A harness [1] was developed by Terrence Tao and some collaborators to prove mathematical results. It has since then been used by others with positive effect. Can someone critique the structure of this harness? I don't know anything about this stuff.
Any benchmarks showing if this actually improves problem solving? Or reduces errors?
The idea is cool, but from own experience in harness engineering, lots of cool sounding ideas can have a negative impact on performance due to emergent and confounding effects.
The TUI and Session manager are straightforward enough. PI is serial by default but can become more DAG-like, where Codex is designed specifically for DAG and this is going more the Codex direction.
I'm much more interested in the memory model and why. As far as I can tell, it's "a vector Db" and not much more is said. Nothing about working memory or procedural memory (there are lots of ways to classify it, https://www.youtube.com/watch?v=BacJ6sEhqMo), but I was disappointed with how "advanced" it seems.
Anyone else have related reading that touches on this? I'm building my own custom harness and want to start implementing loop support, etc. But I also want to build some sort of framework so that it's dynamic (e.g. this needs to run x number of iterations, while planning needs to run y number of iterations).
And its clear that progression is happening on a communith level on all of these and they get integrated later on in commercial offerings like from Anthropic and co.
But also doing a opensource harness and not just giing in to the big companies allows us to have all of this open and transparent and with open models locally.
I'll take the "best way to elicit a clarification response on the internet is to state the opposite confidently" bait...
The example listed in the article -- fanning out a few simple get-population, get-timezone, and make-summary calls -- is, in fact, useless overengineering. This is a basic promise chain with extra steps (priced with tokens).
But as with all software pattern learning, we learn the concepts with simple toy examples that generalize into something bigger. It's the generalization that matters here.
This is talking about a few methods and tricks for spawning effective subagents (collectively, that's the "harness"). Those tips and tricks are nice, but to not be considered useless, we need to make sure we understand why spawning subagents is useful in the first place. Yes parallelism is nice for some tasks, but that's not really what this is about.
The real reason is protecting your context. Yeah, we have 1M context windows that can fit all of LotR in it, but these machines work better when they're narrowly focused. Large context windows run into attention issues and forgetfulness ("Yes, you're right, it was stated I should/n't do X but I ignored it, my bad."). So subagents come into play when you don't want all the tokens associated with a subtask to pollute your main/primary context window and degrade task attention. Split that off to a subagent, let that context navigate the details, and just make sure your main one gets just the input/output blackbox results.
The trick is getting a sense for when the complexity of the task warrants that kind of context protection, vs when a single agent is good-enough. Your toy example will never have enough complexity to warrant the setup, but you might one day find a generalization that may.
agreed. All of these are trying to get to something that can't really every be achieved with LLMs with is determinism. Folks are trying to constrain the models to behave in a certain way with all of these tools, but there's far too many edge cases for them to be reliable. Doesn't mean they can't add some value, but it seems very limited. Hoping it's only a matter of time before we go back to engineering and step back from "vibes".
I felt that way initially. It is also a headache when you invest a lot in that and need to continually test ripping that stuff out as new models come out. Or in the case of opus 5, Anthropic says ditch it entirely and trust it.
But there is another aspect which I do enjoy which is closer to the feeling of dialing in key bindings in vim or getting a really good rhythm going with your vscode extensions or zhs plugins. It is that level of "I want my system to do exactly this thing in exactly this way" customization that a lot of technical people crave.
And you can do it with harness and context engineering in many cases. In other cases it introduces friction because it will be like "cool, I will only output 15 words max unless told otherwise" and then in the next turn completely disregards it with an "oops, you did tell me to do that didn't you."
And that frustration compounds when older model versions may have done a better job of that but new models are like "thank you for your suggestion, your opinion, while appreciated, is irrelevant. Now let me get back to overspending on your token budget. "
We won't. AI engineering is here to stay whether people like it or not.
See what some guys like Linus Torvalds, or Eric S. Raymond are saying about. It's not so much about "vibes" but using the tool (yes the AI tool) in a certain way that can propel yourself towards your goal at unprecedented speeds.
So AI is mostly a thin glue between deterministic processes. Doesn't change the fact that that is enough to achieve an extremely large amount of tasks.
A harness [1] was developed by Terrence Tao and some collaborators to prove mathematical results. It has since then been used by others with positive effect. Can someone critique the structure of this harness? I don't know anything about this stuff.
[1] https://github.com/1stproof/batch-2/tree/main/batch-2-submis...
Any benchmarks showing if this actually improves problem solving? Or reduces errors?
The idea is cool, but from own experience in harness engineering, lots of cool sounding ideas can have a negative impact on performance due to emergent and confounding effects.
So I'm a bit skeptical!
The TUI and Session manager are straightforward enough. PI is serial by default but can become more DAG-like, where Codex is designed specifically for DAG and this is going more the Codex direction.
