot let one tenant's python have write access to trusted surface of another.
-What remains unsolved is what should an Agent reasonably have access to in what context and for how long (etc).
Probabilistic code that can run far faster than human driven code, we don’t have a great model yet. We all should spend our energy there…
- Separating / putting controls on the FS resource is no different than putting the agent behind a firewall / allow-deny list.
It doesn’t invalidate running a sandbox in a sandbox to have better security.
[1] https://openai.com/index/the-next-evolution-of-the-agents-sd...
I'm really intrigued by your point on read-memory vs a dedicated read interface, because it is a real insight about success rates in harness design.
How did you come to the conclusion you did? Could you speak a little to the evaluations you ran, or the data or anecdotes you collected to validate that decision?
I'm also curious about the overall framing of the question, which I'll challenge with, does the agent have to have a where?
An agent could be modeled by a set of states and transitions. I don't think that there's anything inherently necessary about the current "one process claude" approach for harnesses, other than convenience. Why hasn't a fully distributed harness, built on functions and tables, gained more mindshare?
Arguably this is a feature not a bug. Conflict resolution forces the need for a process to come to agreement on a common source of truth - one of the reasons why most Git repos don’t allow users to push to main directly. Writing directly to a shared memory database seems like it would result in chaos and a host of side effects once the number of users scales.
There's no harm in a string, only in the execution.
I create Tools as Actors, which you preconfigured for the LLM context (in-house agent loop). The tools being preconfigured means you setup their environment before they can be executed. If it calls a bash tool for instance, the Tool Actor gets called and then it runs that command against an attached remote VM.
Or filesystem operations, are just read/writes inside a .zip file, which is overlayed onto the target project at build time.
This article is spot on, and I probably say that because it's self reinforcing.
I still kind of think it’s a decent idea but it’s too close to MCP with drawbacks that make it a harder sell than MCP. It’s hard to compete on functionality from a secure sandbox if users decide they don’t care about security.
It currently only exposes a rudimentary set of tools which I’d like to expand. The sandboxes created by MCP are generally ephemeral. The daemon will clean them up after an hour of no usage.
But it’s so cool that they get their own IP and you can ssh straight in. I can see that being very useful when you want to share with a colleague and then close your laptop (assuming it’s running on a remote instance).
Tools, memories, sandboxing, steering, etc
Because there isn't really much more to it. And ever since we, i.e. those of us who played with ChatGPT API early on, bolted tools to it, some half a year before OpenAI woke up and officially named it "function calling" - ever since then, we knew that harness was the key. What kept changing was which logic (and how much of it) to put in explicitly, vs. pushing it back to the model on the "main thread", vs. pushing it to a model on a separate conversation track. But the basic insight remains the same.
--
[0] - Well, today - until recently you'd call it a "runner" or "runtime".
AI companies would love if everything ran in their cloud, but arguably there are latency reasons or other reasons to run at least some stuff in your own computer
The harness is the part that makes the API calls, interacts with the user, makes the function calls, and keeps track of the conversation memory.
You can also use the LLM to summarize the conversation into a single shorter message so you get compaction. And instead of statically defining which functions are available to the LLM you can create an MCP server which allows the LLM to auto-discover functions it can call and what they do.
That’s the whole magic of something like Claude Code. The rest is details.
Personally, for me it embodies a level of autonomy. I define that as, an AI model with potential to interact with something external to itself based on its output, where that includes its own future behavior.
- Easy single command CLI agent spawning with templates
- Automatic context transfer (i. e. a bit like git worktrees)
- Fully containerised, but remote (a bit like pods)
- Central, mitm-proxy zero trust authn/authz management (no keys or credentials inside the agents), rather enrichment in the hypervisor/encapsulation
- Multi agent follow-up functionalities
- Fully self hosted/FOSS
Basically a very dev-friendly, secure, "kubernetes"-like solution for running remote agents.
Anyone has an idea of how to achieve this or potential technologies?
The networking part I can fix with a second docker container and network_mode I think.
The centralised key and permission management and agent dashboarding is severly lacking though. But that's for now my least worry I think.
Latchkey does support some form of permissions management too.
The use case is different but this article strikes some vague similarities around an agent API to remotely execute commands.
