Lifecycle management gives an agent task explicit states such as starting, working, blocked, waiting for approval, idle and finished. Direct runtime events make those transitions more reliable than inferring them only from terminal text.
Clear lifecycle states support orchestration because another agent or a person can wait for a defined condition before acting. Transitions should be durable, observable and able to represent failure or restart without pretending unfinished work completed.
ELI5
An agent lifecycle is the set of stages an AI agent or its task moves through, such as starting, working, waiting, needing approval, failing, and finishing. Clear stages let people and other software know what is actually happening.
For example, a research agent may start as queued, change to working while it gathers sources, move to waiting when it needs a person's choice, and finish only after it produces the requested report. Recording these changes prevents an interrupted job from being mistaken for a completed one.
