{"id":6705,"date":"2026-07-26T09:49:54","date_gmt":"2026-07-26T13:49:54","guid":{"rendered":"https:\/\/workai.tv\/news\/2026\/07\/ai-agents\/smaller-smarter-safer-how-to-build-agentic-ai-on-the-right-foundation\/"},"modified":"2026-07-26T09:49:54","modified_gmt":"2026-07-26T13:49:54","slug":"smaller-smarter-safer-how-to-build-agentic-ai-on-the-right-foundation","status":"publish","type":"post","link":"https:\/\/workai.tv\/news\/2026\/07\/ai-agents\/smaller-smarter-safer-how-to-build-agentic-ai-on-the-right-foundation\/","title":{"rendered":"Smaller, smarter, safer: How to build agentic AI on the right foundation"},"content":{"rendered":"<h2>Share with your CIO<\/h2>\n<p>The model is the easy part. That&#8217;s the core argument Karan Thakrar made at the CIO 100 Leadership Live New York event, and it reframes how enterprises should think about <a href=\"https:\/\/www.cio.com\/article\/4200088\/smaller-smarter-safer-how-to-build-agentic-ai-on-the-right-foundation.html\" target=\"_blank\" rel=\"noopener nofollow\">agentic AI architecture<\/a>. His framework runs three directions: deploy smaller models where large ones aren&#8217;t needed, treat the system around the model as the real decision-maker, and verify outputs at the moment a mistake gets locked in rather than after. The differentiating variable isn&#8217;t model power. It&#8217;s the human learning system built alongside the agentic one.<\/p>\n<h2>What this means for your business<\/h2>\n<p>Most AI infrastructure decisions right now are implicitly bets on model capability, the assumption being that a more powerful model smooths over whatever the surrounding system gets wrong. Thakrar&#8217;s argument inverts that. If you&#8217;re running GPT-4 class models on tasks that a much smaller model could handle with better context, you&#8217;re not buying quality, you&#8217;re buying a workaround for an architecture you haven&#8217;t built yet. The CIO who recognizes that gap is in a different position than the one still calibrating success by which frontier model they&#8217;re running.<\/p>\n<p>The context layer argument is the sharpest claim here, and it holds up. Frontier models from OpenAI, Anthropic, and Google are large partly because they have to infer what they don&#8217;t know about the user, the account, the process, the history. Feed the model that information directly, and you can run a significantly smaller, cheaper model without degrading output quality. This is already visible in enterprise deployments where retrieval-augmented generation (a technique where the model pulls in relevant business data before responding) outperforms raw model size on domain-specific tasks. The infrastructure cost implication alone should land on a CIO&#8217;s roadmap.<\/p>\n<p>The piece&#8217;s framing reflects a consulting and advisory posture, which tilts toward emphasizing architectural complexity as the lever worth pulling, but that tilt doesn&#8217;t make the argument wrong. It makes it directionally conservative in a useful way. The real risk for enterprise AI programs isn&#8217;t that they pick the wrong model. It&#8217;s that they build no durable architecture at all and find themselves re-platforming every time a new frontier model drops. If your current agentic deployment would need to be rebuilt from scratch to swap out the underlying model, that&#8217;s the falsification condition worth testing before your next renewal cycle.<\/p>\n<p><em>Based on reporting from <a href=\"https:\/\/www.cio.com\/article\/4200088\/smaller-smarter-safer-how-to-build-agentic-ai-on-the-right-foundation.html\" target=\"_blank\" rel=\"noopener nofollow\">Smaller, smarter, safer: How to build agentic AI on the right foundation<\/a>, originally published 2026-07-22 10:35:00.<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Share with your CIO The model is the easy part. That&#8217;s the core argument Karan Thakrar made at the CIO 100 Leadership Live New York event, and it reframes how enterprises should think about agentic AI architecture. His framework runs three directions: deploy smaller models where large ones aren&#8217;t needed, treat the system around the [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":6706,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[142],"tags":[185],"tmauthors":[],"class_list":["post-6705","post","type-post","status-publish","format-standard","has-post-thumbnail","category-ai-agents","tag-cio"],"_links":{"self":[{"href":"https:\/\/workai.tv\/news\/wp-json\/wp\/v2\/posts\/6705","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/workai.tv\/news\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/workai.tv\/news\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/workai.tv\/news\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/workai.tv\/news\/wp-json\/wp\/v2\/comments?post=6705"}],"version-history":[{"count":0,"href":"https:\/\/workai.tv\/news\/wp-json\/wp\/v2\/posts\/6705\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/workai.tv\/news\/wp-json\/wp\/v2\/media\/6706"}],"wp:attachment":[{"href":"https:\/\/workai.tv\/news\/wp-json\/wp\/v2\/media?parent=6705"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/workai.tv\/news\/wp-json\/wp\/v2\/categories?post=6705"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/workai.tv\/news\/wp-json\/wp\/v2\/tags?post=6705"},{"taxonomy":"tmauthors","embeddable":true,"href":"https:\/\/workai.tv\/news\/wp-json\/wp\/v2\/tmauthors?post=6705"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}