Artificial intelligence agent observability combines structured logs, traces, metrics, message records, tool-call history, and evaluation results. It helps operators reconstruct what an agent attempted, which inputs influenced it, how long each step took, and where a failure or policy violation occurred.
Multi-agent observability must also capture relationships between agents and shared infrastructure. A complete view needs correlated identities and timestamps so investigators can follow work across messages and delegated tasks. Monitoring only final answers can miss coordination, retries, hidden dependencies, or unauthorized side channels.
