alt Hacker News> because it has business context
It doesn't because it doesn't learn. Every time you run it, it's a new dawn with no knowledge of your business or your business context
> better reasoning
It doesn't have better reasoning beyond very localized decisions.
> and can ask humans for clarification and take direction.
And yet it doesn't, no matter how many .md file you throw at it, at crucial places in code.
> We have clear scaling laws on true statistical performance that is monotonically related to any notion of what performance means.
This is just a bunch of words stringed together, isn't it?
Replies
Almost every task that people are tackling agents on, it’s either not worth doing, can be done better with scripts and software, or require human oversight (that negates all the advantages.
> It doesn't because it doesn't learn. Every time you run it, it's a new dawn with no knowledge of your business or your business context
It does learn in context. And lack of continuous learning is temporary, that is a quirk of the current stack, expect this to change rather quickly. Also still not relevant, consider that agentic systems can be hierarchical and that they have no trouble being able to grok codebases or do internal searches effectively and this will only improve.
> It doesn't have better reasoning beyond very localized decisions.
Do you have any basis for this claim? It contradicts a large amount of direct evidence and measurement and theory.
> This is just a bunch of words stringed together, isn't it?
Maybe to yourself? Chinchilla scaling laws and RL scaling laws are measured very accurately based on next token test loss (Chinchilla). This scales very predictably. It is related to downstream performance, but that relationship is noisy but clearly monotonic