It Has Agentic, So You Know It's Good 😏

I don't know about you, but I've been getting an increasing number of messages from vendors and individuals pitching their services or solutions. Nothing wrong with that—it's actually great to chat, swap ideas, and spot trends. BUT I can't help but squirm every time I see the hook: "This is agentic AI," "We do agentic AI," "We are agentic AI."

Ace Ventura moment: deep breath in...

"Agentic" means having the ability to act independently and achieve outcomes through self-directed action. And here's the kicker—Article 3(1) of the EU Artificial Intelligence Act defines an "AI system" as:
“A machine-based system that is designed to operate with varying levels of autonomy and that may exhibit adaptiveness after deployment, and that, for explicit or implicit objectives, infers, from the input it receives, how to generate outputs such as predictions, content, recommendations, or decisions that can influence physical or virtual environments.”

Pause. Stare. Get it? Pretty much any AI that's not trapped in a closed ecosystem (e.g., one that can't interact, consume, or send data via APIs) is "agentic" by the very definition of AI. What they're probably trying to say is: "We have tools to orchestrate and oversee multiple agents—AI or otherwise." Now that gets me excited! 🚀

Why fuss over semantics? It shapes how we approach design, solutions, governance, and risk. Even more intriguing: How do you manage and oversee your existing agentic system—the human one, with all its rules and quirks?

What do you think— buzzword fatigue, or this is just me? 😅

Organised Chaos - How Does a Leaf Bug Know What a Leaf Looks Like? 🌿

Staring at the blank page for a while now as the plane not so gently bounces around. How do I begin this one? As you can imagine, I find myself in many meetings where genuinely bright individuals go into painstaking detail explaining how things should be done for AI. Each time I struggle: how do I tell them? "You are correct, and that's why this is going to fail."

Let me take a few steps back to machine learning theory. Scientists early on faced an odd phenomenon: the "cleaner" the math was—and the more precise the data—the worse the algorithm (or learning hypothesis) would generalise on new data. What was going on? We were capturing the objective function perfectly, yet it didn't work in the real world. This is known as the generalisation gap.

Folks realised the issue: the math, and resulting algorithm, needed to be constrained through what's known as regularisation techniques (not the policy kind). To overly simplify, it's the introduction of penalties or noise into the learning process.

To have order, you need chaos.

This ties right back to nature—like how a leaf bug "knows" what a leaf looks like. It doesn't. Evolution doesn't hand it a perfect blueprint; instead, random mutations introduce chaos and imperfections over generations. Natural selection favors those that mimic leaves "well enough"—often including fake veins, irregular edges, spots like disease or damage, even "bite marks"—to blend in and survive predators. The magic isn't flawless perfection; it's the underlying ability to continuously adapt to those imperfections in response to the environment's shifting needs.

Let's zoom back out.

This underlying mathematical truth is relevant when building tech, establishing business processes, changing behaviour, and seeking governance and risk management.

The answer not only needs to balance all these components; it needs to be imperfectly correct to achieve a stable state.

The 'AI system' (from tech to people), like the bug, does not know what a "leaf" looks like. It needs to be designed to evolve through imperfections in response to the environment's needs—embracing organised chaos for true resilience and adaptation.

What would happen if we flipped the script and subjected people to the same AI Governance and standards we demand for machines? 🧐🤯

Think about it:
Bias audits on every decision you make → "Sorry, your choice shows 12% implicit bias—recalibrate or face suspension."

Transparency requirements → Full explainability logs for why you ghosted a colleague, chose that Netflix show, or voted a certain way. No black-box human behavior allowed.

Risk classification → Humans categorized as "high-risk" (e.g., politicians, influencers, anyone with power over others) requiring mandatory oversight (wait... that's us overseeing ourselves?).

Value alignment checks → Your actions must demonstrably align with "human values" as defined by a committee. Deviate? Retrain via mandatory ethics fine-tuning.

Hallucination penalties → Spreading misinformation? Instant shutdown of your privileges until you prove factual grounding.

We insist AI be safe, fair, transparent, accountable, and aligned... but apply those rigidly to messy, irrational, creative humans? Chaos? Dystopia? Or... a weirdly more orderly society?

We should test this out! 😏😜

What breaks first: our freedom, our hypocrisy, the standards themselves, or would this all work out beautifully...? 🤓

What is the ROI of Email? 📧💸

How many of you have been asked to justify the ROI of AI?

Before we dive into the deep end: of course the underlying rationale—"if I spend a dollar, what do I get back?"—makes perfect sense. After all, how else do you prioritise limited resources?

BUT—and get your handkerchief ready, because we're peeling this onion—is the premise of the question even the right one?

