Observability captures evidence
Traces, spans, transcripts, and metrics are the raw material. They show where a conversation went and what the agent did.
Observability vs optimization
Observability tells you what happened. Optimization changes what happens next.
Converra sits downstream of AI agent observability. It uses traces and production conversations to diagnose failures, generate fixes, test candidates, and verify real improvement.
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.
Visibility is necessary, but it is not the finish line
Most teams already know their agents fail. The operational bottleneck is deciding what to change, testing the change safely, and proving it improved production behavior.
Optimization decides what to change
Converra classifies the root cause and generates targeted prompt or configuration variants that address the failure.
Testing proves the candidate is better
The proposed fix has to beat the current agent in head-to-head simulation and pass regression checks.
Verification proves the deployed fix worked
After deployment, Converra measures before/after failure rates from production conversations.
How the two layers work together
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.
- 1Use observability tools to collect traces, messages, tool calls, and user outcomes.
- 2Send that evidence to Converra through connectors, SDK, API, or import.
- 3Let Converra diagnose recurring failure patterns and generate candidate fixes.
- 4Validate candidates through simulation and regression tests before deployment.
- 5Verify deployed fixes against real production conversations.
Use both layers for a complete loop
Observability gives teams the factual record. Optimization converts that record into validated behavior change. Without observability there is no evidence; without optimization the evidence becomes engineering toil.
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
Do I still need observability if I use Converra?
Yes. Observability is the evidence source. Converra uses that evidence to automate diagnosis, testing, optimization, deployment, and verification.
Is Converra a replacement for LangSmith or Langfuse?
No. Converra can plug into LangSmith and Langfuse. Those tools capture traces; Converra turns trace evidence into tested fixes and production-verified improvements.
When should a team add optimization?
Add optimization when the team is already seeing repeated agent failures and engineers are spending time reading logs, editing prompts, manually testing changes, and checking whether the fix worked.