Highly recommended read.
This is a topic I have been researching and writing about recently.
The way we use agent memory in harnesses today works, but mostly for simple, one-off, and general tasks.
For more novel, complex, and domain-specific tasks, you will often find that current harness and memory systems don't generalize so well.
So the big question is: how can we encode continual learning capabilities in models and harnesses?
This is where the idea of co-evolving harnesses and models begins to make sense.
This work, along with the benchmark (Continual-ARC), provides an interesting analysis of this.
The harness is not just for steering the model and providing tools and context; it is increasingly becoming a key part of training pipelines to unlock continual learning in models. The harness continues to be important, but it can remain minimal and efficient.