Pranav Kumar,
Senior Director(Data & AI),
Capgemini,
“The real question is not whether machines think, but whether men do.” – B.F. Skinner
Imagine this:
You’ve hired a brilliant analyst. Initially, they simply run reports on request. But over time, they start anticipating questions, uncovering patterns, and making decisions—without z asking. One day, they reroute a shipment, adjust pricing, or send a note to a client. Not because you asked, but because they thought it was the right thing to do.
Now imagine that an analyst is an AI.
This isn’t science fiction. This is where we are today.
Welcome to the agentic era—where artificial intelligence doesn’t just respond to instructions but acts with intent.
The Quiet Revolution: From Automation to Autonomy
Until recently, AI systems functioned like calculators on steroids. They were highly capable, data-hungry engines designed to predict outcomes, classify content, or recommend actions. We called them models—and they waited patiently for prompts.
We were busy tuning models. Accuracy, loss functions, optimization—these were the familiar coordinates of our AI universe. We built systems to predict, recommend, and assist. These systems were impressive, yes—but fundamentally reactive. They waited for a prompt, a command, a dataset. They didn’t act on their own.
Today, that’s changing. Rapidly.
But today, with the convergence of large language models, planning architectures, APIs, and memory loops, we’re witnessing the rise of something fundamentally different: agents. These are AI systems capable of initiating tasks, navigating tools, making decisions across steps, and continuously adapting their actions based on outcomes.
We’re entering the agentic era of AI—one where systems are no longer just trained models but are evolving into autonomous agents. These agents can take goals, interpret environments, make decisions, and trigger actions—often without human intervention. They’re not just tools. They’re becoming actors.
A model answers a question.
An agent finds the question, answers it, books a meeting, writes the email, and logs the interaction in your CRM. Welcome to the agentic era—where artificial intelligence doesn’t just respond to instructions but acts with intent.
It’s a profound leap—from passive intelligence to proactive agency.
And this leap changes everything we thought we knew about AI governance.
This shift challenges one of the core assumptions of how we’ve governed AI so far: that we, the humans, are always in charge.
The Governance Illusion: Why Old Tools No Longer Work
AI governance has traditionally been structured around three pillars:
These frameworks worked reasonably well—when the system was static. You could validate a model before deployment, lock its parameters, monitor its outcomes, and move on.
But agents break this mold.
They learn across interactions. They initiate actions on your behalf. They collaborate with other agents. They even reinterpret their original goals based on evolving contexts.
In short: they behave.
And governance rooted in frozen snapshots is ill-suited for a moving system.
“Change the way you look at things and the things you look at change.” – Wayne Dyer
Governance in the agentic era must be dynamic, context-aware, and intervention-ready. We're no longer designing systems that perform calculations—we're shaping systems that can exercise judgment.
What Does It Mean to Move from Models to Agents?
Let’s simplify it.
If AI models are like GPS devices—they give you directions when asked—then agents are more like autonomous vehicles. You tell them the destination, and they decide how to get there. Sometimes they’ll ask you for help; sometimes they won’t. Sometimes they’ll reroute entirely based on real-time information.
And just like autonomous vehicles, autonomous AI agents introduce a new kind of complexity:
How do we stay in control of a system that makes its own choices?
The AI systems we’re deploying today don’t just complete sentences or classify images. They initiate email sequences, manage supply chains, detect fraud, optimize pricing, write code, conduct research, even handle customer conversations. More importantly, they do this across steps—reasoning, planning, acting.
In other words: agency.
The implications for governance are not evolutionary. They are transformational.
The Shift: Governance as Guardrails, Not Just Rules
“Change the way you look at things and the things you look at change.” – Wayne Dyer
Traditional AI governance is designed for supervised predictability. Checklists, model validations, data audits, fairness assessments—these worked well when AI stayed within the boundaries we defined.
But agents don’t operate within boundaries. They explore them.
The shift from governing models to governing agency calls for a new approach—one that’s dynamic, contextual, and continuously learning. It’s no longer enough to ask: Is the model fair? Is it explainable?