I'm much more interested in the memory model and why. As far as I can tell, it's "a vector Db" and not much more is said. Nothing about working memory or procedural memory (there are lots of ways to classify it, https://www.youtube.com/watch?v=BacJ6sEhqMo), but I was disappointed with how "advanced" it seems.
Anyone else have related reading that touches on this? I'm building my own custom harness and want to start implementing loop support, etc. But I also want to build some sort of framework so that it's dynamic (e.g. this needs to run x number of iterations, while planning needs to run y number of iterations).
Check out https://github.com/strongdm/attractor along with the community implementations at https://factory.strongdm.ai/products/attractor#community.
Check out academic papers about:
1. Hierarchical skills, workflow, skill learning 2. Meta Harness, self-learning harnesses 3. Trace/trajectory representation 4. Common agentic benchmarks
But first more basic things like 5. Blog posts form anthropic 6. How Claude Code/PI/ Hermes!! agent works 7. Agent sessions/ Forking/ Hooks
Sounds like you want something similar to /goal mode in Codex.
> The plan is a graph
I much prefer giving the LLM a REPL loop, and injecting all the tools as functions inside the REPL loop.
That means that the LLM isn't constrained to writing a DAG, it can write code that loops, exits early, etc.
Do you have an example?
A graph is a fancy way of saying a few async await calls, which a REPL can do
(We added the same to louie.ai, not complicated)
Good stuff, keeping it
why do i hate skillks, harnesses , memory systems whatever. such ideas that everyone thinks they've discovered but are totally useless in practice.
No clue?
I don't think they are totally usesless.
And its clear that progression is happening on a communith level on all of these and they get integrated later on in commercial offerings like from Anthropic and co.
But also doing a opensource harness and not just giing in to the big companies allows us to have all of this open and transparent and with open models locally.
I'll take the "best way to elicit a clarification response on the internet is to state the opposite confidently" bait...
The example listed in the article -- fanning out a few simple get-population, get-timezone, and make-summary calls -- is, in fact, useless overengineering. This is a basic promise chain with extra steps (priced with tokens).
But as with all software pattern learning, we learn the concepts with simple toy examples that generalize into something bigger. It's the generalization that matters here.
This is talking about a few methods and tricks for spawning effective subagents (collectively, that's the "harness"). Those tips and tricks are nice, but to not be considered useless, we need to make sure we understand why spawning subagents is useful in the first place. Yes parallelism is nice for some tasks, but that's not really what this is about.
The real reason is protecting your context. Yeah, we have 1M context windows that can fit all of LotR in it, but these machines work better when they're narrowly focused. Large context windows run into attention issues and forgetfulness ("Yes, you're right, it was stated I should/n't do X but I ignored it, my bad."). So subagents come into play when you don't want all the tokens associated with a subtask to pollute your main/primary context window and degrade task attention. Split that off to a subagent, let that context navigate the details, and just make sure your main one gets just the input/output blackbox results.
The trick is getting a sense for when the complexity of the task warrants that kind of context protection, vs when a single agent is good-enough. Your toy example will never have enough complexity to warrant the setup, but you might one day find a generalization that may.
so if you don't use skill, harness and memory systems, what do you use?
Because its all moonshining.
Trying to make gold from pyrite.
What each of these is doing, fundamentally, is solving context management in an opinionated way (that and guardrails).
agreed. All of these are trying to get to something that can't really every be achieved with LLMs with is determinism. Folks are trying to constrain the models to behave in a certain way with all of these tools, but there's far too many edge cases for them to be reliable. Doesn't mean they can't add some value, but it seems very limited. Hoping it's only a matter of time before we go back to engineering and step back from "vibes".
I felt that way initially. It is also a headache when you invest a lot in that and need to continually test ripping that stuff out as new models come out. Or in the case of opus 5, Anthropic says ditch it entirely and trust it.
But there is another aspect which I do enjoy which is closer to the feeling of dialing in key bindings in vim or getting a really good rhythm going with your vscode extensions or zhs plugins. It is that level of "I want my system to do exactly this thing in exactly this way" customization that a lot of technical people crave.
And you can do it with harness and context engineering in many cases. In other cases it introduces friction because it will be like "cool, I will only output 15 words max unless told otherwise" and then in the next turn completely disregards it with an "oops, you did tell me to do that didn't you."
And that frustration compounds when older model versions may have done a better job of that but new models are like "thank you for your suggestion, your opinion, while appreciated, is irrelevant. Now let me get back to overspending on your token budget. "
We won't. AI engineering is here to stay whether people like it or not.
See what some guys like Linus Torvalds, or Eric S. Raymond are saying about. It's not so much about "vibes" but using the tool (yes the AI tool) in a certain way that can propel yourself towards your goal at unprecedented speeds.
So AI is mostly a thin glue between deterministic processes. Doesn't change the fact that that is enough to achieve an extremely large amount of tasks.
Wrong. They are commonly used by millions.