Comment by brainless
6 hours ago
I kind of have a different idea of agents. I totally believe in a deterministic scaffold but I really think that an agent should be as deterministic as possible - the more code, the better.
Think of a typical loop we may ask of Claude Code today (assume we are not using TDD): run some test suite with fail fast mode, diagnose if the failure is due to recent feature changes (pass reference to backend/frontend, github issues, PRD,...). Ask CC to decide if test failed due to feature change and then update the test. Perhaps ask CC to use sub-agent to investigate and fix (if deemed so). Commit each fix, move on to next.
I know, this has so many ways to make blunder but I am talking about the agent here, not our error-prone test maintenance. What if we had an agent that had context of your codebase, deterministically ran test suite, linter, hooks, etc. The "English" prompt would become a code loop with the LLM only brought in to decide if a test has failed because of feature change. Also, we can extract git log, JIRA and what not.
Each tool here is real code. Executable code that calls others and only prompts when they meet edge cases. Edge cases are defined but we can now accelerate the maintenance of these tools using agents themselves. But the system is built on "programs that do one thing and do it well" and then reach out to an LLM for its specific edge case. The agent is how these executables work with each other.
100% agree that the more deterministic code the better up to the limit where you need the LLM's ability to be non-deterministic to kick in.
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.
0 - https://queue.acm.org/detail.cfm?id=3197520
I was unsuccessful in area of automation because of different issue.
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.
This is an interesting share, thanks. Yes, that is my mental model. Use coding agents to generate more "programs" (scripts) to automate everything. Have edge case handlers - and these handlers can develop/update the original scripts.
You could even have a decision tree in code where the leaf nodes are primarily "run this script with these params" but some of the leaves are "ask the LLM"
I believe there should be easy ways to move logic between prompts and code in a smooth ways. Moving from prompts to code is for getting deterministic, fast, well defined and cheap execution - the other way is when you want to quickly extend your system or when you want to relax a pre-condition (which is extending if you look into it deeply).
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-...
What you are describing is precisely my goal with my agent framework.
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.
I’m definitely on the deterministic code train as well. All of my success for long running tasks has been around wrapping the agentic harness (cc, codex-cli, etc.) in a deterministic workflow with deterministic gates. We need a name for this outer layer. In my mind that is the true harness because it constrains the agents failure mode. I think flow engineering has been proposed. Maybe it’s the agentic exoskeleton?
I call it tool-response engineering -- the tool-response JSON object itself and any content supplied, plus the available mechanisms after an initial tool call has been made.
Why do you need a different name?
From harness? Because people expect a squishy set of things from a harness that is different from what I end up building. I end up with a rigid internal structure that the harness uses in-turn (tests with clear error messages, tools, etc.) and a matched rigid external structure that drives the turn tracking progress and deterministically handles the overall progress. You could call that whole thing a harness but that makes the definition muddy and hard to talk about. So scaffold or skeleton seems more appropriate. The harness constrains the agent. The matched endoskeleton and exoskeleton gives it structure.
I believe they call it "loop engineering"
One of my policies for agentic coding is to spend much effort in developing tests, coded tests not LLM based vibes. My projects have around 1:1 LOC between code and tests. Tests are like skin, when the skin is pricked it hurts, agents need to feel pain too.
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...
Thanks for the link. I need to evolve my own system in this direction.
Yes, I agree with this. I am not focusing on tests as much and I think that is a big mistake. Agents need to immediately understand something is off.
I know it's a type of blasphemy here, but deterministic workflows such as what you describe is where langgraph really shines imo.
It is not blasphemy if langgraph is trying to do that. As I understand langgraph manages orchestration in custom built agents. I usually stay away from systems which already make it seem as if building agents is a ritual.
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.
You can drive agents via ACP these days, which I think is the layer you would want for what you’re talking about.
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.
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I get what your saying, however we don't need to be stuck with the Claude code harness. You may find this interesting.
https://www.langchain.com/blog/tuning-the-harness-not-the-mo...
In the age of agents, I feel like BDD is more important than ever since it describes the behavior, and if it does so well it’ll make it much easier for these tools to pick up correct behavior.
Gherkin style tests also come to mind
The best part about LLMs is that, if you write gherkin style tests, you don't even need to automate them yet to get value, the AI can "manually" validate initially and then only codify them into deterministic execution after they've been nailed down. That way you have a bulk set of tests that run automatically, and a few candidate tests that are not automatically run yet, and the boundaries expand over time.
I don't think we're necessarily in disagreement here: I agree that determinism should be taken as far as possible, but once we zoom out of the software engineering world I think things tend to get a lot less easy to automate if that makes sense.
Not necessarily. I have been building client projects for the last few months only using coding agents. I use way more existing tools to handle a lot more of our digital footprint than I used to before: pdf, images, excel, ocr and many more.
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.