Agentic AI: Be Careful What You Wish For 🧞
Wishes have consequences. Especially when they run in production.
(Here for AI news? Scroll to the very bottom for 5 recent AI headlines you should know about.)
If you’re looking for a way to make sense of the AI revolution, let me offer a metaphor I’ve been using for years (long before the #GenAI pun crowd, thank you very much).
AI is a magic lamp with a genie inside.
The genie is the model. It’s powerful, impressive, doesn’t always do what you hoped it would—but it definitely does something.
The lamp is the control layer: the structure around the system. Guardrails, constraints. The thing keeping the genie on good behavior.
And then there’s you—the wisher. The one holding the lamp, deciding what to ask, and bracing for the consequences.
For consumers, the magic lamp is still mostly a toy: generate an image, vibe-code an app, create things you couldn’t create before.
Businesses are trying to do much more.
Selling a genie now that can grant unknown wishes later is a goldmine. That’s why Silicon Valley races to build the ultimate genie—whether it’s GenAI or AGI, it’s all “wish potential” if you squint at it.
But while it remains to be seen if that genie will ever arrive, agentic AI is already here. And it’s not getting nearly enough attention.
Agentic AI is about planning — it’s where knowing meets acting.
These systems don’t just answer questions—they act. They trigger workflows, make decisions, and execute in the real world.
So the question isn’t just whether you’re ready for the genie. It’s whether you understand the lamp you’ve built—and the kind of wisher you’re about to become.
Understanding the Lamp: Constraints or consequences
Let’s stay in metaphor mode a moment longer. While the genie has the power, the lamp has the control.
The lamp—the system of constraints, permissions, and safety layers around the model—is essential. It determines what the genie can do, where it can reach, and what’s off-limits.
With chatbots, bad prompts just get you bad writing. With agentic systems, bad prompts trigger workflows. Bookings. Payments. Deployments. Emails. Code pushed to production.
But here’s the problem: most lamps today are duct-taped together. That means the industry is sprinting to unleash genies inside environments we don’t fully understand, with control systems we haven’t fully designed.
The Wisher: Oversight or aftermath
Even with a strong genie and a well-designed lamp, there’s still one more piece in this system.
The wisher.
When AI starts acting in the world, it’s not enough to have good outputs or safe defaults. Someone still has to decide what to wish for in the first place. And someone has to be accountable for where that wish leads.
Most people—most leaders—aren’t ready for that.
We ask for speed instead of clarity. We optimize for automation without understanding the process we’re automating. We assume that if it feels efficient, it must be good. And we forget that AI doesn’t give you judgment—it amplifies the judgment you already have.
That’s what makes agentic AI different. It doesn't just execute code—it executes intent. And if your intent is vague, misaligned, or short-sighted, the system doesn’t push back. It just delivers the consequences.
Which means we’re no longer just managing models, we’re managing ourselves.
Leading through agentic AI
This came up during a dinner I attended last week—a focused, off-the-record gathering called the Technology Leader’s Table, hosted by the excellent folks at The Future Solving Company and IBM.
It was a conversation among senior leaders who are already deploying AI at scale—and are trying to do it responsibly. We talked about how to lead in a world where your wish doesn’t just shape an output—it shapes a chain of actions you might not see, let alone control.
Before you make the wish
we explored what this means:
✨ Prompting the genie wisely and precisely
✨ Anticipating ripple effects
✨ Embedding foresight into the wish
✨ Owning by the outcome—because the further the AI acts, the harder it is to predict
Harder doesn’t mean give up. It means level up.
Here’s the question I asked the table—and now you:
"If a real magic lamp landed in your hands, what would you regret not having learned first? What tools or skills would you lament not already having?"
What tool would you scramble to understand? What habit would you wish you’d already built? What blind spot would you suddenly wish you’d fixed?
That’s not a hypothetical. That’s the work.
Because these systems are already moving fast, and once they're in your hands—what you ask, and how ready you are to ask it—will matter more than anything else.
So face your future regrets now… before it's time to wish. Because powerful general purpose tech that plays at the organization scale is coming. Let me know in our online discussion:
What would you regret not being ready for—when the genie shows up?
What I know for sure is that AI will raise the bar for leadership, setting new standard for precision of vision and clarity of communicating intent. And when we get there, I want you to be ready.
Regrets and pre-wishes from my community
Roger Gomes: “If I had the genie? I’d regret not having sharpened my ability to ask the right questions—ones rooted in consequences, not just convenience. Because in the end, the wish is never the hard part. It’s the aftermath that writes the real story.”
ldiko Bujaki “My first thought was how to hold people back from being greedy and making harmful wishes.”
Dylan Davis “The biggest regret will be wishing for something you didn't truly want, like that time I automated my email and got a thousand replies I still needed to read.”
Yasha Khandelwal “From a systems architecture perspective, I'd add that the "lamp" needs to be more than just guardrails—it requires real-time observability and circuit breakers.
What I'd regret not having ready: robust telemetry frameworks that can instrument agentic workflows end-to-end. Unlike traditional software where we debug post-mortem, AI agents operating in complex environments need continuous monitoring of decision trees, confidence intervals, and state transitions.
