Love the Unix philosophy and the buffs mentioned of the Linux FS. Lean, transparent, and auditability-first is exactly the direction harness' should continue in.<p>Something I am convinced of though, there probably isn't a single `best` harness for all tasks. Different workloads will likely perform better with certain combinations of model + harness, especially when we are talking about token budgeting and cost tracking.<p>Ambiance feels like a great base “kernel” to build those variants on top of, rather than the one true harness.
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.<p>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.<p>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.<p>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.<p>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.<p>The more I use the above the more it makes sense and the worse the whole "just commit the prompt" seems like nonsense.<p>0 - <a href="https://queue.acm.org/detail.cfm?id=3197520" rel="nofollow">https://queue.acm.org/detail.cfm?id=3197520</a>
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.
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.<p>Gherkin style tests also come to mind
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"
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.<p>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.<p>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.
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.<p>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.<p>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.<p>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.<p><a href="https://horiacristescu.github.io/claude-playbook-plugin/docs/Playbook.pdf" rel="nofollow">https://horiacristescu.github.io/claude-playbook-plugin/docs...</a>
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.<p>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.<p>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.<p>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.<p>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.<p>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.
I think it all comes down to tight, context efficient, deterministic feedback loops. Pre commit hooks work well for this type of thing. Ideally I think a set of those hooks should run on every file edit, however I haven't gotten around to testing something like that yet.
I get what your saying, however we don't need to be stuck with the Claude code harness.
You may find this interesting.<p><a href="https://www.langchain.com/blog/tuning-the-harness-not-the-model-a-nemotron-3-ultra-playbook" rel="nofollow">https://www.langchain.com/blog/tuning-the-harness-not-the-mo...</a>
Mapping agent concepts to the Unix environment sounds like a great idea in general, but I get off the train at adopting the FHS, which is an ancient relic that has no place in a green-field system. I don't know exactly what shape it should take, but something along the lines of Nix seems more appropriate. Or maybe Plan 9.
What's with this "harness" word people have been trying to adopt lately? Are we all going rock climbing?
I think a better term would be bridle, but most people don't know it. But they do put harnesses on their kids and pets.
We're taming a beast.
framework is too 90s
The awesome power of the AI must be somehow contained by us mere mortals.
How much do the labs post-train on the harness inputs & outputs? That's a critical piece to understand if a "generic" harness is at all possible
“That can do anything” gives me Zombo.com vibes. I would be more interested if it seemed to do particular things well.
Feel like there's now a lot of modern software that can do particular things well but there is new promise in finding universal abstractions that can ideally run recursively / safely run any software at their leaves. (Unix + tooling is most of the way there ofc)<p>I feel like Docker Compose / K8S / VM / Dagger.io layers are close but can't quite always recurse flexibly and aren't always simple to run with. Networking / devices / auth are often awkward choke points
Or if it had any kind of measurement across a range of tasks
I think domain specific harnesses are already surpassing generic harnesses. I also think software development is its own domain.<p>My harness is a Claude Code plugin with its own brainstorming, adr, and planning skills with associated review and interview skills. Behavioral testing related to acceptance criteria is built in. Everything in my harness is gated to prevent ratholes.<p>I recently inflated a docker container to execute a set of work with Claude in unsafe mode and immediately saw problems with everything it was doing…and then I realized I had not installed my harness.<p>Running Claude without an engineering harness is like driving a car without brakes or a steering wheel.
What has been the most helpful when developing harnesses:<p>> When in doubt, simplify. Remove, trim and minimize. Reproduce issues in as small cases as possible, understand the full design completely, there is no shortcuts for this.
I really like this idea and the way you mapped the concepts to unix primitives. Indeed llms are already "unix native". I've been experimenting with similar event driven workflows using k8s primitives but that's one level up the stack. This makes a whole lot of sense to me in terms of organizing a shared mental model. Will definitely check it out. Thanks for the good work.
Just one more harness bro, one more harness I swear one more harness and we will have solved AGI bro
Why are we not just using Claude Code or Codex on our machine and using this thing? Real question...
I'm kind of in the same boat and it's been pestering me for months. Every agent is simply a less capable Claude Code.<p>If it had a lossless, massive context window (100m-1b tokens), then it will squash everything. Give it bash + r/w and it can in theory /goal anything.<p>I think there's something to be gained in a production environment be siloing agents for reproducebility/auditability, but I suspect that will go away in the future.<p>There's that video of a silly demo someone made of an OS that was just nested copilot instances that generated the HTML of each window, which allowed you to do whatever you could imagine. It was seen as silly because it was, but that seems truly transformative.
i think the point is less about what agent you should or should not do and more about what is the natural harness for an agent to succeed in. And agents are often autonomously doing things right now, why would you want claude code doing such stuff for you?
This one is open source, and potentially better, since it responds to (file system) events instead of polling.
Awesome work! This is really impressive. I gave a GitHub star.<p>I build precision-editing tools for AI coding agents (hic-ai.com) and worked out thousands of JSON-wrangling and regex issues, so I can verify they are indeed a bit of a pain, across all possible failure modes that AI coding agents and models and harnesses can produce. Anyway, I completely agreed with everything in your article, though I would suggest however that agents need *three* things at runtime to fix a defect: great logging and a clear error response (just like you have it), but also, precision-editing tools that enable agents to make the minimal, surgical change without touching or copying any other portion of the file. These actually change not just the feedback but also the options available to the agent and capabilities in the midst of the workflow to self-heal. If Ambiance adds a kernel to buffer the LLM from the outside world, HIC Mouse adds a "kernel" or buffer between the LLM and its own environment and file system. Anyway, this is such a cool project. Please reach out if you ever add MCP support for Ambiance -- I'm happy to release a new version of Mouse that supports it. Again, great work.