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Agentic AI—So hot right now!

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This is where LAMs come in. They draw on vast libraries of human-like actions to predict and execute the next logical step. However, this predictive ability comes with limitations. Just as LLMs are restricted by the data they’ve been trained on, LAMs can only execute actions they’ve “seen” before. In other words, they’re limited to the range of human examples available for them to learn from.

Here’s the tricky part: Agentic AI isn’t just about automating a single task such as data entry. It aims to automate entire jobs, from beginning to end. While some tasks and roles are clearly unsuitable for full automation, others that seem too complex today might soon fall within the reach of these emerging technologies. The question then becomes: How much of our work should be automated, and what risks come with that shift?

Take professions that involve decision making, creativity, or empathy—fields that have historically been considered beyond the reach of machines. While Agentic AI may not immediately replace these jobs, it could certainly augment them. Imagine a legal assistant who no longer spends hours researching precedents because Agentic AI handles that part of the job. Instead, the assistant can focus on the more nuanced elements of the case.

The dark side of a bright future

Yet, there’s a dark side to this apparently bright future. The more processes and decisions we hand over to AI, the more we risk creating a workforce that’s reliant on automation. In certain cases, this could lead to jobs being fully automated, regardless of whether they should be.

We are in the earliest stages of Agentic AI, and, much like the early days of RPA and GenAI, there’s a lot of excitement but also a lot of uncertainty. While the potential benefits are enormous— streamlined operations, lower costs, fewer human errors—there are equally important concerns about job displacement, bias in AI decision making, and a lack of transparency in how these systems operate.

Furthermore, there’s the issue of data. Remember, LAMs rely on vast libraries of past actions to make decisions. But who will provide these datasets? So far, the companies best positioned to leverage Agentic AI are those with extensive customer logs and business applications—think large enterprises with deep pockets. Small businesses and independent users may find themselves left behind, unable to compete in an increasingly automated world.

Agentic AI holds immense promise, but it also represents a significant leap in our relationship with technology. Whether it becomes a tool to enhance our jobs or a force that replaces them remains to be seen. As with any transformative technology, the key will be finding the right balance—leveraging automation to improve efficiency without losing the human touch.

Are you still hanging in there? Ready or not, the future is coming. The question is: Are we prepared for the agents that will shape it?

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