Stop guessing. Optimize model performance and cost in real time.
Compare models, measure real-world performance and cost, and route traffic to the best option—automatically, in production. All without redeploying.
Agents run in production. Optimization should, too.
- Prompt and model tuning is difficult, manual, and requires a redeploy.
- Visibility into what’s working is limited, and you are always behind.
- Performance and costs spike with no way to set guardrails on models.
- Hardcoded configs guarantee you are overpaying or underperforming.
- Teams define the goal, constraints, and metrics that matter for AI performance.
- The system automatically creates variants, runs evals, and surfaces what performs best. caught at the first sign of degradation.
- Configs are adjusted in real time, in production—with full visibility and control.
- AI performance and cost is continuously optimized.
Quickstart Guide
AgentControl
- 01Define configs and run offline evals.
Set up prompt, model, or parameter variations. Test them against benchmark datasets before any production traffic sees them.
- 02Experiment in production.
Expose variations to real users and measure cost, latency, and output quality against your defined targets. Find what actually wins under real conditions.
- 03Promote the winner and optimize continuously.
Route traffic to the best-performing config and keep iterating. No redeploy required.
What this unlocks in production.
Reduce AI spend while improving performance and quality.
Automatically optimize for best variants as production changes.
Scale AI usage with full visibility and control. No manual tuning or guesswork.
Turn model selection into a continuous optimization loop.
We can take bigger risks in the kinds of AI features we build, and we can validate that they’re worth it because we can see downstream impacts all the way through.
Jon Noronha
Co-founder and Chief Product Officer
Increase in user satisfaction
30%