Now, we must ask:
We’re not just auditing outcomes anymore—we’re trying to understand intentions and behaviors within autonomous systems.
This introduces a deeply human challenge: how do we encode judgment into systems that learn beyond our visibility?
Where This Matters: From Boardrooms to Frontlines
We’re moving from automation to delegation.
When we delegate to a human, we expect them to improvise, learn, and decide—but also to explain themselves, stay aligned with values, and escalate when needed.
That’s exactly the bar we now need for AI agents.
So we must ask new governance questions:
These aren’t philosophical curiosities. They’re boardroom-level concerns.
Whether you lead a team, a company, or a product line, the implications are already here.
This isn’t theoretical.
Last year, an enterprise chatbot acting as a “customer success agent” offered unexpected discounts to customers. The logic made sense to the agent—it was optimizing for retention—but it wasn’t aligned with business policy. The root cause? No clear policy sandbox defining the boundaries of agency.
These are not just technical problems. They’re governance failures.
We need new roles and tools: AI behavior monitors, simulation environments, autonomy thresholds, escalation protocols. And perhaps most importantly, cross-functional governance frameworks that bring together technologists, ethicists, business leaders, and legal experts.
A Reflection: Are We Governing Intelligence, or Delegating It?
“We do not see things as they are, we see them as we are.” – Anaïs Nin
In many ways, AI governance is not just about AI. It’s a mirror reflecting our own blind spots, values, and assumptions about control.
Moving from models to agents forces us to re-examine what it means to delegate decision-making. Not just to people, but to systems. And in that shift lies both potential and peril.
We are no longer merely building tools—we are shaping digital actors. Our responsibility, then, is not just to monitor their performance but to question their purpose, their boundaries, and their alignment.
So here’s a question worth sitting with:
Are we preparing our governance systems to match the autonomy of our AI systems—or are we still trying to control tomorrow’s intelligence with yesterday’s rules?
A New Governance Playbook: Guiding Agency Without Stifling It
So, what does governance look like in the agentic age?
Here’s a starter framework:
This isn't just about minimizing risk. It's about building confidence in collaboration—between humans and intelligent systems.
For decades, AI has been backstage—running calculations, analyzing data, suggesting decisions.
Now, it’s stepping onto the main stage—making choices, taking initiative, representing us.
And just like with any empowered team member, we must provide not just instructions, but guidance. Not just constraints, but context. Not just oversight, but shared understanding.
“A mind that is stretched by a new idea never returns to its original size.” – Oliver Wendell Holmes
Governance in the age of agents is not about pulling AI back into predictability. It’s about evolving our systems, our culture, and our leadership to govern agency wisely.
Because the question is no longer can AI act?
It’s: Can we govern the world where it already does?
The agentic era is stretching our collective imagination—and our governance playbooks. As we walk this path, we’ll need more than compliance checklists. We’ll need wisdom, collaboration, and courage.
Because governing agencies aren't just about AI.
It’s about us.
Let’s keep this conversation alive. How is your organization thinking about AI agents and governance? Where do you see the biggest gaps—or opportunities?
The Journey Into Industry
Pranav Kumar is a distinguished Digital, Data & AI Business Leader with over 20 years of global experience at the convergence of consulting, product innovation, and digital services. He partners with Fortune 500 companies to deliver data-driven customer experiences by seamlessly integrating technology, people, and platforms.
His deep expertise spans composable and packaged CDPs, Adobe and Salesforce ecosystems, conversational AI, and hyper-scaler solutions. A trusted advisor to CXOs, Pranav has led large-scale digital transformation programs that unlock growth and deliver strategic impact across complex, multicultural markets.
Passionate about innovation and the startup ecosystem, he actively mentors entrepreneurs and global business school graduates. He contributes to national innovation platforms such as the Atal Innovation Mission (NITI Aayog) and Technovation. Known for his strategic foresight and human-centered leadership, Pranav also serves as a keynote speaker, DE&I advocate, and advisory board member, championing inclusive innovation and future-forward thinking.
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