"Technical debt is not a sin. Not paying it off is." — Ward Cunningham, who coined the term "technical debt" in 1992

I was talking to a CISO last week who said something that shocked me: "I think we have more AI agents running right now than we know about, and I have no idea how to find them."

The assistant that was configured for a Q3 pilot that never launched and still has Slack access. The chatbot that was built for a marketing campaign that ended in January and is still connected to the CRM. The automation script that an intern wrote two jobs ago and is still running on a production server and still holding API keys.

This is AI agent debt. The accumulation of abandoned, forgotten, or ungoverned AI agents that continue running with access nobody remembers granting and ownership nobody can identify.

Gartner recently made a striking prediction. By 2027, 40% of enterprises will demote or decommission autonomous AI agents due to governance failures discovered only after those agents are already running in production.

Most people will read that as a governance problem. I think part of it is also a lifecycle problem.

Organisations are deploying agents faster than they can govern them. But the lifecycle conversation has barely started.

Very few organisations are asking:

  • When does an agent retire?
  • What happens to its credentials?
  • Who decommissions its permissions?
  • How much does this agent cost to run?

In traditional IT, we have decommissioning procedures for servers, applications, and accounts. Most organisations have processes to identify orphaned cloud resources.

For AI agents, in many organisations, there is no equivalent process. An agent deployed today could still have valid API keys in 18 months. Its model may have been replaced. Its purpose may have disappeared. Yet its permissions remain.

The problem compounds quickly. One abandoned agent may represent a future incident nobody has identified yet.

A few things that may help:

  • Agent inventory as a security control. You can't govern what you can't see. Treat agent discovery like asset discovery.
  • Expiry dates built into agent credentials. API keys tied to agents should auto-rotate and auto-expire, not persist indefinitely.
  • "What retires this agent?" Make it a required field on every deployment request, just as you would document a server decommissioning plan.
  • Quarterly agent lifecycle reviews. Review every agent in production. Is it still needed? Does anyone still own it? If not, shut it down.

AI agent debt is invisible until it isn't. When it becomes visible, it's usually because someone discovered an agent they didn't know existed, with access they didn't realise it still had, or worse, an incident occurs.

Do you know how many AI agents are running in your environment today?

More importantly, do you know how many should no longer be there?

#AIAgents #AIGovernance #Cybersecurity #ShadowIT