Manual agent testing doesn’t scale.
AI behavior changes fast, and new model updates or data shifts can break logic you thought was stable.
Testing is manual, slow, and breaks with every prompt change
QA means writing a few test scripts in Notion, Excel or spreadsheets, it’s painful and impossible to scale
You only discover regressions when users complain
After deployment, issues like tool-call errors, latency spikes, or logic loops appear silently.
It’s hard to convince others the agent is actually ready for production
Stakeholders ask: ‘How confident are we this won’t break in the wild?’ and you don’t have data to show.
We don’t really know how the agent behaves across hundreds of scenarios
Your team can’t possibly test every edge case: ambiguous inputs, multi-turn confusion, API failures, long-context drift.
Simulated Scenarios – Run realistic multi-turn conversations to test your agent in varied situations.
Multi-Turn Chats – Support long back-and-forth dialogues and verify the agent’s performance at every step.
Context Retention – Verify the agent remembers and uses context
from earlier turns instead of forgetting important details.
Tool Call Checks
Ensure the agent invokes the right tools or APIs with the correct parameters during its tasks.
Interactive Debugging
Step through conversations in real-time and inspect each turn to quickly diagnose issues
Regression Prevention
Get early alerts when changes degrade your agent’s behavior, so you maintain high quality and confidence.
Seamless Integration
Plug into CI/CD and testing frameworks (like pytest/Jest) with minimal setup, in Python, TS, or Go.
The Agent Quality Platform
Unlike generic LLMOps tools or observability tools, LangWatch measures the real conversational agents with the right business metrics
Performance Analytics & ROI
Real business metrics tailored for conversational agents (escalation rate, first call resolution, NPS, latency, containment)
Agent Simulations
Realistic multi-turn, multi-thread testing in multiple scenarios before launch
Conversational-specialized Evaluations
ASR/TTS noise evaluation, intention detection accuracy, off-topic guardrail
Conversation Insights
Problem resolution rate, "I don't know" detection, topic categorization
Fix & Improvement Suggestions
AI-generated code and flow fixes via Git integration
Visualized conversation
Identify failure points and understand interaction patterns during testing
Loved by AI engineers working on chat & voice AI
Proven across industries
Enterprise teams trust LangWatch for mission-critical conversational AI
Customer Support
Banks, Retail, Telco
Outbound Sales
SaaS, B2C, Telco
Clinic Receptionist
Healthcare
Hospitality
Hotel Tech, Restaurant tech
Feedback Collection
Research
Legal intake
Legal Tech, Law firms
LangWatch is more than just test scenario.
It’s a complete LLMops platform for LLM-apps and Agentic systems focusing on Conversational, Chat and Voice AI:
Tracing, debugging, alerts and auto-generate datasets
LLM-as-judge or custom evals (tone, helpfulness, accuracy)
Start open-source; run locally or self-host
Fits in CI workflows
Prompt Management, and optimization with DSPy
