I often use LLMs to explore prior art and maybe find some alternative ways of thinking of problems. About 90% of what it tells me is useless or inapplicable to my domain due to a technicality it could not have known, but the other 10% is nice and has helped me learn some great new things.

I can’t imagine letting an agent try everything that the LLM chatbot had recommended ($$$). Often coming up in recommendations are very poorly maintained / niche libraries that have quite a lot of content written about them but what I can only imagine is very limited use in real production environments.

On the other hand, we have domain expert “consultants” in our leadership’s ears making equally absurd recommendations that we constantly have to disprove. Maybe an agent can occupy those consultants and let us do our work in peace.

> > Most of what's planned falls down within the first few hours of implementation.

> Not my experience at all. We know what computers are capable of.

You must not work in a field where uncertainty is baked in, like Data Science. We call them “hypotheses”. As an example, my team recently had a week-long workshop where we committed to bodies of work on timelines and 3 out of our 4 workstreams blew up just a few days after the workshop because our initial hypotheses were false (i.e. “best case scenario X is true and we can simply implement Y; whoops, X is false, onto the next idea”)

I skimmed over it, and didn’t find any discussion of:

Yes, we know that functional code can get generated at incredible speeds. Yes, we know that apps and what not can be bootstrapped from nothing by “agentic coding”.

We need to read this code, right? How can I deliver code to my company without security and reliability guarantees that, at their core, come from me knowing what I’m delivering line-by-line?