When your agents
get complex

Simulation-based AI agent testing and evaluation that turns unpredictable agents into reliable production systems.

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claude code~/voice-agent

improve agent · vibe-eval loop

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simulation — qualified senior candidate

11/11 · 100% · 50.46s

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AI agents are still tested by hand, breaking in production.
LangWatch brings loop engineering to agent testing and evaluation.

An agent can take a hundred paths to the same goal, testing them by hand catches only a few.

The best teams run agent simulations as continuous testing and evaluation, so reliability climbs every release.

Spec-driven agent building

Turn your requirements into agent tests automatically.

Speed up development

Set up a self-improving agent loop.

Replicate and fix issues from production

Turn a production trace into a simulation and prove the fix.

Specs

Simulations

Agent
Improvement

Test. Evaluate. Observe.

One stack for the full agent lifecycle. Open by default, OpenTelemetry-native, runs against any model.

01 / 03

Agent testing

Test agents end-to-end with multi-turn simulations across text and voice. A user simulator drives real conversations, a judge scores every turn, and adversarial runs surface the failures single-shot evals miss.

Multi-turn simulations of real users
Text and voice agent simulations
Adversarial red-teaming runs
Per-turn judge with pass/fail criteria
Powered by Scenario, MIT-licensed OSS
Runs locally or in CI

Explore Scenario

langwatch · agent-testing

simulation — qualified senior candidate

11/11 · 100% · 50.46s

0:00 / 0:17

Hello, and thank you for joining the interview. I am an AI assistant conducting this interview — the conversation may be recorded and assessed, and you can request a human at any time. Let's start: could you tell me about a recent project where you led the development of an LLM evaluation tool?

Our AI tests your AI

Langy turns a PM's goal into a full Scenario test plan, then turns the failures into pull requests.

PMs own the spec. Devs stay in flow. Nothing slips through.

PM writes the goalno codePlain English. No code, no YAML. The brief is the spec.
Langy drafts the planlivePicks the simulator, generates the scenarios, writes the JudgeAgent rubric.
Scenario runs in parallelparallelMulti-turn conversations against your agent, concurrent across projects.
JudgeAgent scores itsignedYour rubric, audited. Faithfulness, policy adherence, de-escalation.
Regressions become PRsready to shipLangy drafts the prompt revision. Devs review and ship via Prompt Registry.