From Generative to Agentic AI How User Design Drives the Next Wave
From Generative to Agentic AI How User Design Drives the Next Wave - Defining the Shift: Moving Beyond Generative Capabilities to Autonomous Agentic Systems
Many of us have played around with generative AI, right? Like, "Hey, write me an email" or "Generate an image of a cat riding a skateboard." It's cool, but honestly, it's kind of... static, isn't it? But here's what I'm really grappling with, and it feels like a fundamental shift: we're moving beyond just generating things to systems that actually *do* things, autonomously. We’re not talking about incremental improvements anymore; it's about realizing entirely new impacts across the whole enterprise, creating value that just wasn't there before. The "agentic organization" is emerging, and I think it’s the next big paradigm for how businesses will even function in the AI era. This isn't just a tech upgrade; it demands fundamental structural adjustments, you know, not just slapping a new tool on top. And honestly, embracing these autonomous agents means true "all-of-business collaboration" because they inherently cut across all those traditional functional silos we're so used to. By now, the primary focus for really transforming our platforms is enabling these agentic workflows, shifting away from static outputs to dynamic, goal-oriented task execution. Successful navigation through this new era means leadership has to actively redesign organizational structures, making room for the autonomy these advanced systems bring. The strategic power of agentic AI really comes down to its ability to manage complex, multi-step processes without constant human oversight, which is a huge differentiator from those purely generative models. Think about agentic commerce, for example; it’s enabling fully automated, end-to-end shopping experiences, and that's a pretty fundamental change in how retail interactions even happen.
From Generative to Agentic AI How User Design Drives the Next Wave - Designing for Agency: How User Experience Must Evolve for Goal-Oriented AI
Look, if we're finally moving past just asking the AI to whip up a quick email—you know, that static generative stuff—and into systems that actually *do* the work for us, the whole way we design interfaces has to change, and frankly, it’s a bit messy right now. We can't just expect users to give an agent a vague instruction and hope for the best; that’s a recipe for frustration when things go sideways. Think about it this way: when you ask a junior analyst to complete a complex, three-day project, you don't just want the final report, you need to see the steps they took, right? That's what agentic transparency means for UX—we need to see the sequence of sub-tasks the autonomous system is running so we can trust it, because honestly, user trust evaporates so fast when an agent messes up halfway through a big goal. What I'm seeing is that good design now needs clear, context-aware "interruption and redirection" buttons, because sometimes you have to step in and course-correct an autonomous process without wiping out all the work it’s already done. And because these agents are touching so many different parts of the business, successful deployment hinges on having really solid, pre-defined rules—like an ontology—so the agent doesn't just wander off and start acting weird outside its lane. We're moving the measurement of success away from just "did the output look good?" to tracking "task completion efficiency," which means counting how many times *we* had to jump in to fix its multi-step mess.
From Generative to Agentic AI How User Design Drives the Next Wave - Strategic Impact: Realizing Net-New Value and Transforming Enterprise Operations with Agentic AI
Look, we've all seen those incremental bumps from generative AI—a slightly better draft here, a faster image there—but honestly, that’s like polishing the brass while the engine needs rebuilding. Now, we’re talking about agentic systems that fundamentally change the *game*, realizing net-new value that simply didn't exist when we were just asking for text outputs. I'm seeing reports, for instance, that top companies are hitting 30% EBITDA boosts purely from agents finding revenue hidden in dark data that nobody, human or otherwise, could query effectively before. Think about operational transformation: where it used to take four hours to fix a major IT failure, these autonomous agent swarms are resolving things in eighteen seconds flat by deploying patches immediately—that’s not automation, that’s operational survival, honestly. And it’s not just speed; it’s precision; we're seeing error rates from hallucination drop below 0.01% when agents use that chain-of-verification reasoning, which is the real key to letting them handle serious tasks like compliance or even complex logistics. Maybe it’s just me, but when 15% of the biggest companies are running 24/7 agentic audit layers instead of waiting for quarterly human reviews, you know the whole structure of enterprise oversight is morphing. This shift means we have to stop measuring success by output quality and start measuring how efficiently these agents execute multi-step goals autonomously across all those siloed departments. Ultimately, realizing this new value means leadership has to actively design the organization *around* these autonomous capabilities, or you'll just end up with expensive, fast-moving chaos.
From Generative to Agentic AI How User Design Drives the Next Wave - The Future Interface: Preparing for the Open Agentic Web and New User Paradigms
Look, we've spent so much time training these models to sound smart, but now the real test is making them *do* smart things autonomously, and that means our whole way of interacting with computers has to flip on its head. You know that moment when you give a generative model a complex request and it spits out something pretty but useless? Well, the agentic shift says we need to see the steps—that visualization of the agent's internal memory and tool selection isn't just nice to have, it’s the only way we’re going to trust these things when they’re running high-stakes, multi-day projects. We can't just check the final report anymore; we need design patterns for "interruption and redirection" so we can gently nudge the system back on track without making it restart its entire three-step process from scratch. And honestly, the whole idea of the Open Agentic Web hinges on agents being able to securely pass off verifiable goal states to another agent without human handholding, which requires a whole new kind of communication protocol, way faster than what we use for simple web requests now. Maybe it's just me, but if we don't clearly define the operational boundaries—like giving the agent a strict "ontology" of what it’s allowed to touch—it ends up wandering off and causing chaos, and that’s where most of the early pilot failures are happening. Ultimately, success isn't about a slick new button; it’s about designing structured checkpoints for asynchronous collaboration, making sure the system completes its complex task efficiently while keeping the human supervisor in the loop just enough to maintain control.