Optimize from failure evidence
Converra starts with recurring production failures and isolates the specific behavior that needs to change.
AI agent optimization
Optimize AI agents from production evidence
Converra turns production failures into tested prompt and configuration improvements, then verifies whether the deployed change moved the metric that matters.
Production proof
Salespeak orchestrator agent
100%
Hallucinations eliminated
The orchestrator stopped fabricating pricing, VAT rules, and infrastructure details — issues users were relying on as fact. Zero occurrences verified across production traffic since Apr 23 deploy.
68%
Fewer routing failures
Mis-routed queries dropped from 16% to 5% of production traffic after Apr 25 deploy. Verified.
0
Engineering hours
Converra generated and tested the fixes; Salespeak's CTO reviewed and applied the winning changes.
Most optimization stops at a recommendation
A dashboard can show a problem and an eval can score an output. Neither closes the loop. Optimization means producing a change, proving it beats the current agent, and verifying it worked after deployment.
Generate targeted variants
The system creates prompt or configuration variants aimed at the diagnosed root cause, not generic prompt polish.
Pick winners with paired evidence
Variants compete against the baseline on the same personas and scenarios so lift is measured head-to-head.
Verify the lift in production
After deployment, Converra measures whether the target failure rate actually dropped in real conversations.
The optimization workflow
Converra is built for teams that already have agents in production and need a repeatable way to improve them without handing every failure back to engineering.
- 1Cluster recurring failures across production conversations.
- 2Identify the failing step, failure mode, and likely prompt or config cause.
- 3Generate variants that directly address the root cause.
- 4Run baseline-vs-variant simulations with regression checks.
- 5Deploy the winner and monitor before/after production impact.
Optimization, not just diagnosis
Converra is for teams that want agent quality to improve continuously without assigning every new edge case to an engineer.
Related reading
Simulation testing
How Converra tests agent changes through multi-turn simulated conversations before deployment.
Production verification
How Converra proves deployed fixes worked with before/after production evidence.
Salespeak case study
The flagship proof point: routing failures down, hallucinated claims eliminated, no engineering time to generate or test fixes.
LLM agent evaluation
How Converra connects evaluation scores to root-cause diagnosis and tested fixes.
Observability vs optimization
Why trace visibility is necessary but not enough to improve production agent behavior.
FAQ
What is AI agent optimization?
AI agent optimization is the process of improving production agent behavior by changing prompts, tools, routing, or configuration based on measured failures and verified outcomes.
How is Converra different from prompt optimization libraries?
Prompt optimization libraries usually run developer-initiated experiments. Converra runs from production evidence, validates changes through simulation, and verifies whether deployed changes worked on real traffic.
What counts as an optimized agent?
A change counts only when it improves the target behavior, avoids regressions, and is verified after deployment. Simulation lift alone is not enough.