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Originally published in\u00a0Forbes<\/a><\/p>\n The hype term \u201cagentic AI\u201d is the latest trending buzzword to repackage pie in the sky AI ambitions, but it does not allude to any particular advancement that might achieve them. It amplifies the overpromising narrative that we\u2019re rapidly headed toward a great leap in autonomy \u2013 most extraordinarily, toward the most audacious goal of all,\u00a0artificial general intelligence<\/a>, the speculative idea of machines that could automate virtually all human work.<\/p>\n Setting unrealistic expectations compromises real value.\u00a0Generative AI and predictive AI<\/a> deliver concrete opportunities that will continue to grow, but the claim that technology will soon hold \u201cagency\u201d is the epitome of vaporware. It only misleads, setting up the industry for costly, avoidable disillusionment.<\/p>\n Most high-tech terms \u2013 such as machine learning, predictive modeling or autonomous driving \u2013 are legit. They represent one of two things: a specific technical approach or a novel goal for technology. But the terms \u201cagent\u201d and \u201cagentic\u201d fail in both respects: 1) Most uses of \u201cagentic\u201d do not refer to any novel technical methodology and 2) the ambition of increasing autonomy is not new \u2013 even as the word falsely implies otherwise on both accounts. Here\u2019s a breakdown of those two failings and their ramifications.<\/p>\n 1) \u201cAgentic\u201d Does Not Refer To Any Particular Technology Or Advancement<\/p>\n \u201cNothing draws a crowd quite like a crowd.\u201d \u2014P.T. Barnum, 19th century circus showman famed for hoaxes<\/p>\n \u201cAgentic AI\u201d poses as a credible near-term capability, but it represents only the most self-evident goal there could be for technology \u2013 increased automation \u2013 not a means to get there. Sure, we\u2019d like a large language model to complete monumental tasks on its own \u2013 including gathering and assimilating information and completing online tasks and transactions \u2013 but labeling such ambitions as \u201cagentic\u201d does not make them more feasible.<\/p>\n The term \u201cagentic AI\u201d intrinsically misleads. Its sheer popularity widens the belief that technology will soon become capable of running much more autonomously, but the buzzword does not refer to any particular technical approach that may get us there. Its trendiness serves to institutionalize the notion that we\u2019re nearing great new levels of automation \u2013 \u201cagentic AI\u201d is so ubiquitous that it may sound \u201cestablished\u201d and \u201creal\u201d \u2013 and this implies the existence of a groundbreaking advancement where in fact there is none.<\/p>\n Despite the fact that the vast majority of press about \u201cagentic AI\u201d only promotes this hype narrative with no substance to support it, autonomy itself is often a worthy goal and researchers are conducting valuable work in the pursuit of increasing it. For example, a recent\u00a0collaboration between Carnegie Mellon University and Amazon<\/a> curates a large testbed of modest tasks in order to assess how well LLMs can manage them autonomously. This study focuses on information retrieval tasks, such as \u201cRetrieve an article discussing recent trends in renewable energy from The Guardian\u201d and \u201cRetrieve a publicly available research paper on quantum computing from MIT\u2019s website.\u201d The study evaluates clever approaches for using LLMs to navigate websites and automatically perform such tasks, but I would not say that these approaches constitute groundbreaking technology. Rather, they are ways to leverage what is already groundbreaking: LLMs. As the study reveals, the state of the art currently fails at these modest tasks 43% the time.<\/p>\n 2) \u201cAgentic\u201d Presents No New Goal Or Purpose<\/p>\n \u201cAgentic AI\u201d spotlights machine autonomy as if it were a new ambition, but it\u2019s an old, self-evident goal. There\u2019s no new, revolutionary thrust at play. While the buzzword is somewhat\u00a0malleable and fuzzy<\/a>, it generally refers to the desire for increased autonomy \u2013 \u201cagentic AI\u201d means hypothetical machines that could perform substantial tasks on their own. This has always been a core, fundamental objective. The very purpose of any machine is to automate some or all of what would otherwise be carried out by a person or animal. Put another way, we build machines to do stuff.<\/p>\n By reiterating our innate desire to automate, \u201cagentic\u201d only states the obvious. Sure, the more machines can safely do for us, the better. But there\u2019s a fairly stubborn limit to the scope of tasks that can be fully automated with no human in the loop. For example, predictive AI instantly\u00a0decides whether to allow each credit card charge<\/a>, whereas the wholesale replacement of physicians with machines is a very long way off at best. \u201cAgentic AI\u201d is as redundant as \u201cevil Sith Lord,\u201d \u201cbook library\u201d or \u201cdata science.\u201d<\/p>\n To be clear, autonomy is often a worthy goal and there is potential for LLMs to excel, at least where the scope of automation is somewhat modest. Economic interests exert pressure to increase autonomy \u2013 and various societal concerns exert pressure in\u00a0both<\/a>\u00a0directions<\/a>. But the scope of unleashed machine autonomy only increases quite slowly. One reason is that technology doesn\u2019t improve as quickly as advertised. Another is that cultural and societal inertia tends to spell slow adoption.