And IMHO while humans are still in charge of these processes, the way that most people can best design and explain what they want is via conversation, exploration, iteration, etc. Not by a single fire-and-forget prompt!

This is as far as the eye can see all very iterative, there is no tail-call or anything fancy, it is loops. Also "Recursive" I feel kind of tries to imply the LLM's weights are being pushed around in some feedback, because to recurse you have to invoke the thing you're recursing into at its very start right? That would be reinforcement probably, and there is none of that in any such RSI so far, at least not public. Please someone contradict me with examples.

So please: "ISI" for iterative self improvement is fine. Also "ISI" does not fit the (outdated!) Vernon Vinge "singularity" trope, and that is a good thing!

1/ Allowing me to easily plug in any harness, using any provider, and make it a first-class worker. Omnigent has out of the box ACP support and it's trivial to use that to add first-class support for any harness out there. I love the ability to have CC + Opus plan, Codex + Luna implement, Pi + Qwen 3.8 give a tie-breaking opinion on a design decision that Opus flagged and Grok and Codex couldn't agree on, all orchestrated by a model of my choosing from any provider using Omnigent's main agent harness.

2/ Reusable agent systems rather than just reusable workflows. You can define agents in YAML whose subagents embody particular roles, with different models/harnesses, skills, plugins, tools, etc. preconfigured for each one.

Of course, claude workflows are now durable but Omnigent's agnt definitions are a bit more abstract in that they define the subagents that are available and how they should work by default rather than the workflow itself (i.e. the specific JTBD). If I have a common workflow that consists of, for example, Sol + Codex writing some script to scrape some data, Pi + a cheap DeepSeek-tier model formatting that data en masse, then Fable + CC doing some advanced analysis on it, I can embody that with a yaml agent definition that I can then use to run with my task of the day as a prompt. All of this is orchestrated by a model of my choice using Omnigent's harness.

This might look like: 'smart scraping agent with all sorts of scraping skills and tools pre-loaded', a 'bulk data processing agent with a cheap, fast model and plenty of pandas/numpy skills preloaded', and 'frontier model to interpret and reason on the implications of the processed data'. The main agent would have instructions about the general workflow of such tasks and when to invoke and delegate tasks to which subagent. The definition describes the workers available to the orchestrator and how they should generally behave, rather than hard-coding the workflow itself. I love that I can create those definitions and re-use them.

All that said, I am sure the labs will come up with their own similar products to (2) (e.g. dots today). I also recently noticed that Claude Code now has subagent 'teams' rather than just 'general-purpose'/'explore' subagents, and these seem to be longer-lived. This seems to be encroaching on the agent yaml definitions, albeit with less fine-grained control on my end. Therefore, the tl;dr (for me at least) is vendor neutrality; I don't think we'll ever see a product coming out of a frontier lab that eagerly delegates a task to their competitor's model (and bank account).

To me the really bad sign is that this is the benchmark that they selected to highlight. Why not one of the less-saturated benchmarks where this harness could (in theory) show meaningful improvement over the OpenCode baseline? Seems fishy to me.