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We originally started with building a CLI tool so our LLMs could more easily interact with our platform. I cannot recommend enough the value of having an internal CLI. It’s both fun to build and extremely useful for agents.
We paired this with skills initially, but found that the way folks built skills was often too prescriptive and limited to the authors own specific function in the company. A 2k line long skill suffers from the same gaps as we do, if an agent is just following a laundry list it’s less likely to reason about the request it’s doing.
So we instead asked ourselves: what if we just _let_ the agent reason about the work to be done and only provided the tools + guardrails to gather context and perform accounting work?
Turns out frontier models are GOOD at what they do, they outperformed our highly prescriptive skills and were able to work across a larger set of tasks even without instruction on how to do those tasks.
It’s a breath of fresh air from the decade of CRUD I’ve worked on, harness engineering is very neat.
The native app I'm building on top, which I hope people who are less technical (or not technical at all) will use, is even more interesting because it's not just supposed to shell out to the CLI for everything and needs its own state.
I find your description intriguing but I'd like to see it to make sure I understand it.
Could you give an example of an accounting guardrail you created?
Usually I’m returning TSV as a default format and I add a `help-all` subcommand to list every available command at once when needed. Another thing that helps is adding just-in-time context-sensitive hints, such as: user has just run a list query with at least one result. Add a one-liner to the response explaining the command shape for getting the detail view of the first response.
In terms of skill files, I like to have my CLI generate them dynamically at runtime by walking their own current command tree and then feeding that through a text template.
Examples from a public project: https://github.com/radiusmethod/gitlab-kiosk/blob/main/skill...
As for an example: if our agent wants to book a journal entry to cash accounts for a client, it MUST provide receipt and directly link the transaction from the clients bank feed, if it attempts to do so without the requisite information we deny the tool call and ask the agent to escalate back to the client for proof of receipt.
Often times this results in the agent not doing the work and instead sending a message back to the client asking for proof of the transaction.
For humans on our platform there may be valid situations where we’d want to allow this, but for our agent this is a hard guardrail thus why it’s not just standard validation for any JE posting on our platform.
When I say handoff, I mean:
Does such a thing exist?I used to think that a PR would be a good place to centralize all this. Who cares what IDE, or developer, or location. But, now I feel like an agent harness might contain that better.
Why do I want handoff? I keep losing context of where my harness is running. Sometimes I am inside an isolated VM. Sometimes I'm on my laptop, sometimes I'm on my home machine with the big GPU for local models. If I could spin up a harness that could identify itself inside my tailscale network, then I could probably have a single web UI which allows me to keep all that context straight.
I'm tempted to experiment with Pi to configure such a thing. But, perhaps there are patterns out there already with a harness I have not considered.
Let's assume handoff happens when one "agent" finishes its work on one task, i.e. "submit a PR".
At that point you want to exit the agent/clear context etc (any context the next actor needs should be in the handoff artifact).
And the orchestrator calls the next agent with the artifact.
Claude can do this with subagents. If you want to get more serious, I'd look at "durable workflows" and check out what the pi people have to say: https://earendil-works.github.io/absurd/ https://earendil-works.github.io/absurd/patterns/pi-ai-agent...
you should also look at dbos https://www.dbos.dev/
And then do a search for these terms on HN and get some idea of their shortcomings vs a 'real' orchestration tool like Airflow or Dagster
And Pi is the best harness because of the amazing extension system. You can build extensions that turn Pi into a stock trader, software factory, anything. I tried switching to another harness but none have extension functionality as good as Pi.
Even if there is a new harness or agent project, I tell Pi to dig into the codebase and then make me an extension that brings that functionality into Pi. I did it with Prime Intellect’s and Deepseek’s harnesses and those are built on Pi.
Don't get ahead of yourself. Harnesses are not exactly rocket science and will be a commodity.
The real value providers here are the hardware, then the LLM as a distant second, and at a much larger distance the harness.
Labs are now post-training models with Harness so that Harness now gets absorbed into the weights.
Solar goes all the way up => power is commodity.
Some hyperscaler goes bankrupt => hardware is commodity.
Models get real good => output is a commodity, no profitable problems to solve anymore.
Open source models get good => models are commodity.
I really though this comment was a satire ...
This entire forum is infested with shameless hype chasers and biological linkedin bots.
