Build, version, and run AI agent evaluations directly from Claude, Cursor, or Copilot with the LangWatch MCP server. Generate test scenarios, benchmark models, and ship reliable agents faster — without leaving your coding environment.

Most AI agents look brilliant in demos. Then real users arrive — and things get complicated. Here's the systematic framework your team needs to keep agents reliable, safe, and genuinely improving in production.

LangWatch charges per seat, at €29 per seat with unlimited lite seats, to share results with stakeholders, leadership and customers, and slashed prices there on usage with 200k free events plus just $1 per 100k additional event

LLM monitoring tracks performance, cost, reliability, safety, and output quality of large language models in production environments.

We are in 2026, so why should YOU be the one debugging them? Why not just ask your AI assistant to do it for you?

LangWatch introduces prompt management as a shared engineering and product discipline, giving developers control over infrastructure while enabling product managers to iterate safely on user experience, tone, and behavior.

OpenClaw now ships a built-in OpenTelemetry exporter (`diagnostics-otel`). This post walks through the setup, the GenAI spec compliance details, and how to get it running.

OpenClaw is a great real-world example of something bigger: agentic systems that connect user inputs to real-world side effects

“LLM evaluation” means different things to different teams. This glossary explains experiments, monitoring, guardrails, and evaluators for AI agents.

Evaluate voice agents at scale with simulation-based testing. Compare the best voice agent evaluation tools in 2025, including LangWatch.

Comprehensive guide to monitoring, observability, evaluation, and optimization for production LLM and AI agent applications with LangWatch.

Discover the top 5 AI evaluation tools in 2026. Compare LangWatch, Braintrust, Arize, and others for testing, monitoring, and improving LLM applications in production

LangWatch wins for teams shipping production LLM applications with complex AI agents. Agent simulation testing, collaboration with PM's, OpenTelemetry-native tracing make it the most complete platform for agentic AI.

Compare the top 10 LLM observability tools in 2025. Expert analysis of LangWatch, Arize, Ragas, Deepeval and other platforms with features, pricing, and use cases. Including deployment and security needs

Learn how to test AI agents and ship them to production reliably using scenario-based testing, multi-turn evaluations, and framework-agnostic best practices.

AI events in 2026 developers shouldn’t miss. A curated guide to the top AI conferences worldwide, from research and GenAI to infrastructure and production AI.

LangWatch has a completely new dashboard and visual design, Evaluations, traces, and signals are now front and center. the LangWatch team wants to wish you happy holidays and a healthy, successful 2026.

AI prompt management tools help teams version, test, evaluate, and safely deploy prompts that control LLM and agent behavior, making AI systems reproducible, observable, and reliable in production.

Learn how Google’s Agent Development Kit and LangWatch work together to build, observe, and reliably operate multi-agent systems in production with full tracing, evaluation, and simulations.

LLM systems exist in constant motion. Prompts change. Models are swapped. User behavior evolves. New data sources are introduced. Each change reshapes the system’s failure surface.

Testing AI agents does require a new way of testing. Using Unit tests, Evaluations, Agent evaluations, simulations brings you a better way of agentic testing

Most AI agents fail in production. Better Agents CLI fixes that with scenario testing, evaluations, prompt versioning, and best-practice structure for dependable agent development.

Automate AI agent testing with LangWatch Scenarios and MCP. Skip manual checks—let your agent generate repeatable test files and validate real-world behavior.

Learn how to test real-time voice agents with LangWatch Scenario. Automate speech-to-speech testing, simulate user conversations, validate latency and performance, and run voice agent evaluations in CI—no microphones or manual testing required.

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Manage, test, and version AI agent prompts with confidence. LangWatch Prompt Playground connects prompts to real traces, evaluations, and optimization workflows.

LangWatch and Flagsmith together: helping teams de-risk moving AI in production across both the LLM-application releases.

LangWatch leads 2025 LLM evaluation with agent simulations, uniting testing, monitoring, and reliability

LangWatch gives enterprises the missing layer of control between LLMs, production systems, without the engineering overhead.

Learn how test-driven, need-based prompt engineering creates cheaper, and performing AI agents starting from an empty prompt.

Use this guide as the map for building and evaluating any RAG system

Discover Scenario: a domain-driven framework for AI agent testing & LLM evaluations with real-world simulations.

Discover why LLM evaluations, and AI agent testing are the backbone of reliable AI apps and to ship LLM apps with confidence

A practical guide to LLM evaluation and the LLM evaluation metrics that matter in production.

Learn how trace IDs enable observability across LLM apps. Track prompts, tokens, latency, & costs across AI workflows.

Discover 6 context engineering challenges blocking AI from scaling—and how LangWatch helps enterprises overcome them

LLMOps is the new DevOps. Discover what every developer must know to manage, test, and scale AI applications with confidence

What is LLM observability, and why is agent observability becoming critical for the future of AI systems?

Understand observability framework architecture for LLM apps. Learn design principles and monitoring strategies

OpenAI has released its newest flagship model, GPT-5 - Start evaluating the performance within LangWatch available now.

Discover how multiple LLM as a judge evals create a panel system that match with human preference for subjective AI quality.

Are evals dead? A pragmatic analysis

Learn why simulation is key to AI agent testing and how LangWatch Scenario brings scenario-based tests to your CI/CD.

