A year ago, most conversations I had with Customer Success leaders focused on experimentation, not AI governance. Teams were testing prompts, generating meeting summaries, and finding ways to save a few minutes here and there. Those conversations have changed.
Today, I see organizations deploying renewal risk agents, onboarding workflows, support triage assistants, customer intelligence copilots, and AI-powered account planning tools. Customer Success, Professional Services, Support, and Operations teams are all building their own solutions.
Small Experiments Surrounding AI Governance Are Becoming an Operating Model
Individually, none of these initiatives seem particularly risky. Collectively, they create a new operational challenge.
When I ask leaders simple questions such as: what AI agents are currently running across your post-sale organization? Who owns them? Which ones are creating measurable business value? Which ones are duplicative? Which ones are consuming budget without delivering meaningful outcomes? Many organizations struggle to answer.
AI Agent Debt Is Starting to Build
That’s why I’ve started thinking about a concept I call AI Agent Debt. Much like technical debt, AI Agent Debt accumulates one reasonable decision at a time. Teams move quickly, solve local problems, and create useful workflows. Over time, those disconnected decisions become difficult to manage, maintain, and govern. We’ve seen this pattern before, and marketing teams experienced it with marketing automation. IT teams experienced it with SaaS applications. Infrastructure teams experienced it with cloud environments, AI is following a similar path.
AI Is No Longer Just Experimentation
The difference is that many organizations still think of AI as experimentation. But once AI begins influencing customer communications, onboarding decisions, renewal prioritization, support interactions, and account segmentation, it becomes part of your operating model. And operating models require governance.
AgentOps Is the Next Post-Sale Discipline
This is where I believe a new discipline is emerging. I call it Agent Operations Management, or AgentOps. AgentOps is not a software category. It is an operational discipline focused on managing the growing ecosystem of AI agents, workflows, automations, and copilots operating inside post-sale organizations. Just as Customer Success Operations emerged to help teams scale systems, processes, and reporting, AgentOps will emerge to help organizations govern AI-enabled work.
The Question Leaders Need to Ask Now
The leaders who start building these capabilities today will be far better positioned as AI adoption accelerates over the next several years. The question isn’t whether AI agents will become part of your operating model, it’s whether you’re preparing to manage them before AI Agent Debt becomes a problem.
