alt Hacker NewsRelated thing is that LLMs are still notoriously bad at writing instructions for themselves (so managing themselves also). They tend to overspecify. If I had a copy of myself I would tell him "do the thing", but agents say "do the thing like this and this, it must be such and such, use X and put it together that way". The subagent is just as intelligent but now it's constrained, sometimes as you start developing something you discover some things are better organized in a different way etc. So it spends much time figuring out how to deliver exactly what was asked even if it's not optimal because that's the task.
Basically a company where you can't ever question decisions of the higher ups, not even ask for clarification, but they force implementation choices on you. I mean, it definitely does happen, but the results are usually not great. Not alawys. For some tasks it works.
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> The subagent is just as intelligent
Isn't it a common practice to use cheaper models to do the grunt work?
> Related thing is that LLMs are still notoriously bad at writing instructions for themselves (so managing themselves also).
Isn't this odd? This seems to be the #1 task that the major labs should be optimizing, since it makes agent so much more effective.
> They tend to overspecify. If I had a copy of myself I would tell him "do the thing", but agents say "do the thing like this and this, it must be such and such, use X and put it together that way". The subagent is just as intelligent but now it's constrained, sometimes as you start developing something you discover some things are better organized in a different way etc. So it spends much time figuring out how to deliver exactly what was asked even if it's not optimal because that's the task.
Isn't this just the planning pattern, common for agentic work? (first plan then implement.. with things like /plan, or a planning skill, with or without creating a PLAN.md). If this methodology is wrong when an agent prompt another agent, it's also wrong when the human runs /plan manually..
And Indeed, I always thought it was odd to constrain the implementing agent like that, because common sense is that we discover things as we implement. Perhaps we should prompt the agent to communicate back, as they implement, any shortcomings in the plan itself - such as things they learned while implementing that contradicts the plan.