The Evolution of the Agent Harness
Models absorbing harness capabilities into weights is reshaping agent architecture toward human-attention scaffolding
“The change last winter, last Christmas — it's a little hard to pin down. I mean, the harness changed and a little post-training changed and then new pre-trained models came… but it felt like a big jump which is not that easy to pin down what did it.”
The Latent Space Blog argues that the Christmas 2025 AI agent capability jump resulted from model improvements and harness maturation crossing simultaneously, not models alone. The thesis predicts a structural shift: as models absorb harness logic into their weights, engineers will progressively delete scaffolding, leaving behind a harness designed to manage human attention rather than model behavior. Transformer co-inventor Lukasz Kaiser corroborates that the jump was real but causally murky, validating the multi-factor framing.