Metrics

Definition

Metrics coverage in this archive spans 6 posts from Dec 2016 to Sep 2025 and treats metrics as a production discipline: evaluation loops, tool boundaries, escalation paths, and cost control. The strongest adjacent threads are ai, measurement, and dora. Recurring title motifs include metrics, measuring, ai, and without.

Key claims

  • The archive repeatedly argues that metrics only creates leverage when it is wired into an existing workflow.
  • Early posts lean on metrics and deleted, while newer posts lean on metrics and ai as constraints shifted.
  • This topic repeatedly intersects with ai, measurement, and dora, so design choices here rarely stand alone.

Practical checklist

  • Define quality gates up front: eval sets, guardrails, and explicit rollback criteria.
  • Start with the newest post to calibrate current constraints, then backtrack to older entries for first principles.
  • When boundary questions appear, cross-read ai and measurement before committing implementation details.

Failure modes

  • Shipping agent behavior without hard boundaries for tools, data access, and approvals.
  • Optimizing for model novelty while ignoring reliability, latency, or cost drift.
  • Applying guidance from 2016 to 2025 without revisiting assumptions as context changed.

Suggested reading path

References