The agent lifecycle is the full path from building an agent to running it in production and improving it over time. A practical lifecycle includes design, instrumentation, offline evaluation, staging tests, deployment gates, online evaluation, monitoring, error analysis, dataset curation, experiments, and iteration.
The important point is that deployment is not the finish line. Once an agent interacts with real users, real tools, and changing context, production behavior becomes the best source of truth for what to improve next.