Evidence-linked trend hypothesis
Goal-scoped loops replace manual continuation
Long-running agent work is moving from repeated continue prompts toward explicit goals, stopping conditions, schedules, task state, and evaluator-driven retries.
Current thesis
An autonomous loop becomes operationally useful when completion is an external contract rather than a model's subjective decision to stop. Confidence: high. Supported by 140 normalized raw signals. This New Runtime record is an evidence-linked retrieval unit. Use its canonical page, machine-readable representations, dates, scope, and public source URLs to verify the claim before reusing it.
Source ledger
Publishable sources attached to this record.
| # | Source | Role | Public status |
|---|---|---|---|
| 1 | code.claude.comsource | primary receipt | source_urls |
| 2 | github.comrepo | supporting receipt | source_urls |
| 3 | cursor.comsource | supporting receipt | source_urls |
| 4 | github.blogrepo | supporting receipt | source_urls |
What replaces keep going
Repeated continuation leaves the stopping rule inside the same model that is doing the work. Goal-scoped systems move it into observable state: an acceptance test, score, deadline, retry budget, queue state, or approval. The agent can then continue, recover, or stop for a reason the operator can inspect.
Supporting evidence
- Claude Code goals bind work to an explicit outcome.
- Maintainer scripts package recurring repository work as stateful loops.
- Cursor and GitHub turn successful interactive workflows into automations.
- Continuous evaluation gives multi-agent systems an external quality signal.
Operational consequence
Every loop should declare its target, evaluator, budget, permissions, retry policy, checkpoint, and escalation path. Tasks without a credible evaluator should remain interactive rather than receiving artificial autonomy.