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nb: current models (e.g. GPT 5.6 Sol) are very good at long horizon tasks

they no longer need crutches or rube goldberg machines to keep them going

minimal agent harness is just a loop that loops until no more tool calls are coming

GPT 5.6 Sol continues to drive the loop until the task is done or it decides that it wants to present the user with information

at that point it is probably good to not automatically continue (!)

(YMMV of course, for some tasks it makes sense, then you can still add a loop around it + the necessary signals, the main thing I want to say is that what used to be essential to keep models going is no longer needed, current models can do long-horizon tasks way better than when these outer loops where necessary)

self-plug: "smol", is a minimal agent in ~20 lines of Go that implements this pattern (keeps going until no more tool calls):

https://github.com/smol-env/smol

works just fine

2 comments

In my daily work, I found expeciall terra and sol now stopping every few rounds again, telling me the tak is done. I even had them create a detailled plan - and told them to finish "end to end" - and they appruptly stop after the plan. Because they interpret this as finished. Even if the DOD is clearly not "finish the plan".

The new models are shite (pardon my French), when it comes to long running tasks and I find myself more and more using open wheights models or switching back to gpt-5.5 for "real work".

This might be the fact, that i use them for non coding work. But the degradation between 5.5 and 5.6 is stark in my daily work.

As always with AI - everybody's mileage will vary.

Interesting, that is not my experience (but I'm mainly using them for reading and writing code right now)

but I don't doubt that you're seeing this behaviour, ty for sharing!

Forgive me if a dumb question, but is there no compaction? Do you ever run out of context?

great question, not yet

I think it is worth adding support for it though