I’ve been following how AI systems are shifting from pure text-based responses toward actually executing tasks inside tools and platforms. I was reading this explanation of Large Action Models and how they coordinate multi-step actions instead of just generating answers: https://www.trinetix.com/insights/what-are-large-action-models-and-how-do-they-work. It made me think about a small internal automation project I worked on where we chained a few API calls together, but it was still very rule-based and fragile. The idea of models dynamically planning and executing actions feels powerful, but also a bit risky in production environments. How do you think teams should approach introducing this kind of “action-based AI” without breaking existing workflows?
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I don’t work in AI development, but I’ve been noticing these discussions more often lately because automation tools are becoming common in non-technical teams too. From my perspective in operations, what stands out is that people tend to assume “automation” means everything becomes hands-off, but in reality there’s usually still a lot of checking and manual correction involved. Even small automated processes can create confusion if outputs aren’t clearly explained or if something unexpected happens. So while I don’t deal with the technical side, I can definitely see how important it is to keep humans in the loop when systems start making decisions on their own.