An autonomous optimisation loop where an AI agent iterates on a single target, a prompt, module or Skill, to maximise a scalar metric computed by an automated evaluator. Hypothesise, modify, evaluate, keep if better else reset, repeat. The binding constraint is not the loop; it is knowing what to measure.
How does seasonal validation run inside the pipeline?
A three-file architecture separates the goal (read-only), the target being optimised (read-write) and the evaluator that returns a single number the agent never sees the source of. The loop iterates until the metric stops improving. It is the optimisation engine that keeps a Skill library from going stale, every Skill improves continuously against its metric.
The hard part is metric design. As the source puts it: the skill of the future is knowing what to measure.
What do the numbers say about topical authority content?
| Metric | Figure | Source |
|---|---|---|
| Google searches that end without a click (2026) | 68% | SparkToro |
| Forecast drop in traditional search volume by 2026 | 25% | Gartner |
Figures as reported by the cited sources on the dates they published them.