If you ask most enterprise, IT teams how many AI agents are active today, you will probably get a shrug for an answer.  

AI assistants, service accounts, automated bots, and background agents – they’re everywhere. But here’s the reality:  

We’re living in an era where AI agents outnumber employees, not just in system count but in operational impact. Yet most organisations have no clear idea who owns them, monitors them, or ultimately who is accountable.

The Hidden Problem: Autonomous Agents with No Ownership

Let’s be honest. The explosion of AI agents across enterprises didn’t happen by accident. Teams love speed, so developers spin them up. And then, business units deploy them to move faster.  But while speed skyrockets, governance collapses.

Humans go through onboarding, reviews, and exits. AI agents usually go through none. This is the gap where enterprise risk grows silently and at scale.

Why This Matters More Than You Think

We don’t talk about this enough, but a bot isn’t just code. It acts on behalf of the enterprise. So, when an AI agent makes a faulty decision, exposes sensitive data, or causes financial loss – who is responsible?  

In most organizations, that question has no clear answer.  

Leaders are only now beginning to realise that deploying AI without governance isn’t transformation. Its risk is disguised as innovation.

The Root of the Issue: Lack of Ownership

Enterprises know how to define processes. Where they struggle is defining accountability for autonomous systems.

What’s missing is a framework to assign accountability.

Every AI agent needs:

• A defined role

• A clear human owner

• Controlled access

• A documented exit plan

So, what’s the Solution?

1. Treat AI Agents Like Full Fledged Workers

AI agents should be managed like a non-human workforce.

• Assign a clear human owner for accountability

• Continuously monitor agent activity

• Register every agent within enterprise identity systems

This is how responsibility is established and how governance becomes proactive.

2. Establish Lifecycle Governance

AI agents need a lifecycle, just like employees:

• Creation
• Approval
• Active monitoring
• Periodic review
• Retirement

Without lifecycle governance, AI agents outlive their usefulness, quietly accumulating access and risk. Lifecycle control prevents bot sprawl and keeps them in control.

3. Embed Expertise into AI Decisions, Not Just Automation

Most impactful AI implementations don’t just automate tasks; they replicate expert decision-making.

Those decisions must follow business rules, domain expertise, and defined risk boundaries. Not unchecked logic or shallow automation.

And this is where leadership must step in. AI governance isn’t a technical issue; it’s a leadership responsibility.

Parkar POV

If AI agents already outnumber your employees, but no one owns them, reviews them, or retires them, you’re not scaling intelligence – you’re scaling unmanaged risk.  

In mature enterprises, nothing acts on behalf of the business without responsibility attached. AI should be no different.  

The next phase of enterprise AI will not be won by who deploys the most agents. It will be led by organizations that govern AI as a workforce, with clear ownership, defined authority, and lifecycle accountability.  

Real AI transformation begins when leaders stop asking what AI can do and start asking who is accountable for it.  

At Parkar, we help enterprises make AI agents visible, owned, and governed — not as experiments, but as accountable contributors to business outcomes.  

If your organization is moving from pilots to production, the question isn’t whether to govern AI agents. It’s whether you do it deliberately.  

Or wait until they start governing your systems for you.