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To make AI subagents observable, you capture a timeline DAG of event sequencing from dispatch to handoff, clarifying decision origins and artifact provenance across observer lanes.

You collect verifiable evidence by enforcing dispatch gates and evidence inspector mechanisms, utilizing isolated worktrees and append-only records to preserve history for replay and verification.

A timeline DAG in AI observability structures event sequencing from dispatch to handoff across observer and subagent lanes. You need it when demonstrations require auditable, verifiable governance anchors and safe startup sequences.

Yes, you can use isolated worktrees alongside append-only evidence collection to preserve history for replay and verification during governed AI demonstrations and handoffs.

You enforce safe startup sequences by applying required governance anchors and graph-query prerequisites, ensuring dispatch gates and evidence inspection are recorded and reviewed before subagent handoffs.

Observer lanes are constrained by their dependency on isolated worktrees and append-only evidence, requiring strict dispatch gates and graph-query prerequisites to maintain auditable, replayable governance demonstrations without missing required anchors.