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From my own experience building LLM augmented systems this year, I've been telling a lot of my colleagues that most use cases don't really need an "agent". Instead, they need a well defined product workflow with models(llms etc.) in the mix. It's reassuring to read the recent @anthropic.com blog...

...where they walk readers cleanly through a lot of this noise (right now & probably all of 2025). I really love the fact that they keep iterating for simplicity. When starting out, there's no good reason to inherit complexity(langchain etc.), esp. if you plan to have end-to-end control over it.

I appreciate that they've introduced names for some common workflows. I've already been using Prompt Chaining & Routing, considering Parallelization next. More importantly, notice that they're not new hypey names, these flows already existed in the data engineering world.

And then, there's a pretty good coverage on building agents. Once again, after a good amount of warning that most systems don't need to be designed this way. Sure, agentic systems sound more fun. But, from what I see, most real world problems don't need autonomous plan-attempt-verify loops.

Finally, the blog summarizes by once again warning against complexity. LLM augmented systems are already hard enough to evaluate, it's only sensible to keep things simple outside the LLM boundaries.

I probably read this blog multiple times today. You can really tell that it's written by a team that cares about helping engineers build simple & reliable systems. Thanks again to the authors Erik Schluntz and Barry Zhang. I wish more of the @anthropic.com team would be here on Bsky.