Have this shortcomings of llms been addressed by better models or by better integration with other tools?
Like, are they better at coding because the models are truly better or because the agentic loops are better designed?
They can and are improved (papered over) over time. For example by improving and tweaking the training data. Adding in new data sets is the usual fix. A prime example 'count the number of R's in Strawberry' caused quite a debacle at a time where LLM's were meant to be intelligent. Because they aren't they can trip up over simple problems like this. Continue to use an army of people to train them and these edge cases may become smaller over time. Fundamentally the LLM tech hasn't changed.
I am not saying that LLM's aren't amazing, they absolutely are. But WHAT they are is an understood thing so lets not confuse ourselves.
The harnesses have helped in training the models themselves (i.e. every good trace was "baked in" the model) and have improved in enabling test time compute. But at the end of the day this is all put back into the models, and they become better.
The simplest proof of this is on benchmarks like terminalbench and swe-bench with simple agents. The current top models are much better than their previous versions, when put in a loop with just a "bash tool". There's a ~100LoC harness called mini-swe-agent [1] that does just that.
So current models + minimal loop >> previous gen models with human written harnesses + lots of glue.
> Gemini 3 Pro reaches 74% on SWE-bench verified with mini-swe-agent!