Yeah for sure - that's exactly why we use that approach - it's unsurprising, simple and definitely works.
One difference is that you don't necessarily need structured data in, just output validation from the LLM. This is a big difference from ML because you're not having to worry too much about doing complex data engineering - or at least it solves the annoying ingestion problem in many cases.
Another observation is that most businesses don't have any ML engineering capabilities in-house - they're pretty much willing to pay a premium, because unlike the bespoke ML solutions, you can just do it with an off-the-shelf system (provided it's designed with the right validation loops).
The agent is in some ways an abstraction that just enables use and adoption - even if it would be orders of magnitude worse than normal ML solutions - it's competing against no solution, not ML-based ones.
Last thing is just around what level of autonomy people expect from these things. You can go pretty far, but like flipping N coins, the more you flip the greater the chance that something goes awry. Agents still need a lot of guidance and it's up to the system builders to bring that to them, either by connecting humans or very tightly integrated, well-designed tools.
One difference is that you don't necessarily need structured data in, just output validation from the LLM. This is a big difference from ML because you're not having to worry too much about doing complex data engineering - or at least it solves the annoying ingestion problem in many cases.
Another observation is that most businesses don't have any ML engineering capabilities in-house - they're pretty much willing to pay a premium, because unlike the bespoke ML solutions, you can just do it with an off-the-shelf system (provided it's designed with the right validation loops).
The agent is in some ways an abstraction that just enables use and adoption - even if it would be orders of magnitude worse than normal ML solutions - it's competing against no solution, not ML-based ones.
Last thing is just around what level of autonomy people expect from these things. You can go pretty far, but like flipping N coins, the more you flip the greater the chance that something goes awry. Agents still need a lot of guidance and it's up to the system builders to bring that to them, either by connecting humans or very tightly integrated, well-designed tools.