There is this ACM blog post called "Manual Work is a Bug" [0] that was originally written to help humans automate processes using code. I find it just as applicable today as when it was written. You and the LLM look at what has to be done and then figure out the scripts/tools to make it happen. You then tie those tools into a system.
The more I use the above the more it makes sense and the worse the whole "just commit the prompt" seems like nonsense.
By trade I am a .Net software developer so as a lot of people would imagine — I was not able to accept a script that wouldn’t be reusable and flexible, basically over engineered.
I do quite some devops so I finally had to accept the fact that I can write simple script with hardcoded values that will live on a server (where I can copy paste and change values to meet other server) and most likely I will not have to look at that script for years as it will be running with cron doing its job without an issue.
Over engineered scripts designed from get go always required debugging from time to time so lots of time I was just doing stuff manually to make it quicker.
So I started winning when I accepted first script can be really simple and when needed I can move it to be parametrized but if not it will just keep doing it's job there on the server.
The upshot of this is you actually have a much better understanding of the different way your script needs to work if you're adapting it for a third use instance.
One of the meta-processes designed in is pushing automated processes, both defined and discovered, down as far as possible. "Down" here means as far towards the metal as reasonable. So automate the automatable stuff, and leave the LLMs to do stuff LLMs are actually good at.
A trivial example is 'handle this bugfix ticket'. Many actions in a bugfix are pre-defined, for example a git commit at the end of the ticket. So Maelstrom will, at the end of a bugfix workflow, will force a git commit from the LLM that did the implementation. The LLM never even sees the git command, it just fills in a JSON field with a commit summary, and the workflow handles the commit.
There are some inroads into this vision - but I haven't seen anything build directly for this (beside my own experiment).
I have some 'vibe noted' notes on this: https://zby.github.io/commonplace/notes/unified-calling-conv..., https://zby.github.io/commonplace/notes/rlm-tendril-and-llm-...
You may want to read earlier discussions https://news.ycombinator.com/item?id=48881112 And https://news.ycombinator.com/item?id=48051562
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Gherkin style tests also come to mind
OP's idea "everything is a text file" is good and I use it too. My plans are saved as task.md files, numbered and named. Work items are checkboxes inside the file, closed work items are checked and a comment is added on the same line to provide feedback about the implementation.
I also keep a current-state-of-the-world document, it should be <20KB of text, keep the essential decisions and intents. Loading it allows resuming in <30s.
Something I never saw anyone else do - I save all user messages in a chat_log.md file which is referenced for intent alignment and state recovery. I consider the chat log on the one hand, and coded tests on the other hand as the two walls, the agent works in the mid section between them.
https://horiacristescu.github.io/claude-playbook-plugin/docs...
What I am saying is the opposite - use Claude Code or whatever else - generate actual "programs". Basically scripts. We have tons of ways for "programs" to interact with each other. Then have clearly defined edge case handlers - think "try/catch". How far do you want to go down the rabbit hole in the "catch"? Do you want to re-write a new version of the "program" itself? I do not know, but this type of a system is what Unix already is, with the addition of programs themselves reaching out to LLMs in well defined edge case handlers.
The API is basically what you see as a user of Claude Code or Pi or whatever. You can make new sessions, send messages to sessions, configure which MCPs get started, etc.
I’ve been poking at something similar to what you’re talking about via that route. My client prompts the agent to do a thing, and then afterwards launches deterministic things to check it which can either re-prompt the original session or start a new session.
Eg it automatically runs the tests afterwards, and will send a new prompt in the original chat to fix them if they fail. I also briefly poked at a security analyzer that gets changed files via git and makes a new session to check whether there are security issues and propose a fix that then gets sent to the original session.
If you want a circular loop where the LLM can adjust its own workflow while keeping it deterministic, you can let the agent modify the ACP client that drives it.
https://www.langchain.com/blog/tuning-the-harness-not-the-mo...
As coding agents have accelerated my work, I just build tons of tooling around existing software. Or in rare cases build new ones. If we zoom out of software engineering, we will still be in the realm of files - text or binary. That does not change.
The question is - do we let agents run the tools or the "programs" call the LLMs. The OS is the new agent, but not the same sense of "agent". I want LLMs to be lightly sprinkled in a future "agent" OS, not the other way around.