Mildly interesting article - I mean, you can already run a ton of libraries that talk to an inference backend. The only difference here is that the client-side code is in Python, which by itself doesn't make creating agents any simpler - I would argue that it complicates things a tone.
Also, connecting a model to a bunch of tools and dropping it into some kind of workflow is maybe 5% of the actual work. The rest is spent on observability, background tasks, queueing systems, multi-channel support for agents, user experience, etc., etc., etc.
Nobody talks about that part, because most of the content out there is just chasing trends - without much real-world experience running these systems or putting them in front of actual customers with real needs.
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