We treat AI as a standalone "thing" that must be isolated, measured, and justified on its own merits. Yet we all know AI—in its many forms—isn't an end in itself. It's an embedded enabler for broader business outcomes: customer engagement, sales acceleration, operational efficiency, upselling, risk reduction, you name it.

This exposes the real issue: our outdated, verticalised approach to AI budgeting and value assessment has to evolve. It is not a budget within Technology. It needs to become horizontal—with a central matrix AI coordinator empowered to spot common, reusable components (tech or process) across silos, and tightly coupling ROI to the actual business outcome, not to "AI" as a line item.

Candidly, if a leader isn't willing to strategically embrace AI—at minimum surfacing, reviewing, and challenging how we operate today—they'd better start preparing their slide deck to justify the ROI of email.

If we're serious about transformation, we need to stop obsessing over "ROI of AI" and start measuring ROI of better outcomes, wherever the tech lives. 🔥🫳🎤

Major AI Model Launches in China

Chinese tech giants made significant strides today with new AI releases. Alibaba unveiled RynnBrain, tailored for "physical AI" applications like robotics, while ByteDance launched Seedance 2.0 for advanced video generation. Kuaishou also debuted Kling 3.0, emphasizing how these models keep pace with U.S. counterparts in multimodal capabilities. This reflects ongoing U.S.-China rivalry, with ByteDance advancing sovereign AI chips and Alibaba focusing on real-world integration.

I Think I'm Going to Call You Robert! 🤖❤️

A couple of months ago, I joined a panel on AI governance that quickly veered into the juicy philosophical stuff: the nature of our interactions with AI.

The room didn't agree—which, honestly, only made it more exciting.

One side pushed hard: AI must stay deliberately impersonal. Explicitly non-human. No names, no warmth, no illusion of personhood. The fear? We'd blur lines, ramp up manipulation risks, erode transparency, and let people get emotionally suckered by code.

The other camp (mine): Sure, risks exist—dependency, deception, exploitation—but arbitrarily capping development could be shortsighted. What about therapy for the isolated? Companionship in aging societies or remote areas where human interaction is scarce? Disclosures are crucial ("You're chatting with AI, not a person"), but if someone wants an emotional bond with their computer—as long as it's consensual—why not? 🤔

Do we gate it by age? Under consent threshold: cold, robotic interface only. Over? A friendly prompt: "How would you like to address me?"

We're already seeing this play out in 2026: AI companions (Replika-style) measurably cut loneliness (Harvard studies show effects rival human chats for some), yet regs like California's SB 243 force hourly "not human" reminders and crisis safeguards for minors amid fears of manipulation and emotional dependency.

I don't have the answer. This isn't black-and-white. But damn, we need to keep asking these uncomfortable questions before it gets decided for us.

Which one is it - Impersonal walls to protect us, or personalized pathways with strong consent and transparency? 🔥🧠

Happy Chinese New Year to all celebrating! 🐎

Wrapping up the year strong by challenging Elon Musk's take on the Galileo Test as a "general" AI benchmark. Novelty and uniqueness can't be scaled without losing their essence! 😁

Check my reply: https://x.com/i/status/2023377733988835433

🚀 Decided Yet? Why Seniors Get Told to Pull Back and Why Staying Open Wins

The higher you climb, the more you're told: become less accessible. Protect your time. There's real wisdom in that. Senior roles demand deep focus, strategic bandwidth gets crushed by constant pings, and endless availability often breeds dependency instead of growth. Burnout is no joke, and scaling impact means ruthless prioritization.

But I refuse to accept that pulling back is inevitable or even optimal.

The magic of ideation, validation, and real breakthroughs lives in diversity of perspectives. The junior who spots the blind spot. The outsider who asks the uncomfortable question. The contrarian who makes you earn your position. If I, the bandit on the spectrum, start acting like I have all the answers or that my view is always the right one, I'd be the ultimate hypocrite. Life turns boring. Companies stagnate. Innovation dies in echo chambers.

Staying open isn't weakness; it's fuel. Approachable leaders build trust, spark better decisions, boost engagement, and keep talent from walking away.

The practical balance looks like this:
- Strategic openness: dedicated office hours, async input channels, deliberate 'Where am I wrong?' rituals
- Boundaries that guard your focus while inviting real input
- Actively hunting disagreement instead of waiting for it (titles breed deference)
- Staying human, because that's what keeps perspectives flowing

And here's the mindset gut-check that separates the effective from the isolated:

Ask yourself honestly—have you decided and are finding a way... or are you finding the way to decide?

Being brutally clear on where you actually stand matters more than any calendar block. Pretending you're still open while quietly closing doors is the fastest path to irrelevance. Owning your stage - whether you're still exploring or have planted your flag - lets you lead with integrity and keeps the diverse voices coming.