I’m thinking of how in cyberpunk, people are replacing their cybernetic enhancements all the time. You could alternatively bioengineer your own body towards the desired outcomes, but that’s more constrained by the trajectory your body has already taken, whereas the promise of cybernetic parts is that they are more independently replaceable. (Probably an illusion in practice, but I’m talking about the fictional ideal.)
As another analogy, monolithic software tends to quickly become hard to change significantly, whereas a plugin architecture tends to be more flexible and modular, and people can share and combine their various plugins.
They probably used an LLM to come up with this bizarre metaphor.
- They can already reason better than many humans and are still improving all the time
- Harnesses are improving all the time
- We're already exploring things like long term memory, long term goals, and other things that humans have which LLMs traditionally lack
- An AI agent can read and reason about every piece of AI research ever published, including looking for insights that humans may have missed. A team of humans could never do this even if they dedicated their whole lives to it.
- They can design and execute experiments on a mass scale to determine what does and doesn't work
- Large AI labs have more than sufficient resources and motivation to throw at the problem, and are in fact doing this.
And no I came up with the metaphor all on my own, send me the chat of you getting the LLM to come up with it. Why not argue based on merit instead of strawman and ad hominem attacks?
You can never tell if the goomba opinion of the forum will agree we have reached AGI (seen that happen on a few threads lately) or will readily call that a ludicrous proposition.
The words "once that settles" are doing historic levels of work here.
No human on earth has a clear idea whether model technology will settle tomorrow or 100 years from now.
There's every reason to expect architectural breakthroughs will keep being discovered and causing nuclear blasts of forward progress.
I do agree that harnesses are going to extend AI capabilities a lot in the next year, but after reading Pi's page I don't see anything that makes it particularly special in terms of functionality, other than being more provider-agnostic.
Many of my harnesses eventually turn into customized UIs around the chat interface.
The harness facilitates the work animal doing work for you.
Not climbing harnesses to keep you safe.
1. You can use the '/new-tool' and tell what kind of tool you want (including whether it should be task-scoped, workspace-scoped, or global), the model builds it, the harness runs validation and other tests until the tool is ready
2. The model decides that in such and such task, it would be helpful to have a tool like this, it can build a task-scoped tool.
In either scenario, the tool catalog is rebuilt, and the new tool is instantly available in the next turn.
What I can see is a world where we end up with a Chromium-shaped harness, a fully featured standard implementation everyone builds against, because doing every single thing yourself would be crazy.
The antithesis to Pi, if you will.
https://pi.dev/packages?type=extension
I primarily like how it manages sessions, and how agents can easily reference other sessions.
https://github.com/manojlds/pi-rlm
So if you do want to use it, use the Codex sub. Once you install it, run Pi and /login and you’ll get login with ChatGPT. From there, Pi can tweak it’s settings if you ask. Check out their extensions (or ask Pi) and that will take you most of the way there.
What hiccups were you having?
Not out of the box, but you can add agent sdk. I'm not sure how great the results will be though.
Don't understand what people see in them.
0. https://github.com/Kahtaf/OpenCandle
looking at the website. i can't really tell if they have benchmarks and measuremnts on how all that improves capablities over just using regular agent withtout all that
also i think its hard to build general harnesses if they were trained on specific harness architecture.
There’s evidence of harnesses making a smaller, weaker model perform better than SOTA and some benchmarks ban harnesses because it becomes too easy.
the harness is what you take with you on a trip/task
whatever you take with you is not free (system prompt, tools, skills …)
some models are really good even if you bring almost no skills, tools or system prompt
the harness is the complement to the model
the better the model the more minimal the harness can be
harnesses like pi [0] and smol [1]are on the more minimal end of things
[0] https://github.com/earendil-works/pi
[1] https://github.com/smol-env/smol
Well kind of, I wouldn't be surprised to see that some things marketed as agents are actually good old deterministic software.
It truly proves like there's a handful of thought leaders on Twitter that everybody follows blindly and start to copy down to the lexicon and parrot everywhere else.
Anyway I've been building my own harness on top of pi- www.freepi.ai (it's based on Pi, but now I have an OpenAI compatible endpoint so I'm thinking of it more like free-api :-) ). Basically ad+training supported so I can offer completely free inference. It's really important to me that we don't have harnesses and intelligence trapped in a "have and have not" world. If we don't all have access to intelligence we will end up in a dark place.
Thats again where the visual of Steven Hawking and the wheelchair really stand out in my mind. It's not enough to have the raw intelligence, we need a really good wheelchair too.
https://github.com/aaif-goose/goose
Full disclosure: I work at the LF, but not the AAIF.