{"id":20635,"date":"2025-09-11T05:31:21","date_gmt":"2025-09-11T05:31:21","guid":{"rendered":"https:\/\/uxmag.com\/?p=20635"},"modified":"2025-09-11T05:31:22","modified_gmt":"2025-09-11T05:31:22","slug":"the-do-a-kickflip-era-of-agentic-ai","status":"publish","type":"post","link":"https:\/\/uxmag.com\/articles\/the-do-a-kickflip-era-of-agentic-ai","title":{"rendered":"The \u201cDo a Kickflip\u201d Era of Agentic AI"},"content":{"rendered":"\n

At some point during the 2010s, a meme surfaced wherein people would see someone on a skateboard, point a camera at them, and call out, \u201cDo a kickflip!\u201d<\/a> It was a kinder call from the car window than the \u201cskate or die\u201d that dominated the \u201890s, but DAK is pressurized. I\u2019ve only landed a handful of kickflips in my lifetime, and if a stranger implores me to do one, part of me wants to perform.<\/p>\n\n\n\n

We\u2019re in a similar moment right now with technology, as organizations and individuals are being asked to do something dazzling with AI. \u201cBuild an agent\u201d has become the equivalent of \u201cdo a kickflip\u201d \u2014 often well-intended, but usually blind to the complexity behind completing the task.<\/p>\n\n\n\n

It can take months (or years) of trying for a skateboarder to land a single kickflip, but that doesn\u2019t mean that each attempt that doesn\u2019t land is a failure. They are often learning opportunities \u2014 Wha? I relaxed my ankle that time and was able to flick the board better!<\/em><\/p>\n\n\n\n

It might also take hundreds of attempts to get AI agents working effectively, but every agent that doesn\u2019t behave as intended can get you closer to building one that does. It\u2019s making the API call at the right time, but I need to check the JSON in the MCP and try again. When teams are building with frameworks and runtimes and can crank out new iterations in a matter of hours, or even minutes, learnings can be poured directly into new versions. In this way, the organizations making the most progress with agentic AI are trying kickflips all day long.<\/p>\n\n\n\n

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Failing forward is failing<\/h2>\n\n\n\n

Agentic AI relies on a fast interaction process that\u2019s often described as being \u201cmore agile than Agile.\u201d Beware when skating down the proverbial sidewalk. Common adages around \u201cfailing forward\u201d and \u201cmoving fast and breaking things\u201d are the invisible pebbles that lock up your wheels and send you flying face-first into the pavement.<\/p>\n\n\n\n

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With agentic AI, the immediate objective isn’t to build an AI agent that can run an entire business. The first step is figuring out what to automate and what not<\/em> to automate. This can be a difficult thing to gain clarity on, and there\u2019s an understandable urge to use AI agents to automate existing processes. Using AI agents to automate processes that are already being handled by robotic process automation (RPA)<\/a> might offer some meager improvements by introducing a conversational interface, but it often means inviting complexity for complexity\u2019s sake, without providing a whole lot of value \u2014 failing forward.<\/p>\n\n\n\n

McKinsey research suggests that 90% of pilots fail to reach full production<\/a> due to the complexity of the task at hand. \u201cEnterprises need to get many elements right simultaneously \u2014 address unstructured data, develop advanced algorithms, build the right IT architecture, drive capability building, change management, and domain expertise, to name a few,\u201d says senior partner Ben Ellencweig. \u201cAn ecosystem is the best way to stay current and win in this space; it moves too quickly for prior conventional methods.\u201d<\/p>\n\n\n\n

So while the sentiment of \u201cfail forward\u201d is correct \u2014 you do have to be willing to take chances \u2014 it\u2019s dangerous to adopt the mindset that by simply doing anything at all with AI, you will be making progress. Organizations taking agentic AI seriously are looking for agent runtimes that can:<\/p>\n\n\n\n