Harrison Chase argues that eval-driven development works for narrowly scoped tasks but breaks down for more autonomous agents, invoking Goodhart's Law that a measure ceases to be useful once it becomes a target. He asks how such agents can be hill-climbed, and the post does not provide an answer.
This is great point
Goodharts law: when a measure becomes a target, it ceases to be a good measure
Initial take: eval driven development works for narrowly scoped things, but for more autonomous agents it doesn’t
So then - how do you hill climb those?
1. Create new high-value evals 2. Realize you need to fight against Goodhart's Law 3. Go to 1View quoted post on X
Source: Harrison Chase · x.comPublished · added here