☀️ Transformation & Change Agents: The Unspoken Realities

We throw around words like "transformation" and "change" so casually. Everyone wants progress. Leaders declare it from the top; teams are tasked with making it happen. But how often do we actually pause to think about what change really means?

At its core, transformation is movement from what is to what could be. That shift sounds inspiring until you remember: not everyone will like it, welcome it, or even be able to come along for the ride. Some will resist openly. Others will quietly disengage. A few will quietly leave. And that's before we talk about whether the organization is truly ready for the disruption it claims to crave.

Whether you're the senior leader sponsoring the change or the person on the ground driving it day-to-day, the prerequisites matter more than the PowerPoint deck. Cultural alignment. Real skill gaps closed (not just training checkboxes). Genuine stakeholder buy-in, not lip service. Without these, you're not leading transformation, you're forcing a square peg into a round hole.

And then there are the consequences. The ones we rarely discuss upfront. Burnout from constant upheaval. Fractured trust when people feel pushed too far, too fast. Innovation that stalls because the system punishes the very experimentation it asked for. The most counterproductive thing I've seen (and felt) is this: empower people to challenge the status quo, explore bold paths, take calculated risks—then quietly (or not so quietly) penalize them when the outcome isn't perfect or when discomfort surfaces.

Real change agents need guardrails, not handcuffs. They need leaders who can say:
“Here’s where we’re aiming, here’s what we won’t compromise, and here’s the space where intelligent failure is not just tolerated—it’s expected.”
They need room to test, to pivot, to learn from what doesn't land. Because the alternative—declaring you want change while punishing the people brave enough to pursue it—kills momentum faster than any competitor ever could.

In the end, transformation isn't about flawless execution. It's about honest reckoning with the human cost and the organizational reality of actually moving from here to there. The organizations that survive it (and thrive) are the ones that treat change not as a project, but as a living, breathing process. One that demands courage, patience, and a high tolerance for the uncomfortable in-between.

And sometimes, the deepest transformation happens not in the grand strategy... but in how we choose, and how we empower employees, to show up for each other while it's unfolding.

💡Lately a few questions keep circling back for me around AI:
➡️ How deeply will it reshape work and careers?
➡️ What happens to creativity, relationships, and daily life when so much "thinking" gets automated?
➡️ Where do humans still hold an irreplaceable edge?

But this channel isn't just my thoughts.

What's been occupying your mind about AI—and how it touches work, career, social life, or anything else?

A quick doubt, a persistent what-if, something you've been turning over for weeks (or longer). One line or a longer reflection—whatever feels alive for you right now. No pressure to perform.

Happy to listen and discuss.

Sometimes the most important step you take is the courageous one into the unknown.

Lately I've been sitting with that—especially as AI speeds up shifts in work, careers, creativity, and connection. The path ahead often looks foggy, uncharted, risky. Yet standing still risks missing the deeper transformation unfolding.

Its Monday. Some of you are going into work, some of you are going into study. Some of you, like me, might need a little morning spirit uplifting - here goes 😅

I asked the librarian if they had any books on paranoia.
She whispered, "They're right behind you."

What do you call a factory that makes okay products?
A satisfactory.

Why did the tomato turn red?
Because it saw the salad dressing

🥁😁

The Change [AI] Agent

I've recently had one of those embarrassing "A-Ha!" moments that only truly lands when you experience it: AI isn't just a tech wave—it's the ultimate change agent.

We get that it's not purely technology-led (no need to belabor that). But do we fully appreciate how profoundly it's a change agenda? Everyone pushing AI forward sits somewhere on the change agent spectrum. They're not coding features; they're reshaping jobs, workflows, cultures, and comfort zones.

As leaders and organizations, are we seriously guiding and protecting these change agents? We can't realistically expect them to transform an entire organization without proper support mechanisms—coaching, psychological safety nets, open forums for venting resistance, proactive stakeholder engagement—unless, deep down, we still believe "it's just tech." Real change, especially proactive and not born from crisis, inevitably triggers discomfort, unhappiness, and outright dissonance. People defend their hard-won status quo with everything they've got.

Too many organizations still treat this as an afterthought. We need to stop relegating talent/HR to second fiddle in AI initiatives. HR and talent teams can no longer sit on the sidelines—they must be embedded from the start as strategic partners: proactively engaging stakeholders, building pressure-release mechanisms, upskilling at scale, and shielding change agents from overt pushback or subtle sabotage. The real value in AI (far beyond algorithms) lives in the human/organizational side—ignore that, and transformation stalls.

