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.
74%
Fewer routing failures
Mis-routed queries — users landing with the wrong specialist — dropped 74% across production traffic after the 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.
How to improve AI agent reliability
AI agent reliability is not one uptime score. It is repeatable task success across changing inputs, stable tool and routing behavior, recovery from failures, regression protection, and evidence that deployed fixes worked. Converra connects those checks in one loop: measure the failure, optimize the behavior, protect what already works, and verify the production outcome.
Related reading
Diagnose reliability failures
Use the production failure taxonomy to distinguish routing, grounding, tool-use, and workflow breakdowns.
Prevent regressions
Test changed behavior and protected cases before a candidate fix reaches production.
Verify production recovery
Measure whether the deployed fix reduced the target failure without confusing correlation with proof.
Review the reliability methodology
See the consent, normalization, adjudication, and correction gates for comparable reliability evidence.
See a verified outcome
Review production evidence for routing and unsupported-claim fixes, including explicit verification boundaries.
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.