# Every run makes the next one better.

Production traces become regression datasets. Datasets become scored experiments. Experiments become CI gates. Monitors hold the line. That's the loop, and every pass around it raises the score.

## The problem: Most teams fix agents in place

A bug gets patched, the patch gets eyeballed, and nothing guarantees next month's model bump doesn't undo it. Without a loop, quality is a level you visit, not a ratchet that only turns one way.

## How RunAgain solves it

1. **Trace → dataset.** Yesterday's incident becomes tomorrow's test case: capture real runs, especially the failures, into versioned datasets straight from the trace view.
2. **Dataset → experiment.** Every candidate fix runs over the whole suite and gets scored by judges, rubrics and assertions, with significance testing deciding what's actually an improvement.
3. **Experiment → gate.** CI runs the suite on every merge and blocks statistically significant regressions. Progress compounds because it can't silently un-happen.
4. **Monitors hold the line.** Online evals, run baselines and PSI drift monitors watch production continuously; human annotations keep the judges calibrated. When the line moves, you're the first to know, and the failing run enters the loop as a new test case.

## Related solutions

- [Goal optimization](https://runagain.ai/solutions/agent-goal-optimization.md)
- [Monitoring & alerting](https://runagain.ai/solutions/agent-monitoring-alerting.md)
- [Experimentation](https://runagain.ai/solutions/agent-experimentation.md)

Book a demo: https://cal.com/tamas-szuromi/30min · Get in touch: tamas@runagain.ai

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Markdown mirror of https://runagain.ai/solutions/continuous-improvement-loop for agents and LLMs. Append .md to any runagain.ai page URL for its markdown twin. Overview: https://runagain.ai/llms.txt · For agents: https://runagain.ai/for-agents.md