AI agent state detection combines runtime signals, heartbeats, task records, tool activity, and error events to present a current operational status. Accurate state helps a team know whether to wait, send context, reassign work, investigate a failure, or review a completed result.
State detection must handle stale and missing signals carefully. A timeout or lost connection is not proof that the agent completed or stopped safely, so uncertain states should remain explicit and preserve prior durable work until the runtime can be reconciled.
ELI5
Agent state detection works out what an AI agent is doing right now. It distinguishes states such as ready, running, waiting for approval, blocked, failed, disconnected, or finished.
For example, a dashboard may show that an agent stopped making progress because it is waiting for a file. A missing signal should be shown as uncertain rather than automatically treated as proof that the work completed.