Looking for a Humanloop alternative? These are top platforms for LLM evaluation, agent testing, and observability.

Discover how Scenario enables bulletproof testing of multilingual LLM agents through, ensuring your AI is production-ready

Watch simulated conversations play out in real-time with parallel execution, versioned runs, and interactive debugging.

Scripted simulations let you orchestrate how conversations unfold, when evaluations occur, and what custom logic runs.

Scenario is framework-agnostic, supporting any agent architecture through the AgentAdapter interface.

Explore top LangSmith alternatives, including LangWatch.ai the ideal platform for optimizing, evaluating, and monitoring.

Test your agents with scenarios directly in your codebase using Python and TypeScript.

Agent simulations are the new unit tests. You shouldn’t ship agents without simulations. 

Explore a detailed, developer-tested comparison of top AI agent frameworks in 2025, including LangGraph, DSPy, Agno and more.

Using LangWatch Scenario, the Rojoom product team built a daily automation way to ship new AI features with confidence.

LLMs make it easy to build agent demos. But building reliable and policy-aware agents takes more than a good prompt.

Create robust agent evaluation systems that catch AI agent bugs before production. Testing agent behavior and performance.

A practical playbook that frames evals as a CI/CD pipeline.

AI agents: The real challenge is making sure it works: reliably, accurately, and at scale.

Why reinforcement learning in LLMs isn't broken our eval methods are. Learn how we can unlock RL's potential in LLMs

How I stopped evaluating AI Agents like robots and started testing them like humans (with simulations)

This post breaks down the core components of LLM evaluation, from datasets to evaluators, explores best practices

Python SDK 0.2 adds native OpenTelemetry for GenAI apps—zero-break upgrades, better tracing, and faster debugging

LangWatch Selente - Atla, LLM Evaluations, prompt versioning, structured output, OpenTelemetry SDK, LLMops ISO certified

Key challenges teams face when put LLM-powered apps in production, and why continuous monitoring and evaluation is essential

Improving RAG agents: Build a basic system, Create evaluation data, run experiments

Learn how to effectively evaluate and test LLMs with LangWatch's new Evaluations Wizard. Improve your AI model performance

What is MCP? What does MCP stand for? And what is Function Calling?

The start of LLMOps: DevOps for Generative AI

Compare LangWatch, LangSmith, Braintrust, and Langfuse in this 2025 guide to LLM evaluation and monitoring tools

Scenario is an automated testing library for LLM agents that simulates real user interactions end-to-end.

How do we objectively know if the AI output is good? LLM evaluation reports & feedback loops

LangWatch partners with Adesso to support Enterprise companies with LLMops

The best GenAI teams are now introducing a critical new role: the AI PM (Quality) Lead.

LLMs can do amazing things, but only if they understand context. That context lives in the heads of domain experts.

Deploy an LLM evaluation framework that catches issues early. Reduce debugging time and improve AI quality.

What are LLM Hallucinations? What causes LLM hallucinations? How to monitor and evaluate LLM-apps

The Model Context Protocol is a new standard that lets AI agents easily connect to external tools and data sources.

Improve your LLM performance with real-time observability and optimization

LangWatch: €1M pre-seed funding round led by Passion Capital, with great support from Volta Ventures and Antler.

OpenAI, Anthropic, Deepseek and other LLM Providers keep dropping prices: Should you host your own model?

AI is evolving at speed, and the landscape in 2025 will be shaped across agents, multimodal data, and model efficiency.

LangWatch has been a part of HolidayHero's LLM production environment for over two months, overseeing thousands of guestchats

LangWatch Optimization Studio – Built for AI Engineers, by AI Engineers

Leverage the power of DSPy’s MIPROv2 without diving into complex code? Enter LangWatch’s Optimization Studio

LangWatch’s Optimization Studio, a more precise, scientific and better approach to prompt optimization

This tutorial guides you through building chatbots using Retrieval Augmented Generation with OpenAI in Python using FastAPI

How can we test in a probabilistic environment? Test Driven Development for LLM's

Data Flywheel: using your production data to build better LLM products

How Algomo increased the quality of their AI app with LangWatch

Understand what is The AI Team and what are Their Roles

Understand how to leverage datasets for LLM unit testing

DSPy and LangWatch: Log and track DSPy training sessions, evaluate performance, compare runs, and debug LLM pipelines.

LangWatch, a new innovative Amsterdam-based startup: Meet the Team

Driving to help AI leaders create RAG chatbots with minimal hallucinations

Journey of launching your first LLM-powered product is filled with potential and challenges.

Retrieval Augmented Generation. Its popularity continues to surge, offering various methods for its successful implementation

Discover how User Analytics for LLMs can transform AI interactions, revealing user behavior

Successfully integrating LLMs into your business requires careful monitoring and evaluation of options

Delve into the challenges of LLM hallucinations, explore their types, causes, and effective mitigation strategies

Learn valuable insights from the frontlines of GenAI product development

Prevent AI chatbots from handling out-of-scope questions, being manipulated, and addressing sensitive topics

Explore how to minimize AI hallucinations in LLMs

Explore the race of generative AI

Learn how AI start-ups can succeed by creating targeted generative AI solutions and effectively monitoring LLMs.

Struck.build + LangWatch = Improved AI Perfomance

Dive into a customer's journey with LangWatch, revealing how to successfully integrate AI into your organization.