{"id":24055,"date":"2025-07-25T21:29:26","date_gmt":"2025-07-26T01:29:26","guid":{"rendered":"https:\/\/blog.tmcnet.com\/blog\/rich-tehrani\/?p=24055"},"modified":"2025-07-25T21:32:54","modified_gmt":"2025-07-26T01:32:54","slug":"the-rise-of-ai-agent-washing-separating-hype-from-reality-in-enterprise-ai","status":"publish","type":"post","link":"https:\/\/blog.tmcnet.com\/blog\/rich-tehrani\/ai\/the-rise-of-ai-agent-washing-separating-hype-from-reality-in-enterprise-ai.html","title":{"rendered":"The Rise of AI Agent Washing: Separating Hype from Reality in Enterprise AI"},"content":{"rendered":"\n<p><strong>Key Takeaways:<\/strong><\/p>\n\n\n\n<ul>\n<li>Many enterprise vendors are rebranding basic automation tools and LLM-integrated applications as \u201cAI agents\u201d without meeting core agentic criteria.<\/li>\n\n\n\n<li>True agentic systems must demonstrate autonomy, goal pursuit, reasoning, adaptability, and the ability to operate with minimal human input.<\/li>\n\n\n\n<li>Akhil Sahai, PhD, MBA and CPO at Kanverse.ai, warns that unchecked agent washing undermines trust, misguides buyers, and slows real innovation.<\/li>\n\n\n\n<li>Gartner research shows only a small fraction of claimed \u201cagentic\u201d products currently deliver meaningful agentic functionality.<\/li>\n\n\n\n<li>Leaders are encouraged to ask critical questions to validate vendor claims and avoid investing in mislabeled, underpowered solutions.<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<p>In a recent LinkedIn <a href=\"https:\/\/www.linkedin.com\/pulse\/rise-ai-agent-washing-akhil-sahai-phd-mba-wfoec\/\">essay<\/a> that\u2019s gaining traction across enterprise technology circles, Akhil Sahai, PhD, MBA, and Chief Product Officer at Kanverse.ai, raised an urgent red flag: AI agent washing is here\u2014and it\u2019s spreading fast. Much like the earlier overuse of buzzwords such as \u201cAI-enabled\u201d or \u201ccloud-native,\u201d companies are now attaching the label \u201cAI agent\u201d to systems that fall far short of true autonomous intelligence.<\/p>\n\n\n\n<p>This reminds us a lot of green-washing when many companies claimed their solutions were far more environmentally friendly than they actually were. This was a big concern starting around two-decades ago when measuring carbon footprint and net carbon zero became all the rage.<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"alignright size-full\"><a href=\"https:\/\/blog.tmcnet.com\/blog\/rich-tehrani\/wp-content\/uploads\/2025\/07\/image-101.jpeg\"><img loading=\"lazy\" decoding=\"async\" width=\"270\" height=\"270\" src=\"https:\/\/blog.tmcnet.com\/blog\/rich-tehrani\/wp-content\/uploads\/2025\/07\/image-101.jpeg\" alt=\"\" class=\"wp-image-24056\" srcset=\"https:\/\/blog.tmcnet.com\/blog\/rich-tehrani\/wp-content\/uploads\/2025\/07\/image-101.jpeg 270w, https:\/\/blog.tmcnet.com\/blog\/rich-tehrani\/wp-content\/uploads\/2025\/07\/image-101-90x90.jpeg 90w\" sizes=\"(max-width: 270px) 100vw, 270px\" \/><\/a><figcaption class=\"wp-element-caption\">Akhil Sahai, PhD, MBA, and Chief Product Officer at Kanverse.ai<\/figcaption><\/figure><\/div>\n\n\n<p>According to Sahai, the consequences go beyond marketing hype. Mislabeling tools as agents not only misleads buyers and investors, but it also risks stalling progress in the agentic AI field by blurring the line between genuine innovation and opportunistic repackaging. It wastes resources, invites disappointment, and makes it harder for authentic solutions to break through.<\/p>\n\n\n\n<p>True AI agents, Sahai <a href=\"https:\/\/www.pymnts.com\/news\/artificial-intelligence\/2025\/how-to-recognize-agent-washing-before-ai-leaves-you-out-to-dry\/\">argues<\/a>, are software entities capable of reasoning, planning, adapting, and acting independently. Simply embedding a large language model into a user interface or workflow engine does not qualify. Without core characteristics like goal-directed behavior, contextual reasoning, and minimal reliance on human instruction, these systems are nothing more than smart automations or wrappers around static scripts.<\/p>\n\n\n\n<p>This phenomenon\u2014agent washing\u2014mirrors what we saw in past waves of tech transformation. During the early days of cloud computing, legacy applications were rushed into virtual machines and rebranded \u201ccloud.\u201d During the first phase of AI adoption, every decision tree or chatbot became \u201cAI-powered.\u201d Now, we\u2019re seeing it again: scripted bots, deterministic flows, and API calls presented as full-fledged AI agents.