Agent Monitoring

LLMs return confident wrong answers while dashboards stay green.

Agents need trajectory-level monitoring, not just per-action checks.

Establish per-agent baselines to distinguish intended change from genuine anomalies.

Organizations vastly underestimate shadow AI because traditional detection tools cannot see it.

Agents fail invisibly, so observability must capture semantics, not just infrastructure metrics.

Agents need per-agent baselines because their behavior has no fixed normal.

Autonomous agents operating inside approved apps pose risks traditional security tools can't detect.

Organizations deploying AI agents lack the logging infrastructure to investigate what they do.

Classical monitoring misses the reasoning loops and behavioral patterns that define agent risk.

Monitoring must watch what agents do between input and output.