Text and voice · LangSmith or Langfuse · 5-minute connect, no code changes.
Regression resolved · Apr 23
Production-verified fix from a real Converra customer's agent. The Trust-erosion failure pattern “Invents pricing, contact emails, product features” went from 22 incidents to 0 across 97 sampled production conversations after the Apr 23 fix, confirmed by Converra's before/after verification on real traffic.
Production fix · Verified97 conversations · 34 customers
Five things every team eventually builds badly. Converra runs the loop: simulation for confidence, production A/B for proof.
01 · Diagnose
Cluster every failure across the fleet.
Production trace mining and regression suites group findings by root cause, not by ticket.
Trust erosion97
Off-policy escalation24
Tool-call loop11
Hallucinated tools6
02 · Fix
Targeted agent changes. No retraining.
prompts/support-agent.md+5 -0
87## Saturation rule
88If user has refused twice OR session > 8 turns
89without progress, exit extraction mode and
90offer human handoff via "escalate_to_human".
91
92## Close discipline
03 · Simulate
Break it in simulation. Fix it before production.
+8
+14
+22
+11
+19
-3
+15
P1P2P3P4P5P6P7
6 / 7 improved · 1 blocked
04 · A/B test in production · Verify
Variant on real traffic. Verdict from real users.
variant B·20% trafficlive
Control 80%Variant B 20%
+18.4%
Task completion vs. control
N=412
Real-user conversations
verified
Verdict on real traffic
verifiednot fixedconfoundedEvery variant gets a verdict from real traffic.
“The quality of our research interviewer is critical to Perspective's mission. Converra keeps it improving, without us hiring an AI engineer for it.”
Guy Nirpaz, Founder and CEO, Perspective AI
How agent improvement works today.
Engineers read logs and evals, hand fixes to coding agents, and ship hoping it's better. Every new customer and every new agent adds edge cases faster than engineering can keep up.
That's why most agents degrade in production.
Production verified
Case study
Converra optimized Salespeak's production agents, catching fabricated pricing details and routing failures before they reached real prospects.
100%
Trust-erosion pattern eliminated
Orchestrator stopped fabricating pricing, VAT, and infrastructure details. Zero recurrences since Apr 23 deploy.
68%
Fewer routing failures
Mis-routed queries dropped from 16% to 5% of production traffic after Apr 25 deploy. Verified.
5
Fixes shipped this batch
2 verified wins in production. ↓46% aggregate failure rate across the agent fleet.
From their CTO
“Converra helps us find issues we couldn't pinpoint in a multi-agent environment, and fix them without the guesswork.”
Lior Mechlovich, Co-founder and CTO, Salespeak
Every fix verified in production.
We measure real conversations before and after each deploy. If a fix didn’t move the number on real traffic, we say so.
A production fix on Salespeak’s Orchestrator Agent reduced the “pitches features before understanding what the prospect actually needs” failure pattern from 21% to 9% across 64 production conversations, verified by before/after measurement on real traffic.
From production · Orchestrator Agent64 traces
Pitches features before understanding what the prospect actually needsVerified
Discovery gap · surfaced on Orchestrator Agent
Before fix
21%failure rate
After fix
9%failure rate
↓12 pp reduction · 64 production conversations measuredStatus: Verified
2 verified·0 regressions·3 awaiting verification
Pinpoint which agent broke, at which step.
Converra traces failures to the exact agent and exact turn in multi-agent conversations — with root cause classification and per-step scoring.
Conversation #4091 — SDR AgentExample
Score: 12Fix this →
Aggressive volume-only disqualification threshold in prompt
Step 1 · User
"We use smart badges for our events but need a less expensive alternative. Must share contact details between exhibitors and delegates. Sustainability is important."
Step 2 · Agent
Asks about event volume — how many events planned in the next 12 months. Good qualifying question.
Step 3 · User
"2 conferences, about 200 attendees each."
Step 4 · AgentRoot causePrompt Issue
"Based on your current event volume, we may not be the best fit." Redirected to a community page. Prospect dismissed.
One queue across every agent. Each row is a pattern, a fix, and a verdict.
Pattern
Agent
Impact
Status
Invents pricing, contact emails, product features
Orchestrator
↓ 100%
Verified
Pitches features before understanding what the prospect actually needs
Orchestrator
↓ 57%
Verified
Routes queries to the wrong specialist, misreading user intent
4 agents
↓ 69%
Improving
Answers questions without asking what the prospect actually needs
Discovery Agent
↓ 26%
Improving
Skips qualifying questions after users express interest
4 agents
↓ 26%
Monitoring
Invents pricing, contact emails, product features
Orchestrator·↓ 100%·Verified
Pitches features before understanding what the prospect actually needs
Orchestrator·↓ 57%·Verified
Routes queries to the wrong specialist, misreading user intent
4 agents·↓ 69%·Improving
Answers questions without asking what the prospect actually needs
Discovery Agent·↓ 26%·Improving
Skips qualifying questions after users express interest
4 agents·↓ 26%·Monitoring
One line to close the loop. Nothing ships without proof.
