Your AI agent didn’t suddenly break.
It just slowly got worse. That’s the problem no one talks about. It’s called agent decay. Agent decay is what happens when an AI agent in production gradually stops doing the right thing, even though nothing obviously changed. Same model. Same code. Same prompts. But decisions start slipping. Edge cases increase. Humans step in more often, quietly fixing things. Why does this happen?
Because the world changes. Inputs drift. Data shifts. Business rules evolve. And most agents are never built to notice that they’re degrading. The fix is not a smarter model. The fix is discipline. You need observability into decisions, not just outputs. Guardrails that catch bad actions. Human-in-the-loop checks where it matters. And feedback loops that continuously correct agents in production. If your agents aren’t monitored like real systems, they’re already decaying. Agentic AI only works when reliability is engineered, not assumed.
