The autonomous improvement loop for AI agents.

Every fix ships with a production verdict — verified, not fixed, or confounded. You approve what goes live.

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Text and voice · LangSmith or Langfuse · 5-minute connect, no code changes.

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
Failure rate22 → 0 incidents0%
Verified · real traffic
Invents pricing, contact emails, product features
Trust erosion · 22 incidents → 0 after Apr 23 fix · 97 conversations sampled
Works with
Vercel AI SDK
LangChain
LangSmith
Langfuse
OpenTelemetry
Any LLM provider

Diagnose. Fix. Simulate. A/B test. Verify.

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
SalespeakCase 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.

Disqualified on attendee count alone — ignored product-fit signals (smart badges, contact sharing, sustainability).
Intent 25Relevance 15Context 20Tool Use 30

From symptom to tested fix. Across every agent.

One queue across every agent. Each row is a pattern, a fix, and a verdict.

  • 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.

Stop shipping and hoping.
Every fix gets a verdict.

Converra surfaces failures, drafts fixes across prompts, routing, guardrails and config, then A/B tests the ones you approve on real traffic.

Get started free