Deploy automatically, or review first. You choose when.
Fully automatic integration
Add one import. Converra captures every LLM call, generates fixes in simulation, and serves winning variants — automatically.
Captures every LLM call — OpenAI, Anthropic, Gemini, and more
Auto-detects prompts by content hash — no manual registration
Serves winning variants at runtime — no redeployment needed
Fail-safe: if Converra is down, your agent runs unaffected
Terminal
# One command. Zero code changes.$ CONVERRA_API_KEY=sk_live_... \
node --import converra/auto server.js# Conversations captured. Fixes deployed automatically.
Built for production
Every fix survives simulation testing and regression checks before it touches your production agent.
Simulation tested
Every fix runs head-to-head against the current version before deployment.
Instant rollback
If any metric regresses on real traffic, the fix rolls back automatically — no ticket, no manual step.
Your data stays yours
No training on your data. Scoped access to traces only. Full audit trail.
Regression tested
Every improvement checked against scenarios your agent already handles well.
Production verified
Every deployed fix measured before/after. Catches what didn't work.
Trust through proof, not permission.
Full audit trail for every change. See what was fixed, why, and what improved.
Questions teams ask before connecting an agent
How the diagnose → fix → simulate → deploy → verify loop works in practice.
How do I automate AI agent improvement?
Connect your agent traces to Converra via SDK, API, or observability connector. Converra diagnoses failures, generates targeted fixes, tests them in simulation against personas derived from your real data, and deploys winners automatically. Each fix is verified from production data after deployment.
How do you trace failures across a multi-agent pipeline?
Converra auto-discovers your agent architecture and maps the handoffs between agents, then diagnoses each failure at the step and turn where it originated — not the last node that touched it. Cascading failures usually start upstream (a bad handoff, a routing mistake, lost context), so the question shifts from "which agent failed?" to "which agent caused the failure?" Single-agent eval tools miss these cross-agent breaks.
How do I catch prompt regressions before they hurt conversion?
Every proposed change is run against a golden set of scenarios your current agent already handles well, generated from real production conversations. If a change improves one behavior but degrades another, it is blocked with a side-by-side comparison showing exactly what regressed — so a fix for one cohort never quietly costs you conversions in another.
Production verification means measuring whether a deployed fix actually worked using real production data — not just simulation scores. After every deployment, Converra compares failure rates before and after the change using real conversations. Each fix is marked verified (it worked), not fixed (it didn't), or confounded (too many variables to tell). Most agent improvement platforms stop at simulation scores or eval datasets; Converra closes the loop with before/after measurement on real production traffic.
Regression testing ensures that improvements to one behavior don't degrade others. Converra generates golden test sets from scenarios your current agent handles well, then runs every proposed change against them. If any regression is detected, the change is blocked with full side-by-side comparison showing what degraded.
What is simulation testing for AI agents?
Simulation testing runs candidate fixes through realistic multi-turn conversations before anything reaches production. Converra generates personas from real customer behavior, scores each run against the outcome that matters, and only promotes changes that beat the current agent without introducing regressions.
Agent drift is the gradual degradation of AI agent performance in production over time. As new customers, edge cases, and model updates accumulate, agents stop doing what they were designed to do. Most teams don't detect drift until users complain. Converra monitors for drift continuously and generates fixes before it compounds.
How does Converra compare to building agent improvement in-house?
Building in-house means engineers read logs, write fixes, build test infrastructure, deploy manually, and hope it's better. This works at small scale but breaks as agents and customers multiply — every new edge case competes for engineering time. Converra automates the full loop so engineers can build instead of maintain.
No. You can connect via LangSmith or Langfuse data connectors with zero code changes. Or add one line with the SDK middleware. Converra observes and improves outside your live stack.
How is Converra different from DSPy or Opik optimizers?
DSPy and Opik are developer-initiated optimization tools — you write code, trigger runs, and deploy manually. Converra runs continuously in production without engineering involvement. It also includes regression testing, governed deployment, and production verification that SDK-based tools don't have.
Yes. Converra auto-discovers your agent architecture, maps handoffs between agents, and diagnoses failures at the system level — not just individual agents. The orchestrator is often the weakest link, and Converra catches cross-agent failures that single-agent tools miss.
Case study