alt Hacker NewsI’ve been working on a harness for accounting agents at my job recently and it’s been a pretty interesting experience.
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.
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This is the same "tension" I keep seeing in my day job. Some people approach LLMs like they're writing code. They give a long list of detailed instructions for specific scenarios. When I use LLMs I leave things as open as possible. I just give them the information they need and my ask.
As you say frontier models are very good at figuring things out. Being too prescriptive is counterproductive, it over-constrains the model, it fills the context with conflicting instructions, it reduces the ability of the agent to respond to novel situations (and really in real life most situations are going to be novel). If you want to follow a process or a checklist you probably shouldn't use an LLM, or you should use it for some sub-tasks in the checklist/process but something more deterministic to work through the list.
I've been building a harness (on top of Pi for that matter) and have had similar experiences. Pi itself helps a lot with it being extensible by design but it's definitely been a challenge to make certain things work in an expected way.
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.
Can you post a generic version of code for this somewhere (e.g. codeberg or whatever)?
I find your description intriguing but I'd like to see it to make sure I understand it.
So you still have CLIs but they have I presume an help command that describes the capabilities right.
Could you give an example of an accounting guardrail you created?
I think you've really hit the mark on how the harness should be structured:
1. Guardrails - deterministic, social intelligence, team alignment & accountability 2. Learn by doing 3. make it stupid easy for the agent to research and access data 4. DRY
Research supports this. Try picking up some ideas from my harness: https://github.com/rush86999/atom