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by OleksandrC·27d ago·view on hn ↗
The article makes a point about agent harnesses being sticky (the supposed moat). I have been building my own agent harness for a while, and I can tell with confidence that the harness almost does not matter, the entirety of the AI magic is the model itself. The harness can be almost barebones (like, for example, mini-swe-agent used for benchmarks), and yet the model still does the task just fine.

So from my perspective, it's doubtful that this is the moat. Besides, for example, Claude Code in particular is so buggy (and always has been).

6 comments
By the harness I believe he means the entire end-user product experience, not specifically the harness code. I’ve mostly stuck with codex because their Mac app is better and I’ve gotten used to running automations through it. The more workflows they can build around this (design tools, collaboration, etc), the better chance of lock-in.

He talks about this in another recent essay https://stratechery.com/2026/anthropics-safety-superpower/

> If you own the user touchpoint, then you have meaningful lock-in, and the best way to own the user touchpoint is to be the canvas for everything they need to do. This, by extension, means that the frontier labs are on a collision course with software companies: it’s software that owns the user touchpoint, and it’s in the frontier labs’ long-term interest to not simply be a commodity input into software but to simply replace software outright.

For me, harnesses are mostly sticky insofar as the model providers only allow you to use their subsidized plans through their own harnesses, unfortunately. But of course switching model + harness is an option.
> model providers only allow you to use their subsidized plans through their own harnesses

true for anthropic, not true for openai.

Facts, I was able to code a personal self improving harness in a weekend (something a bit more similar to Hermes or OpenClaw at the time but with a more expansive set of features for my use cases and requirements) and it works great for 90% of the tasks I would use Claude Code or Codex (now ChatGPT App) for, with the remaining 10% being able to be implemented with a few more prompts from within the harness itself.

For this reason alone I would also argue that the idea about an agent harness being sticky is a non-starter long-term.

The harnesses will tend towards commoditization, but for now the harness quality matters a lot. Especially for non terminal harnesses.
we've barely scratched the surface when it comes to the available design space of agent harnesses.

i also expect you'll see markedly different results if you constrain yourself to small models. there even trivial harness improvements like Codex's /goal feature, and more capable basic tooling (e.g. semantic code grep, js-capable `fetch` tooling) make or break the actual task success rate.

Yeah it certainly feels like the harnesses are pretty minimal value add on the token pipe