Test conversations, not isolated responses
Agent failures usually happen over multiple turns. Converra runs complete conversations with follow-ups, interruptions, confusion, and handoffs.
AI agent testing
Test agent changes before customers do
Converra tests production AI agents with multi-turn simulations, head-to-head comparisons, regression scenarios, and production verification.
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.
Prompts cannot be tested like normal functions
A prompt change can look fine in a few manual examples and still break routing, tone, tool calls, or policy behavior in production. AI agent testing needs to measure behavior across realistic conversations, not just single-turn outputs.
Use personas from production patterns
Synthetic personas are derived from real behavior clusters so tests cover the users and edge cases your team actually sees.
Compare against the current agent
Every candidate change runs against the same personas and scenarios as the baseline, making the comparison direct and fair.
Protect what already works
Regression scenarios catch changes that fix one failure while silently degrading another flow.
The testing 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.
- 1Build or import a scenario set from production conversations.
- 2Generate synthetic personas that reproduce real intents and edge cases.
- 3Run the baseline and candidate agent head-to-head on the same conversations.
- 4Score task completion, accuracy, safety, tone, and custom business metrics.
- 5Block changes that improve the target issue but regress golden scenarios.
Designed for teams shipping real agents
Use Converra when manual QA is too thin, production A/B testing is too risky, and static evals do not cover how your users actually behave.
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 testing?
AI agent testing is behavioral testing for agent systems. Instead of checking one expected output, it measures whether an agent completes the right task across realistic multi-turn conversations.
Why not just run static evals?
Static evals are useful, but they miss failures caused by conversation flow, handoffs, user corrections, and accumulated context. Converra uses simulations to test full interactions.
Can Converra test prompt changes before deployment?
Yes. Converra runs the current agent and candidate change under identical simulated conditions, then deploys only changes that improve the target metric without regressions.