Another benefit of moving the harness outside the sandbox is you get to avoid accidentally creating a massive distributed system and you therefore don't have to think so much about events/communication between your main API and your sandboxes.
The use cases will evolve so much more in coming weeks and months that the boundaries will blur. You can still contain the agent outside in a sandbox though.
This problem is quite common and not limited to memories. For instance, Claude Code will block write attempts and steer the agent to perform a read first (because the file might have been modified in the meantime by the user or another agent).
Same principle here: rather than trying to deterministically “merge” concurrent writes, you fail the last write and let the agent read again and try another write
Anyway. General advice: treat harnesses as any other (third-party) software that you run on your server. Modern harnesses (the ones from big companies, you need to subscribe to) are black boxes. Would you run a random binary you fetched from the internet on your server? Claude code, codex etc. are exactly this.
My company is not a "sophisticated" software development organization. We're 3000 people where 2000 do nothing with AI, 900 are naively jumping on every AI bandwagon that comes along (or rather, parroting whatever they read on the Internet to complain about how hard their job is despite it not having materially changed in the last 15 years), 90 are capable of doing anything towards implementing anything involving AI beyond "just use Claude," and 10 have the experience to really scrutinize what is going on. And our work is of a nature that scrutinizing the exact process of how results occurred, what data we came to it by, is essential. There are regulations and compliance issues that could land people in prison if we don't and the results are eventually proved to involve inappropriate data. What does that mean? I'll just say we primarily work for DoD.
I have very long experience with managers asking for the moon and not listening when the engineering staff raises red flags. They ask us, "why can't we just do X?" Where X is whatever they've recently read about in whatever MBA-targeted publication that was bought and paid for by the service provider profitting off of X, with no skin in the game regarding the nuance, because the relevant regulations are written to scapegoat the person in the chair bashing the keys and not the person making the decisions and hanging the former person's jobs over their heads. The "why can't we just do X" is not an in-good-faith question, it's a statement that you need to shut up about your concerns and "just do X."
But out of desperation/malicious compliance, I've started developing an agentic harness that can "just get AI to do it" for the data sources on which we work. And I've noticed two things: A) agent harnesses are not that hard to write (honestly, anyone with basic programming competency should be able to do it), and B) they can only ever work on what you give them. I suppose the last point should be obvious, but I've had enough conversations with folks who expect magic that it is clear that it is not actually obvious.
And that's where I get into "extant agent work is lazy." The agent harness I've developed is incapable of accessing data its operator should not have. If you are cleared to only see a subset of the universe of data, then running this harness cannot possibly give you access to more than your clearance. I'm not trying to brag here, because this was not a difficult guarantee to make. In developing the harness and giving it tools to do work, I just developed the same access controls I would have done for a human user accessing an API to the same data. The only thing that is different about my approach is that I didn't use an off-the-shelf harness with tools developed by others. I just wasn't lazy about my job.
My key stakeholder was skeptical that I was able to do this, mostly because he has subconsciously intuited that our organization is not very sophisticated in developing software. He doesn't understand that employing AI isn't magic, and I think that is the case for a lot of the people who use AI the most here. They see products like Claude go to work and think there is some kind of special sauce there that requires the development by a "frontier" AI firm to actualize. But the truth is, the more you develop agentic AI capability, the less AI you are actually employing. The AI eventually becomes just an orchestrator of tools that perform work by not-AI means. If you are lazy, you try to lean on naive tool implementations that let the AI do whatever "it wants." And that's where you get into trouble. But if you show up to your job and be diligent about implementing those tools, there is no possible way the AI can screw you over, because you never gave it the unrestricted access to curl or `rm -rf`.
This is why, even if AI does become a permanent fixture of software development (still not convinced, even after all this experience), you're still not getting rid of us software engineers, despite how much you hate us. You still need us to protect your data, and nothing about AI has changed the equation that ends in "data is king." If anything, it's more important than ever.
Edit: I'm specifically developing a multi-user agent, accessed via a Web application over a shared database. Row-level access control is baked into every tool and I can do this with little effort because dependency injection Is A Thing. Thus, the parameters of access control never even reach the AI.
