Downstream of observability
Trace tools show what happened. Converra turns those traces into diagnosed failure patterns, targeted fixes, and validated agent changes.
AI agent infrastructure
Infrastructure for improving production AI agents
Converra is the layer between agent observability and deployment. It diagnoses what broke, generates the fix, validates it in simulation, and verifies whether production behavior improved.
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.
Production agents need more than dashboards
Most AI infrastructure tells teams that an agent failed. The hard part is what comes next: isolating the failure, deciding what to change, proving the change will not regress other behavior, and confirming it worked on real traffic.
Built around production agents
The system starts from real conversations, real failure rates, and real business outcomes instead of hand-written toy eval sets.
Simulation before deployment
Every candidate fix competes against the current agent in head-to-head simulated conversations before it reaches customers.
Verification after deployment
Converra measures before and after production conversations so teams know which fixes actually worked.
The operating loop
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.
- 1Connect traces from LangSmith, Langfuse, SDK, API, or imported conversations.
- 2Diagnose the exact failing step, root cause, and business impact of recurring issues.
- 3Generate targeted prompt or configuration fixes for the failure pattern.
- 4Run head-to-head simulations and regression checks before anything ships.
- 5Deploy the winner with audit trail, then verify the impact from production traffic.
Where Converra belongs in the stack
Use Converra after traces, logs, and eval signals exist. Observability captures the evidence. Converra turns that evidence into a tested, governed, production-verified improvement loop.
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 infrastructure?
AI agent infrastructure is the production layer that helps teams operate, test, improve, and govern AI agents after launch. Converra focuses on the improvement loop: diagnose failures, generate fixes, validate changes, deploy winners, and verify production impact.
Is Converra observability, evaluation, or optimization?
Converra sits across all three, but its center of gravity is optimization. It uses observability data, runs evaluations and simulations, then turns the evidence into tested fixes that improve production behavior.
How is this different from GPU or cloud AI infrastructure?
Converra is not compute infrastructure. It is operational infrastructure for production AI agents: quality, testing, improvement, deployment, and verification.