Why the simplest desktop agent abstraction wins
bytebot.ai39 points by atupem 3 days ago
39 points by atupem 3 days ago
What are the biggest issues that the agent faces at the moment? I still find these general purpose agents frustrating to use at times because people position it as if it could do anything and then when you give it a reasonably complex task it breaks down.
I guess if someone figured out way to minimize the impact of an error, like a way for it to gracefully handle it without it feeling like too much work, that would fix most of the problems.
Lots of interesting issues:
- The agent has a tool to set it's task to 'completed', 'failed', or 'needs_help', with the last one being a option for human in the loop scenarios. Sometimes the agent gets lazy and says it needs help prematurely.
- Additionally, the agent can create subtasks for itself, either to run immediately, or to schedule in the future. Here it again can call that tool a bit too eagerly, filling duplicate subtasks for a task that involves repetitive work.
- Properly handling super long running tasks, that run for 1+ hours. The context window eventually hits it's limit (this will be addressed this week)
Aside from those top of mind issues, there's a whole bunch of scaffolding issues - filesystem permissions, prompt injection security, i/o support, token cost - lot's to improve!
We're still super early, but already these agents are showing flashes of brilliance, and we're gaining more and more conviction that this is the right form factor
> showing flashes of brilliance
A “flash” of anything is also called a fluke, or a coincidence. The dumbest moron can have a flash of brilliance on occasion. So could a random word masher. Consistency is what matters.
> and we're gaining more and more conviction that this is the right form factor
Are we? Who’s “we”? Because it looks to me like the LLM approach is lacklustre if you care about truth and correctness (which you should) but the people and companies invested don’t really have a better idea and are shoving them down everyone’s throats in pursuit of personal profit.
Agreed, and the consistency has improved over time. I remember only a 9 months ago struggling to get a browser agent to accurately click on a checkbox. The growth trajectory is what has us excited.
"We" are a YC-backed startup: https://www.ycombinator.com/companies/bytebot.
Re: truth and correctness, their are different tolerances depending on the type of task.
> We're still super early, but already these agents are showing flashes of brilliance, and we're gaining more and more conviction that this is the right form factor
Slow down cowboy; we're seeing "flashes of brilliance" and "that this is the right form factor" for writing code only!
I'm still waiting for AI/LLM's to be posing a danger to jobs other than those in software development and the arts.
This one isn't for coding, they mention in the post that coding agents thrive in custom tool-use environments.
> This one isn't for coding, they mention in the post that coding agents thrive in custom tool-use environments.
Well, that is why I am skeptical and said
>> I'm still waiting for AI/LLM's to be posing a danger to jobs other than those in software development and the arts.
The goal of this product is admirable but, I feel, lacks some grounding: doing screenshots, then converting those images to text, then processing, then converting that to actions, then converting the actions to input events ... results in 4 separate points of failure. So many points of failure each with a success rate (last I checked) of <90% gives you something stupid like an eventual success rate of 0.9 * 0.9 * 0.9 * 0.9 = 0.66.
The same iterative workflow for software development is pretty much 2 steps: process input, then produce output, with 100% success (or close to it for "output", as it's just rewriting the files according to the processing) and 90% for processing which is why it appears to work so well[1].
I dabbled briefly in this and explored a few different ways of making LLMs use the ERP/business system effectively, and with all the current popular business systems, this is simply not possible with a high enough success rate because those systems have few "structured text" output, and even fewer "structured text" input. In fact, some of them have exactly zero "structured text" input.
To make the most of LLMs in your business system, you're going to need a new one that is primarily text-IO based (structured text, if necessary) and only secondarily GUI-for-humans based.
[1] In truth, using tools is a poor way to extend the reach and grasp of the LLM into the operator's context.
It works well for one mainstream use-case: software development, because then you need less than a dozen tools to automate an entire development iteration (read file, list files, insert into file, run test command, etc).
Try doing that with a mini-ERP type of system; there's just no way to keep a small set of 12 tools that can do any workflow that the operator can do. You'll quickly run into a situation where every prompt request includes tool description for about 500 tool calls.
Agentic automation is working very well for coding, where all the input is structured text, all the output is structured text, and all the changes are structured text.
The only way for ERP, Accounting, etc to ever get to this level of agent-based automation is if the base product itself is completely 100% structured text IO based, with the human-operator interface built on top of that.
I respectfully disagree! There's a lot of opportunity behind keyboard + mouse + screen.
In a way Bytebot is a maximalist bet on the growth and improvement of multi-modal LLMs. I firmly believe that in a short period of time, the token cost will drop, while the capability increases (both dramatically). It's still uncertain, which makes it a great asymmetric bet.
We don't do any sort grounding or image conversion, and we offer a handful of tools. I'll go into more detail in my next post.
See my comprehensive reply downthread (it's very long, you cannot miss it).
While I am skeptical due to already having explored this for SMME Line of Business applications, I wish you all the best of luck.
My approach is to simply build a new system from the ground up that can take advantage of structured IO.
[EDIT: send me a message with a link to a post about your product (or this blog), I'll connect with you on linked-in and share your post with my network, meager though it may be]
What is your business model?
We're working with design partners as forward deployed engineers, helping setup Bytebot on their infra and tackle use cases.
We'll be launching a self-serve cloud platform soon!
Does anyone have experience getting agents to understand terminal applications? Like, in general an arbitrary ncurses application.
A more specific case I’ve struggled with is output from a long-running program like ping. You’ve got to know when to terminate.
I wrote a terminal-based falling sand game in rust and incrementally fed the entire screen output to a multimodal LLM (for better generalization) and also got it to attempt to generate interesting initial conditions by spitting out raw characters.