The Secret Sauce For Agentic AI Success — Human Agency

TL;DR: How the pursuit of autonomous intelligence reveals our misunderstanding of human agency

There's an irony playing out in Silicon Valley's glass towers and in enterprise boardrooms around the world.

We are witnessing the birth of Agentic AI - technology hailed as the most transformative since the internet. Yet, in our haste to build machines that can think and act like us, we may be committing one of the greatest intellectual errors of our time.

We're automating the symptoms of intelligence while ignoring its essence.

What Does The Cook's Secret Sauce Reveal About Human Agency?

One of my passions is food, and I love to experiment with dishes and create my own.

I may not be a restaurant-grade chef, but through trial and error and constant experimentation, I have gotten to a point where I know what I am doing in a kitchen (and even publish my favorite discoveries or inventions).

Cooking for me is almost a meditation, and I feel myself entering a flow state. I find myself adding salt or spices, or turning the pot from a boil to a simmer without conscious thinking.

This knowledge wasn't downloaded. It was earned. (My family can confirm that not all dishes have been perfect!)

Crucially, even when following a recipe, I am exhibiting human agency. The choices, successes and failures are all mine.

The parallel to AI development is clear: human expertise involves tacit knowledge, learned intuition, and personal accountability that cannot simply be programmed or automated.

Why Are We Building Digital Pyramids in the Empire of Efficiency?

Enterprise leaders have become enamored with a seductive narrative: intelligent agents will revolutionize productivity.

"2025 will be the year of agents," says IBM's Ruchir Puri. Salesforce predicts a billion AI agents by fiscal 2026. The projections are bold. The momentum is real.

But in many ways, we're constructing monuments to efficiency - digital pyramids built at scale - without questioning what they're for.

Reid Hoffman's new book Superagency: What Could Possibly Go Right with Our AI Future offers a timely call to action for the AI era. His central assertion - "AI doesn't replace agency - it expands it" - captures precisely what many are missing in today's rush toward agentic systems.

Most agentic systems today are what we might call "sophisticated mimics." They excel at replicating the visible patterns of work - processing documents, responding to emails, triggering workflows - but they fundamentally lack what separates human agency from algorithmic execution.

They can perform tasks, but they cannot experience the weight of their consequences.

What Do AI Systems Still Not Understand About "Impossible Intelligence"?

After years of working alongside global banks, insurers, and manufacturers, I've come to recognize three dimensions of human intelligence that most agentic architectures overlook. Together, these form the foundation of what I call "impossible intelligence" - not because it can't be modelled, but because it can't be simplified.

1. Abstraction: The Power to Create Patterns

Human abstraction is not just the ability to detect patterns - it's the ability to create them in moments of ambiguity. When a senior underwriter evaluates a borderline application, they don't just apply rules. They invent new ones in real-time, shaped by urgency, accountability, and context.

Bill Gates recently noted that AI agents are "proactive, pattern-aware, and continuously learning." But what he didn't say is that human learning often depends on forgetting - on filtering out noise, letting go of details that no longer serve us. That's something no LLM is designed to do.

2. Tacit Knowledge: The Intelligence We Can't Explain

The best decisions are often felt before they are understood. The pause before a difficult conversation. The unspoken hunch that a customer is about to churn. The intuition to escalate, or to hold back.

A master chef knows when a sauce is perfect, without plunging a thermometer. A surgeon senses when something's off, even if the monitors say otherwise. This kind of expertise - shaped by time, stress, and repetition - doesn't live in code. It lives in people.

And crucially, it lives in the friction between knowledge and responsibility. Decision-making, at its highest level, is a moral act.

3. Reasoning: The Art of Navigating Trade-Offs

Real-world work isn't about optimal paths. It's about trade-offs.

The best employees know when to bend a rule to serve a customer, when to prioritize trust over metrics, when to make the imperfect decision for the right reasons.

And sometimes the best decision is to choose the "wrong" path. Think of the entrepreneur who pursues a vision, despite market research suggesting otherwise.

What many in the agentic AI space miss is that human intelligence isn't optimized for efficiency. It's optimized for survival in an uncertain world, which often means making the "suboptimal" choice that preserves options and maintains relationships.

Why Will 40% of Agentic AI Projects Fail by 2027?

Today, much of what passes for agentic AI is theatre. Over 40% of agentic AI projects will be cancelled by the end of 2027, due to escalating costs, unclear business value or inadequate risk controls, according to Gartner.

"Most agentic AI projects right now are early-stage experiments or proof of concepts that are mostly driven by hype and are often misapplied." - Anushree Verma, Senior Director Analyst at Gartner.

We are witnessing a modern-day cargo cult: building systems that look intelligent but lack the animating spark of agency. These agents optimize workflows, but not mindsets. They execute tasks, but they don't transform how decisions are made.

The result? Tools that move faster, but think shallower. Systems that "do" more, but understand less.

How Should Companies Design Agentic AI Systems for Long-Term Success?

The future of agentic AI lies not in replacing human intelligence, but in understanding what makes it irreplaceable. The most successful implementations share three characteristics:

They capture the invisible architecture of expertise. Using real-time telemetry and behavioral observation, they surface the tacit knowledge that lives between the lines of official procedures.

They preserve human judgment in the loop. Not as a safety mechanism, but as the source of moral gravity that gives decisions their weight and meaning.

They amplify rather than automate. They extend human capability while preserving the creative tension that drives innovation and growth.

At Skan AI, we've built our process intelligence platform on this philosophy. Rather than trying to recreate human intelligence, we observe it in its natural habitat - watching millions of actions in the real world, abstracting behaviors across top performers, and creating systems that reflect the best of how humans actually think and work.

What Is the Choice Before Us in Building Artificial Agency?

We stand at a crossroads that will define the next century of human progress. We can continue building fast-moving systems that do more but understand less - digital assembly lines optimized for throughput rather than insight.

Or we can choose a different path: Building agentic systems that honor the irreducible complexity of human intelligence while extending our capabilities in ways that make us more human, not less.

Efficiency without understanding is just elaborate automation. What we need - what the moment demands - is intelligence that mirrors not just the outputs of human cognition, but its depths.

The question isn't whether we can build artificial agency. It's whether we can do so without losing our own.

The companies that understand this distinction will build the most valuable AI systems of the next decade. Those that don't may end up automating themselves into irrelevance.

"In a world increasingly shaped by algorithms, the value of human agency becomes more precious than ever. The goal isn't just automation. It's intelligence that mirrors the richness of human agency."