AgentOps Is Not MLOps: What Breaks in Your Monitoring Stack When Agents Go to Production

Traditional MLOps monitoring is proving inadequate for AI agents because core assumptions about stateless, predictable model behavior no longer hold true. This critical disconnect means old monitoring tools can falsely report failed or looping agent runs as healthy, creating dangerous silent failures. Unlike simple models, agents are stateful and non-deterministic, so an early error can compound across a multi-step task, and a high per-step success rate can mask a very low overall completion rate. A new "AgentOps" approach is therefore needed, shifting focus from monitoring individual calls to evaluating the success, cost, and consistency of the entire agent trajectory.