# Optimize for conversion, not just correctness.

Correct-but-useless is still useless. Define the outcomes you actually want, like conversion, resolution or approval, as scores on every run, then drive the loop until those numbers move.

## The problem: A 0.94 faithfulness score doesn't pay the bills

An agent can be factual, polite and fast while quietly failing at its job: the checkout that doesn't convert, the support thread that doesn't resolve. Quality metrics are table stakes; goal metrics are the point.

## How RunAgain solves it

1. **Goals as first-class scores.** Any outcome you can measure becomes a score on the trace, from built-in judges and trajectory checks to your own goal signals attached as metadata. Numeric, boolean or categorical, all queryable.
2. **Segmented by what matters.** Because traces carry your business context (tier, cohort, region, version), you optimize where it counts: the premium checkout agent's conversion, not a blended average that hides it.
3. **Candidates proven against the goal.** Experiments score every variant against your goal metrics with confidence intervals and significance tests, so the prompt that "reads nicer" but converts worse never ships.
4. **Guardrails while you push.** Drift monitors and eval alerts watch the metrics you're not optimizing, so a conversion win can't quietly buy itself with a faithfulness loss.

## Related solutions

- [Continuous improvement](https://runagain.ai/solutions/continuous-improvement-loop.md)
- [Experimentation](https://runagain.ai/solutions/agent-experimentation.md)
- [Trace enrichment](https://runagain.ai/solutions/enrich-agent-traces.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/agent-goal-optimization 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