<\/p>\n\n\n\n<p>Sahai is not alone in his concern. Gartner recently reported that of over 2,000 vendors claiming to offer agentic capabilities, only around 130 meet even minimal criteria for autonomy, reasoning, and adaptability. Their analysts expect that nearly 40% of enterprise agent projects will be canceled or fail by 2027 due to misaligned expectations and disappointing results.<\/p>\n\n\n\n<p>The risk is especially high in regulated sectors such as healthcare, finance, and legal services, where buyers rely on claims of intelligent behavior to justify automation. When systems marketed as agents behave like brittle macros or chat interfaces, users lose trust\u2014and future investments stall.<\/p>\n\n\n\n<p>So what exactly makes a system agentic? Sahai offers a simple framework:<\/p>\n\n\n\n<ul>\n<li><strong>Autonomy<\/strong>: Can the system act without continuous human intervention?<\/li>\n\n\n\n<li><strong>Goal orientation<\/strong>: Is it working toward defined objectives, not just executing steps?<\/li>\n\n\n\n<li><strong>Reasoning<\/strong>: Can it evaluate options, make decisions, and adapt plans?<\/li>\n\n\n\n<li><strong>Learning<\/strong>: Does the system improve over time based on experience or feedback?<\/li>\n\n\n\n<li><strong>Coordination<\/strong>: Can it interact with other agents or systems to achieve outcomes?<\/li>\n<\/ul>\n\n\n\n<p>If a product can\u2019t meet these benchmarks, calling it an AI agent is misleading. Worse, it crowds the market and makes it harder for enterprises to identify solutions that truly offer transformative potential.<\/p>\n\n\n\n<p>Sahai encourages business leaders to adopt a more skeptical, structured approach when evaluating \u201cagentic\u201d offerings. Rather than focusing on demos or interface polish, buyers should assess the underlying mechanics: how goals are set, how reasoning is implemented, how failures are handled, and whether the agent can function across dynamic environments without constant reprogramming.<\/p>\n\n\n\n<p>He also points to the need for governance frameworks. Before deploying agents in sensitive business processes, companies must define autonomy limits, escalation protocols, audit logging, and human-in-the-loop checkpoints. Without these safeguards, even well-designed agents can introduce operational risk or compliance violations.<\/p>\n\n\n\n<p>Agent washing also has implications for organizational credibility. Vendors that overstate agent capabilities risk damaging their reputations. Buyers that fall for marketing hype may overspend or underdeliver, eroding stakeholder trust. Over time, such missteps create drag on the entire AI ecosystem.<\/p>\n\n\n\n<p>As Sahai explains, it doesn\u2019t have to be this way. Enterprise leaders can protect themselves\u2014and accelerate real innovation\u2014by asking sharper questions:<\/p>\n\n\n\n<ul>\n<li>What planning models or reasoning frameworks are used under the hood?<\/li>\n\n\n\n<li>Can the system operate in novel contexts or only within predefined scripts?<\/li>\n\n\n\n<li>Is there evidence of learning or performance improvement over time?<\/li>\n\n\n\n<li>What level of autonomy is granted\u2014and how is it constrained?<\/li>\n<\/ul>\n\n\n\n<p>Sahai\u2019s insights echo his earlier writing in Fast Company, where he laid out ten key questions companies should answer before deploying AI agents. That list includes infrastructure readiness, observability, failure handling, coordination mechanisms, and governance principles. Without these foundations, even promising agent pilots are likely to struggle at scale.<\/p>\n\n\n\n<p>Importantly, Sahai does not dismiss the future of agentic AI. On the contrary, he\u2019s a vocal advocate for well-governed autonomy. But he believes the path to maturity depends on clarity. Inflated claims about autonomy might win short-term attention, but they erode long-term confidence. The goal should be sustainable, verifiable progress\u2014built on real capability, not exaggerated promise.<\/p>\n\n\n\n<p>His message to the industry is clear: agentic AI is powerful, but also complex. It requires new thinking around systems design, interaction models, and human oversight. As adoption grows, so does the responsibility to label products accurately, educate stakeholders, and avoid shortcuts that undermine trust.<\/p>\n\n\n\n<p>Looking ahead, Sahai anticipates that only those vendors who build agents with true planning, goal pursuit, and observability will succeed at scale. Others may dominate headlines for a moment, but will ultimately fail when tested by real-world scenarios. It\u2019s not enough to call something an agent\u2014it has to act like one.