<\/p>\n The Farfetched Notion Of Machine \u201cAgency\u201d<\/p>\n There\u2019s another problem with using the words \u201cagent\u201d and \u201cagentic\u201d to evoke the goal of autonomous machines: Crediting machines with \u201cagency\u201d is fantastical. This doubles down on AI\u2019s core mythology and original sin, the anthropomorphization of machines. The machine is no longer a tool at the disposal of humans \u2013 rather, it\u2019s elevated to have its own human-level understanding, goal-setting and volition. It\u2019s our peer. Essentially, it\u2019s alive.<\/p>\n The spontaneous goal-setting that comes with agency \u2013 and its resulting unbottleability \u2013 have been seeping into the AI narrative for years. \u201cAI that works doesn\u2019t stay in a lab,\u201d\u00a0writes Kevin Roose<\/a> in\u00a0The New York Times. \u201cIt makes its way into weapons used by the military and software used by children in their classrooms.\u201d In\u00a0another article<\/a>, he wrote, \u201cI worry that the technology will\u2026 eventually grow capable of carrying out its own dangerous acts.\u201d Likewise, Elon Musk, one of the world\u2019s most effective transmitters of AGI hype, announced safety assurances that cleverly imply a willful or dangerous AI. He says that his company\u2019s forthcoming humanoid robot will be hardwired to obey whenever anyone says, \u201cStop, stop, stop<\/a>.\u201d<\/p>\n The story of technology taking on a life of its own is an age-old drama. We need to see this high tech mythology for what it is: a more convincingly rationalized ghost story. It\u2019s the novel Mary Shelley would have written had she been familiar with algorithms. The implausible, unsupported notion that we\u2019re actively progressing toward AGI \u2013 aka artificial humans \u2013\u00a0underlies much of the hype<\/a> (and often\u00a0overlays it explicitly<\/a> as well). \u201cAgentic\u201d invokes this narrative.<\/p>\n Despite the unprecedented capabilities \u2013 and uncanny,\u00a0seemingly humanlike qualities<\/a> \u2013 of generative AI, the limit on how much human work can be fully automated will continue to only very slowly budge. I believe that we will generally need to\u00a0settle for partial autonomy<\/a>.<\/p>\n Don\u2019t buy \u201cagentic AI\u201d and don\u2019t sell it either. It\u2019s an empty buzzword that, in most uses, overpromises. The AI industry runs largely \u2013 although certainly not entirely \u2013 on hype. To the degree that it continues to overinflate expectations, the industry will ultimately face a commensurate burst bubble: the dire disillusionment and unfulfilled debt that result from unmet promises.<\/p>\n I have followed up on this topic with a second article, \u201cThe Agentic AI Hype Cycle Is Out Of Control \u2014 Yet Widely Normalized<\/a>.\u201d<\/p>\n \u00a0<\/p>\n About the author
Eric Siegel is a leading consultant and former Columbia University professor who helps companies deploy machine learning. He is the founder of the long-running\u00a0Machine Learning Week<\/a> conference series, the instructor of the acclaimed online course \u201cMachine Learning Leadership and Practice \u2013 End-to-End Mastery<\/a>,\u201d executive editor of\u00a0The Machine Learning Times<\/a>\u00a0and a\u00a0frequent keynote speaker.<\/a>\u00a0He wrote the bestselling\u00a0Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die<\/a>, which has been used in courses at hundreds of universities, as well as\u00a0The AI Playbook: Mastering the Rare Art of Machine Learning Deployment<\/a>. Eric\u2019s interdisciplinary work bridges the stubborn technology\/business gap. At Columbia, he won the Distinguished Faculty award when teaching the graduate computer science courses in ML and AI. Later, he served as a business school professor at UVA Darden. Eric also publishes\u00a0op-eds on analytics and social justice<\/a>. You can follow him on\u00a0LinkedIn<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"Originally published in\u00a0Forbes The hype term \u201cagentic AI\u201d is the latest trending buzzword to repackage pie in the…\n","protected":false},"author":2,"featured_media":91405,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[45],"tags":[182,10690,181,507,59690,40519,62373,62375,62374,74],"class_list":["post-91404","post","type-post","status-publish","format-standard","has-post-thumbnail","category-artificial-intelligence","tag-ai","tag-analytics","tag-artificial-intelligence","tag-artificialintelligence","tag-data-mining","tag-data-science","tag-predictive-analytics","tag-predictive-analytics-jobs","tag-predictive-analytics-news","tag-technology"],"_links":{"self":[{"href":"https:\/\/www.newsbeep.com\/us\/wp-json\/wp\/v2\/posts\/91404","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.newsbeep.com\/us\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.newsbeep.com\/us\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/us\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/us\/wp-json\/wp\/v2\/comments?post=91404"}],"version-history":[{"count":0,"href":"https:\/\/www.newsbeep.com\/us\/wp-json\/wp\/v2\/posts\/91404\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/us\/wp-json\/wp\/v2\/media\/91405"}],"wp:attachment":[{"href":"https:\/\/www.newsbeep.com\/us\/wp-json\/wp\/v2\/media?parent=91404"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.newsbeep.com\/us\/wp-json\/wp\/v2\/categories?post=91404"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.newsbeep.com\/us\/wp-json\/wp\/v2\/tags?post=91404"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}