Everyone seems to be writing about Agentic AI these days. Thought pieces abound, strategy decks are multiplying, and the hype train shows no signs of slowing down. And yet, when you look for tangible, concrete initiatives you can actually start today—there’s surprisingly little to grab onto. It’s this very disconnect—between strategic ambition and tactical inertia—that makes Agentic AI so compelling, and yet so elusive. Present, competent, and invisible until you need it, Agentic AI is not just another layer of automation—it’s a genuine paradigm shift—on par with the arrival of the graphical user interface or the jump to mobile computing. In those moments, we didn’t just get new tools—we got entirely new ways of interacting with machines, reshaping expectations, workflows, even business models. Agentic AI sits in that lineage: a shift not just in what software does, but in how it behaves. Where yesterday’s software waited for your click, Agentic AI anticipates, decides, and collaborates. It’s as if your Excel sheet suddenly developed ambition—and initiative.

Think of Agentic AI like a smart, slightly overeager intern on their first day. Full of promise, clearly intelligent, but in need of direction. You don’t expect them to run the business on day one—but you do expect them to learn quickly. Give them a few clear instructions, some context, and before long, they’re taking care of tasks you didn’t know could be delegated.

For decades, our software has been reactive—helpful, yes, but always waiting for a prompt. It’s limited the potential of what technology could be. We’ve grown used to scrolling, clicking, searching, switching. But imagine a world where your systems proactively collaborate with you. Where your low blood sugar triggers a fridge check, which triggers an automatic healthy meal order. Welcome to the age of agency—and yes, your digital intern just handled lunch.

Agentic AI isn’t a new feature; it’s a new paradigm. Just as mobile made computing ubiquitous, and cloud made it instantly deployable, and machine learning made it optimised, Agentic AI makes it intelligent. Not in the philosophical sense, but in the “gets things done while you’re busy living” sense. It connects models, memory and tools to build digital sidekicks that reduce friction in everyday tasks: from travel planning to managing your health. Much like an intern who begins to anticipate your calendar clashes or preps your slides without being asked, the agent learns by doing.

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But with all new paradigms comes confusion—and FOMO. The market is drunk on demos and bloated with hype. AI strategies often sound like a checklist of acronyms (LLM, RAG, MCP, TTS, ... —oh my!). Organisations panic about being left behind, and paradoxically, this panic makes them slow. Paralysis by analysis. But the antidote is simple: play. Tinker. Experiment. But do it with intention. Just like onboarding a real intern, start with safe tasks and build it up from there.

Here are three principles that should guide your first few steps: Chat first. Don’t lock in. Cut a niche.

🗣️ Start with conversation—because that’s where the real, juicy data lives. Let users talk, vent, complain. Train your agents on what actually matters to your userbase (often a subset of informed early testers).

👐🏻 Next, keep things open—don’t marry a single platform or tool. This field is moving faster than most vendors will admit. In times of rapid change, a "best of breed" approach—where you select the optimal tool for each part of the puzzle—offers more flexibility and resilience. It’s a hedge against placing all your bets on a single vendor who may not be GenAI-relevant in six months.

💡 Lastly, go deep, real deep, on a single use case. Don’t build a chatbot that does everything poorly. Build an agent that solves one problem brilliantly. Much like an intern who’s mastered your inbox before moving on to client reports.

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Some of the best examples already exist:

• Our very own Batibouw-buddy (shameless plug): a renovation sidekick that knows your kitchen budget better than your contractor.
• The Atlantic: a sandbox for journalists to play with generative tools before publishing;
• Limbic an AI-enabled triage system in healthcare that actually respects both the patient and the physician. Actually, there's great (very fresh) research to back the claims made here even more, from another clinical institute in the States

These aren’t experiments—they’re the early signs of a smarter, more agentic world.

And here’s the twist no one wants to say out loud: general-purpose AI might not be where your gold lies. Unless your salary slip reads OpenAI, Meta, Anthropic or another AI behemoth, your competitive edge won’t come from trying to build another all-purpose foundation model. That race is already run by those with billions to spare. The secret sauce is specificity. You don’t need millions of users—you need a hundred who love what your agent does. Build something that becomes a part of their workflow, their morning routine, their life. Think small but deep—like assigning your intern a single project that becomes their proving ground.

So yes, build your agent. But do it thoughtfully. Test fast, iterate faster. Keep your architecture open, your ethics grounded, and your scope narrow. Because in the age of Agentic AI, it’s not about controlling intelligence. It’s about unleashing assistance. Treat it like the promising intern it is: coach it, empower it, and you might just find yourself wondering how you ever got things done without it.

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