l-01-session-job-lifecycle

Standardizing an AI agent session lifecycle involves coordinating an end-to-end workflow from request to close, applying mandatory grounding checks, evidence-backed reframing, and explicit developer agreement before deeper research.

The best way to govern developer-model collaboration during bug triage is to use a built-in gate protocol with per-job lenses, ensuring evidence-backed validation and explicit agreement before proceeding from report to action.

A trust checkpoint within an AI workflow framework acts as a mandatory grounding gate, requiring evidence-backed reframing and explicit developer agreement before the session routes into deeper research or build decisions.

You do not need external job variants, as the framework supplies companion artifacts and per-job lenses for bug, feature, triage, and research to guide planning, validation, and documentation throughout the session.

You should avoid using a full session lifecycle governance framework for rapid, ad-hoc exchanges that require no evidence-backed validation, strict gate protocols, or formal handoffs between request and close.