<\/p>\n\n\n\n<p>For enterprise buyers, this means sharpening their evaluation lens and demanding evidence before making strategic commitments. For vendors, it means doing the hard work of building real agentic systems\u2014and resisting the temptation to rebrand what already exists. And for the industry as a whole, it means treating agentic AI not as a passing trend, but as an architectural shift deserving of precision, transparency, and discipline.<\/p>\n\n\n\n<p><strong>Le<em>arn how AI Agents can supercharge your company\u2019s profits and productivity at&nbsp;<a href=\"http:\/\/www.tmcnet.com\/\">TMC\u2019s&nbsp;<\/a><a href=\"https:\/\/www.aiagentevent.com\/\">AI Agent Event&nbsp;<\/a>in Sept 29-30, 2025 in DC.<\/em><\/strong><\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><a href=\"https:\/\/blog.tmcnet.com\/blog\/rich-tehrani\/wp-content\/uploads\/2025\/07\/AiAgent-500x600-Speaker-logos-v3.jpg\"><img loading=\"lazy\" decoding=\"async\" width=\"600\" height=\"500\" src=\"https:\/\/blog.tmcnet.com\/blog\/rich-tehrani\/wp-content\/uploads\/2025\/07\/AiAgent-500x600-Speaker-logos-v3.jpg\" alt=\"\" class=\"wp-image-23949\"\/><\/a><\/figure><\/div>\n\n\n<p><em>Rich Tehrani serves as CEO of&nbsp;<a href=\"http:\/\/www.tmcnet.com\/\">TMC<\/a>&nbsp;and chairman of&nbsp;<a href=\"http:\/\/www.itexpo.com\/\">ITEXPO<\/a>&nbsp;#TECHSUPERSHOW Feb 10-12, 2026 and is CEO of&nbsp;<a href=\"https:\/\/www.rt-advisors.com\/\">RT Advisors<\/a>&nbsp;and is&nbsp;a Registered Representative (investment banker) with and offering securities through&nbsp;<a href=\"https:\/\/www.4pointscapital.com\/\">Four Points Capital Partners LLC&nbsp;<\/a>(Four Points) (Member FINRA\/SIPC). He handles capital\/debt raises as well as M&amp;A. RT Advisors is not owned by Four Points.<\/em><\/p>\n\n\n\n<p>The above is not an endorsement or recommendation to buy\/sell any security or sector mentioned. No companies mentioned above are current or past clients of RT Advisors.<\/p>\n\n\n\n<p>The views and opinions expressed above are those of the participants. While believed to be reliable, the information has not been independently verified for accuracy. Any broad, general statements made herein are provided for context only and should not be construed as exhaustive or universally applicable.<\/p>\n\n\n\n<p><em>Portions of this article may have been developed with the assistance of artificial intelligence, which may have contributed to ideation, content generation, factual review, or editing<\/em>.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Key Takeaways: In a recent LinkedIn essay that\u2019s gaining traction across enterprise technology circles, Akhil Sahai, PhD, MBA, and Chief Product Officer at Kanverse.ai, raised an urgent red flag: AI agent washing is here\u2014and it\u2019s spreading fast. Much like the earlier overuse of buzzwords such as \u201cAI-enabled\u201d or \u201ccloud-native,\u201d companies are now attaching the label<\/p>\n","protected":false},"author":44,"featured_media":24058,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[194],"tags":[],"post_mailing_queue_ids":[],"_links":{"self":[{"href":"https:\/\/blog.tmcnet.com\/blog\/rich-tehrani\/wp-json\/wp\/v2\/posts\/24055"}],"collection":[{"href":"https:\/\/blog.tmcnet.com\/blog\/rich-tehrani\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blog.tmcnet.com\/blog\/rich-tehrani\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blog.tmcnet.com\/blog\/rich-tehrani\/wp-json\/wp\/v2\/users\/44"}],"replies":[{"embeddable":true,"href":"https:\/\/blog.tmcnet.com\/blog\/rich-tehrani\/wp-json\/wp\/v2\/comments?post=24055"}],"version-history":[{"count":2,"href":"https:\/\/blog.tmcnet.com\/blog\/rich-tehrani\/wp-json\/wp\/v2\/posts\/24055\/revisions"}],"predecessor-version":[{"id":24059,"href":"https:\/\/blog.tmcnet.com\/blog\/rich-tehrani\/wp-json\/wp\/v2\/posts\/24055\/revisions\/24059"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/blog.tmcnet.com\/blog\/rich-tehrani\/wp-json\/wp\/v2\/media\/24058"}],"wp:attachment":[{"href":"https:\/\/blog.tmcnet.com\/blog\/rich-tehrani\/wp-json\/wp\/v2\/media?parent=24055"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blog.tmcnet.com\/blog\/rich-tehrani\/wp-json\/wp\/v2\/categories?post=24055"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blog.tmcnet.com\/blog\/rich-tehrani\/wp-json\/wp\/v2\/tags?post=24055"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}