Confident AI vs Converra: Production Agent Evidence
Confident AI is the production monitoring service around the open-source DeepEval framework. Its documentation describes evaluating traces, spans and threads in production, surfacing failures and turning them into goldens for the next evaluation dataset. Converra fixes production agent behavior: diagnose the failing step, generate a bounded prompt or configuration change, compare it with the current agent, ship what you approve, and return a production verdict.
Both workflows use production evidence to improve what is tested next. The useful distinction is whether detecting a failing trace, validating a change before release and measuring the approved change afterward remain separate evidence. This page uses the same dimensions as the agent optimization hub.
The short version
Choose Confident AI when you want DeepEval metrics to surface and route failing production traces into future evaluation datasets. Choose Converra when you need a bounded behavior change compared with the current agent before approval and a per-fix production verdict after release.
Confident AI vs Converra, side by side
Each row answers the same question for both products. Confident AI statements come from its own documentation, reviewed on the date shown above; sources are listed at the end.
- Dimension
- Primary job
- Confident AI
- Production monitoring and evaluation for LLM applications, paired with the DeepEval evaluation framework
- Converra
- Runs the improvement loop for production agents: diagnose, fix, simulate, ship with approval, verify
- Dimension
- Production evidence
- Confident AI
- Instrumented traces, spans and threads evaluated against configured DeepEval metrics
- Converra
- Imports traces through LangSmith and Langfuse connectors, or receives conversations through its SDK and traces API, which accepts OpenTelemetry-compatible data; it is not a tracing tool
- Dimension
- Failure diagnosis
- Confident AI
- Surfaces failing traces and clusters failure modes so teams can investigate and create future test cases
- Converra
- Step-level diagnosis that labels and counts recurring failure patterns across conversations
- Dimension
- Fix generation
- Confident AI
- Public documentation focuses on finding failures and turning them into goldens, rather than proposing a bounded application fix
- Converra
- Generates bounded prompt or configuration variants aimed at a diagnosed pattern
- Dimension
- Testing before release
- Confident AI
- DeepEval runs selected metrics against test cases, datasets and traced components before release
- Converra
- Head-to-head multi-turn simulation against the current agent on the same personas, plus regression scenarios
- Dimension
- Deployment and approval
- Confident AI
- Teams keep their own CI and deployment workflow; DeepEval can gate development tests in CI
- Converra
- Review-first by default: a GitHub pull request or a direct update a person applies; optional auto-deploy requires a passing regression test
- Dimension
- Verdict after release
- Confident AI
- Documents continuous evaluation and failure monitoring, not Converra's per-fix verified, not fixed or confounded production decision
- Converra
- Measures the targeted failure on live traffic and marks the fix verified, not fixed, or confounded
- Dimension
- Hosting
- Confident AI
- DeepEval is an open-source framework; Confident AI is its optional cloud service for production monitoring and shared evaluation workflows
- Converra
- Managed cloud service; the Enterprise plan lists an on-prem or VPC option
Both products learn from production behavior
Confident AI documents a loop in which production traces, spans and threads are evaluated against chosen metrics. Failing traces can be routed to annotators, grouped into failure modes and turned into goldens for the next dataset. That is useful evidence for deciding what the team should test next.
Converra also starts with production behavior, but it narrows the next action to a diagnosed failure pattern and a bounded prompt or configuration candidate. A production trace can reveal a real issue without establishing why it happened or which application change should ship.
An evaluation dataset validates a candidate before release
DeepEval supports development and CI evaluation of test cases, including traced components and complete agent trajectories. Re-running a revised prompt or model configuration against the goldens created from production failures can catch a regression before deployment. The quality of that result depends on the selected metrics, representative cases and the behavior the tests cover.
Converra compares a bounded behavior change with the current agent before approval, including regression scenarios for behavior the baseline already handles. Keep a passing evaluation, an approved release and later production evidence separate so a strong pre-release score is not called a verified fix.
Production evidence still needs an attribution boundary
A support agent might begin giving wrong refund answers after a model update. Confident AI can surface the failing trace and help the team make it a future golden. Before release, test a candidate change against eligible refunds, expired returns, ambiguous purchase dates and escalation cases.
After an approved release, measure the same failure on comparable production traffic. Converra records a verified, not fixed or confounded production verdict when its evidence rules support one. Confounded does not prove failure: overlapping edits, model changes or traffic-mix shifts can prevent attribution. This is a workflow example, not customer performance evidence.
When to choose Confident AI
You use DeepEval and want configured metrics to evaluate production traces, spans or multi-turn threads.
You want failing production evidence to become goldens or datasets for the next development evaluation.
Your team owns the prompt, code, CI and deployment workflow and primarily needs monitoring plus evaluation evidence.
When to choose Converra
A recurring production behavior failure needs a bounded prompt or configuration candidate, not only a new evaluation case.
You want the current agent and candidate compared head-to-head before approving a change.
You want the approved behavioral fix tracked afterward with a verified, not fixed or confounded production verdict.
Does Converra replace Confident AI?
They overlap on converting production failures into more useful test evidence. Converra does not replace DeepEval metrics, trace instrumentation or a team's CI gate.
Do not assume a native Confident AI connector. Converra's documented native trace connectors are LangSmith and Langfuse. For another stack, use the Converra SDK integration guides to send supported conversation data and verify capture coverage before relying on the combined workflow.
Frequently asked questions
What is the difference between DeepEval and Confident AI?
DeepEval is the open-source framework for evaluating LLM application behavior. Confident AI is its production monitoring service, which evaluates instrumented production traces and helps turn failures into future evaluation cases.
Does a passing DeepEval run prove the production agent is fixed?
No. A passing run is pre-release evidence against the cases and metrics selected. You still need an approved release, comparable production evidence and clear attribution limits before calling a behavior fixed.
What does Converra add after a production finding?
Converra turns a diagnosed behavior failure into a bounded candidate, compares it with the current agent in simulation, keeps regression coverage, and records verified, not fixed or confounded evidence after an approved deployment.
Can I use Converra from Claude Code, Codex or Cursor?
Yes. The remote MCP server exposes authorized diagnosis, optimization, simulation and deployment-impact tools. Start with the coding-agent setup guide and approve any operation that changes state or incurs inference cost.
Written by
Oren CohenFounder of Converra. Previously founded Buildup (acquired by Stanley Black & Decker) and, as VP Product Growth at Totango, owned AI end-to-end from design through production.
Related reading
Take a production failure through the fix loop
Converra fixes the behavior, not the code. Diagnose the failing step, compare a bounded fix with the current agent, approve what goes live, and get a production verdict: verified, not fixed or confounded.