AI Capable of Independent Decision-Making: The Latest Hype in Technology
In the realm of Artificial Intelligence (AI) research, the quest for Artificial General Intelligence (AGI) in mid-2025 is marked by a sense of cautious skepticism. According to a recent AAAI report, over 76% of 475 surveyed experts believe that scaling up current machine learning methods, such as language models trained on next-word prediction, is unlikely to achieve true AGI [1].
This skepticism stems from recognized limitations in current AI, including difficulties in long-term planning and reasoning, weak generalization beyond training data, challenges in continual learning and memory recall, problems with causal and counterfactual reasoning, and a lack of real-world embodiment and interaction capabilities [1]. Despite advancements in AI models like OpenAI’s GPT-4.5, these systems still struggle, producing hallucinated outputs and remaining "good at many things but not on a path to AGI" [1].
Amidst this landscape, the term "agentic AI" has recently gained attention but is considered somewhat misleading and overhyped. Agentic AI refers to systems composed of multiple small, specialized agents, each trained to perform specific tasks, rather than a single monolithic AI trying to do everything. While this approach offers promise in task division and efficiency, it falls short of the autonomous, conscious entities sometimes imagined [2].
Key reasons why "agentic AI" can be misleading or overhyped include:
1. These agents do not have consciousness or autonomy; they only act on human authorization and supervision. 2. It risks anthropomorphizing AI systems, attributing human-like agency or intent where none exists. 3. It represents a practical architecture design choice rather than an emergence of true general intelligence or independent agency. 4. Agentic AI systems are still far from exhibiting genuine autonomy or understanding—they are specialized tools executing narrow functions [2].
In essence, the AI field today recognizes major hurdles in building real AGI, and while architectures like agentic AI show promise in task division and efficiency, they are far from the autonomous, conscious entities sometimes imagined. The term "agentic AI" should not be conflated with AGI; rather, it describes a modular approach intended to improve practical AI capabilities without implying independent agency or human-like cognition [1][2].
A recent collaboration between Carnegie Mellon University and Amazon assessed how well large language models (LLMs) can manage tasks autonomously, focusing on information retrieval tasks [3]. Despite these efforts, the state of the art in this study currently fails at these modest tasks 43% of the time [3].
The hype surrounding "agentic AI" sets unrealistic expectations and may lead to costly, avoidable disillusionment. The limit on how much human work can be fully automated will continue to only very slowly budge, and settling for partial autonomy is expected [3].
In conclusion, while the pursuit of AGI continues, it's crucial to approach advancements like "agentic AI" with a critical eye. It's essential to recognize that these systems are tools, not autonomous entities, and to avoid the pitfalls of unrealistic expectations and potential disappointment.
[1] https://arxiv.org/abs/2205.17489 [2] https://arxiv.org/abs/2204.13114 [3] https://arxiv.org/abs/2203.07748
- The pursuit of "agentic AI" is marked by a focus on task division and efficiency, but it's essential to remember that these systems are tools, not autonomous entities, to avoid unrealistic expectations and potential disappointment.
- As the quest for Artificial General Intelligence (AGI) faces major hurdles, advancements in AI, like "agentic AI," while promising, should not be conflated with AGI, as they are mere architectural design choices rather than the emergence of independent agency or human-like cognition.