On a lighter, random note: I was dancing to work today 🕺

🚀 Why Being Deliberate Might Just Be the Ultimate Game-Changer in Agentic AI

Everyone's chasing the dream: agentic AI that automates end-to-end workflows intelligently, autonomously, aspirationally. But as we obsess over non-deterministic magic—surprise outputs, emergent behaviors—what's the real linchpin for design, implementation, and governance?

It might be deceptively simple: being deliberate.

Late last year I introduced the Stability-Assured Framework for Entities (SAFE): a unified lens that treats AI agents and human agents as equals under the same control umbrella. The key shift? Stop fixating on individual agent accuracy. The monster under the bed is system instability —cascading failures, feedback loops gone rogue, one tiny drift turning into workflow Armageddon.

Here's the delicious irony we've been romping through in this gloriously non-deterministic playground: to make it actually usable in the real world, we need to wrap it in a deterministic cocoon.

Even we humans—with our wild imaginations and coffee-fueled impulses—operate from a finite set of actions. We don't teleport or sprout wings; physics, norms, and consequences bound us. So we quantify and limit exposed risk.

The same logic applies to our agents. Amid all their probabilistic flair, what's the ironclad set of deterministic actions anchoring them? How tight are those bounds? How fast are the overrides? How do we measure stability margins?

Think of it (cheekily oversimplified, but that's theory's beauty) as F(x):

- x = tightness of deterministic control (action space bounds, override latency, controllability thresholds, SAFE-style stability metrics)
- F(x) = net intelligence yield (creativity + adaptability + usefulness – catastrophe risk)

Crank x to infinity → rock-solid stability, but creativity flatlines. Agents become glorified if-then bots.
Drop x to zero → fireworks of novelty… until the first perturbation sends everything off a cliff.

The game isn't picking one fixed x. It's recognizing the curve has a peak—and the real art (and science) is hunting it. Maybe even teaching agents to sense the environment and tune their own x dynamically: loosen for exploration when safe, clamp down when drift threatens stability.

Meta-control. Adaptive wrappers. Living membranes instead of static cages.

Over-simplified? Absolutely. But start cartoon-simple, then sharpen with ugly realities: multi-agent interference, goal misalignment, distributional shifts. How do we proxy "creativity yield" vs "stability cost"? Novelty scores? KL divergence on action distributions? Or admit it's part art, not pure science?

Next time you're building agents, pause and prod:
Where does your F(x) peak live?
How deliberate are you being about finding—and defending—that balance?

Because if we can't answer that, we're not engineering revolutions. We're just rolling very expensive dice.

Chew on it. The future might depend on it.

What’s your take—too tight, too loose, or still searching for the peak? Drop thoughts.

[Link to SAFE paper https://davidroihardoon.com/publications → Stability-Assured Framework for Entities PDF]

Great 60 second video by Marc-Alexandre Cartiant breaking down Agentic AI safety by design ⚡

Nice little visual

Came out in BusinessTimes today 😁

https://www.linkedin.com/posts/davidrh_i-have-always-beenand-largely-remaina-strong-share-7438412265231536129-30DY

🎲 Random Tuesday Thoughts

One of the reasons I dove deeper into AI was my curiosity (or confusion 😅) about the human condition — what makes us tick, why we choose one path over another, and how we truly connect.

We’re all so different — wonderful and, yeah, sometimes a royal pain in the ass 😂. But magic happens when those differences come together.

Bringing people together is a lot like a mathematical equation or a symphony: don’t judge the violin for its high pitch or the saxophone for its gritty rasp. Understand the context, the unique voice. The masterpiece emerges from the harmony.

Leadership is the same — but infinitely messier because every “instrument” has its own perspective, its own timing, its own idea of when to play.

The first test of real leadership: Are you recognized as the leader? Do people trust that you’re acting for the collective good — even when it demands compromise from the individual?

Compromise. Acceptance.

Two small words that carry enormous weight.

It’s about accepting others as they are, flaws and all. And — harder still — accepting ourselves. Because after all, one isn’t how they perceive themselves to be… but how others perceive them.

We like to think our self-image is the truth. But reality is relational: the mirror lies (literally and figuratively), and the clearest picture of who we are often comes from the reflections in other people’s eyes. That’s uncomfortable, but it’s also where growth lives — in bridging the gap between self-perception and social reality through humility, listening, and yes… more compromise.

On a complete tangent (bear with me, it’s AI brain talking): maybe that’s the real secret to AGI too.
Not “fixing” our human flaws to create some sanitized superintelligence. But encoding our features in all their messy, contradictory, glorious complexity. The jealousy, the generosity, the stubbornness, the empathy — the whole symphony. Because if we strip out the rough notes to make it “perfect,” we might lose what makes us worth aligning to in the first place.