The “Do a Kickflip” Era of Agentic AI
IDEAS IN BRIEF
- The article compares building AI agents to learning a kickflip — failure is part of progress and provides learning.
- It argues that real progress requires strategic clarity, not hype or blind experimentation.
- The piece calls for proper agent runtimes and ecosystems to enable meaningful AI adoption and business impact.
In the rush to adopt AI agents, many organizations are acting like beginners attempting a kickflip — eager, ambitious, but unprepared. This article explores why failing forward isn’t just about trying more, but about doing it with strategy, proper tools, and the right environment. Learn how agent runtimes, ecosystems, and knowledge bases can turn experimentation into meaningful business impact, and why avoiding ‘kook’ behavior is key to mastering agentic AI.
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, “Do a kickflip!” It was a kinder call from the car window than the “skate or die” that dominated the ’90s, but DAK is pressurized. I’ve only landed a handful of kickflips in my lifetime, and if a stranger implores me to do one, part of me wants to perform.
We’re in a similar moment right now with technology, as organizations and individuals are being asked to do something dazzling with AI. “Build an agent” has become the equivalent of “do a kickflip” — often well-intended, but usually blind to the complexity behind completing the task.
It can take months (or years) of trying for a skateboarder to land a single kickflip, but that doesn’t mean that each attempt that doesn’t land is a failure. They are often learning opportunities — Wha? I relaxed my ankle that time and was able to flick the board better!
It might also take hundreds of attempts to get AI agents working effectively, but every agent that doesn’t behave as intended can get you closer to building one that does. It’s 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.
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By Josh Tyson
