Artificial Intelligence · 22.08.2026, 07:46 UTC
The Evolution of the Agent Harness
| Schweregrad | info |
|---|---|
| Kategorie | Artificial Intelligence |
| Quelle | Latent Space ↗ |
| Veröffentlicht | 22.08.2026 UTC |
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Sometime around Christmas 2025, AI engineers noticed a change in agents. They started to work! It’s hard to pin down exactly why. Maybe we finally had holiday downtime to try the newest agents with the newest models. Maybe the models had crossed some capability threshold. Maybe the wrappers around the models had matured.What I’ll argue in this post is that it was the confluence of the last two. The model and the harness improving together and then their curves of improvement crossing at the right moment. And that dynamic helps to explain what comes next: models keep absorbing the harness into their weights, engineers keep deleting what got absorbed, and what remains is a harness for human attention rather than for the model.Lukasz Kaiser, one of the people who invented the Transformer, said on “Unsupervised Learning” in June:“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 answer to “What happened?” isn’t solely in the model weights. It’s in the system that grew up around the weights.The answer is in the agent harness.Think back to November 2022, when ChatGPT was the most advanced AI tool. The only capability at its disposal was next-token prediction and some Reinforcement Learning from Human Feedback (RLHF) that allowed it to act like a helpful assistant. No tools, no search, and no reasoning.The original ChatGPT was confined to its training data and the prompt you sent …