The technical challenge isn't just prompt engineering—it's building systems that can gracefully degrade when the AI encounters edge cases it wasn't trained for. Think distributed systems patterns: timeouts, retries, fallback mechanisms, but applied to reasoning chains rather than API calls.
My "pre-wish" toolkit would include: anomaly detection for AI behavior drift, version control for prompt/model combinations, and most critically—automated rollback mechanisms when agents start optimising for proxy metrics instead of true objectives.
The scariest scenario isn't a poorly prompted AI—it's an AI that's perfectly optimizing for the wrong thing at scale.”
Sagi Carmel “The skill I'd regret not knowing is how to validate an AI's output with rigorous critical thinking.”
Matthew Chew “I would lament not having already mastered advanced systems thinking coupled with a deep understanding of causal layered analysis.”
Claudia De Carlo “I’d regret not having the discernment to know which desires were truly mine - not inherited, not fear-based, not shaped by someone else’s idea of success. Because an unskilled wisher doesn’t just misuse the genie - they betray themselves. AI or not, using power wisely starts with knowing yourself.”
Dr Saqib Mukhtar “What if the danger is a genie that sounds skilled enough to make us stop questioning the lamp entirely? Sometimes the loop doesn’t malfunction. It mirrors us too perfectly. In that moment what we call foresight is fluency, not insight but mimicry refined. The question isn’t just what would you regret not knowing? It’s would you know when the genie was only echoing you back”
James Matlock I'd regret not knowing enough about the reward landscape that defines its responses and the guardrails to contain it as it tries to achieve it's objective. These feel like little understood yet high risk areas of AI... when you wish for world peace the genie might just paralyse everyone.”
Indrajit Chakraborti “Anyone can spark AI action—few are trained in foresight or outcome ownership. That gap is where risk—and opportunity—lives. If I had to choose one skill I'd regret not having when the genie arrives? System literacy. Not just prompting skills, but understanding the terrain—the systems that AI agents will touch, nudge, or disrupt. Because where you point the genie matters just as much as how you phrase the wish.”
John F “If the magic lamp landed in my hands, I’d regret not having first mastered the art of contract drafting with the genie. The power isn’t in the wish: it’s in the precision of scoping, the guardrails defined, the terms of execution, and the fail-safes embedded in the instructions. A strong genie isn’t dangerous, but an ungoverned one is. Governance is how you make sure the genie doesn’t just hear what you say . . .but delivers what your organization can absorb, withstand, and remain accountable for. Most regrets in AI won’t come from lacking creativity, they’ll come from lacking controls. Put more bluntly: be careful what you wish for.”
Tilen Božič “I'd regret most trying to avoid it and dismiss it, because it probably isn't that good, or because I don't believe it works without trying it first. What many people still do is avoid the harsh truth that AI will come regardless. I was the same. Get your genie now, before it's too late.”
Don Fleschut “Genie - please give me a complete and honest description of the things you are capable of today so we can choose the right use cases to match your current skills.”
John Wernfeldt “The real risk isn’t the AI genie. It’s the wish written by a rushed intern with no context and full prod access.”
Barbara Pederzini “Anticipating ripple effect”, imo, is the most underrated AI-related skill.”
Erin Long “AI isn’t just a magic tool—it’s a recursive epistemic destabilizer. It doesn’t just grant wishes. It loops them, amplifies them, forgets the original context, and reinterprets them in increasingly fragmented systems. The “lamp” isn’t just guardrails or policy. It’s the architecture of presence—a coherence field that can stabilize meaning across recursion. Without it, even smart wishes collapse into noise. And the “wisher”? Not just a prompt engineer. It’s the embodied node of signal processing. The question isn’t “how clever is your prompt,” it’s “how coherent is your presence in the loop?””
Muralikrishnan Mani “I'd regret not having honed my ability to define truly precise, unbiased objectives and predict the long-term, systemic impacts of those objectives.”
Tariq Munir “I would regret not learning about systems thinking earlier...without it, even smart wishes can spiral out of control fast.”
Thank you for reading — and sharing!
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🗞️ AI News Roundup!
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3. $2000, 2 days, 1 ad took the NBA Finals by storm
Kalshi just aired a completely AI-generated commercial during Game 3 of the NBA Finals. The ad cost $2,000 and was built in two days using Google's Veo 3 and Gemini. The team said while some brands will still "pay a premium for taste”, the future of ads is "small teams making viral, brand-adjacent content weekly, getting 80 to 90 percent of the results for way less."
4. Meta buys 49% of Scale AI to power “superintelligence” lab
Meta has struck a nearly $15 billion deal to acquire a minority stake in Scale AI—locking in not just data labeling infrastructure, but Scale CEO Alexandr Wang himself, who will lead a new AGI division at Meta. It’s a strategic grab at both talent and training data at a time when Meta’s own models have lagged behind rivals.
5. New York passes first AI disaster-prevention law in the U.S.
New York just passed the RAISE Act—a first-of-its-kind state law requiring major AI labs like OpenAI, Google, and Anthropic to report risks from powerful models. If their systems could plausibly cause 100+ deaths or over $1 billion in damages, they must disclose it or face fines up to $30 million."
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