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The problem it's solving: teams building AI agents are fully in their coding assistant, but evaluation still requires logging into a separate platform, learning a new UI, and context-switching. The MCP closes that gap. What you can do from within your editor:

Ask your AI assistant to instrument your existing code with LangWatch tracing (it fetches the docs, adds imports, wraps functions with @langwatch.trace()) Generate simulation-based agent tests using Scenario — describe the behavior in plain English and it writes the pytest file

Search and inspect live traces from your project without touching the dashboard

Version and sync prompts to LangWatch's registry

Query cost/latency analytics in natural language

Set up LLM-as-a-judge evaluators that can gate CI/CD

Three real-world cases from the blog post:

A PM at an HR/payroll platform generated 63 agent test scenarios across 11 categories (happy paths, edge cases, wage tax mutations) in a single Claude conversation — no code written by hand.

A Senior AI Engineer migrated an entire Langfuse implementation to LangWatch in one session: Claude read the existing integration, rewired tracing, converted Jinja prompts to versioned YAML, and scaffolded model benchmarking notebooks comparing GPT-4o, Gemini, and Anthropic models.

A Dutch government AI team (LangGraph, multi-agent grant assessment system) used the MCP to build a full testing pyramid: end-to-end scenario tests, model comparison notebooks, and CI-gated quality evaluators before they'd written a single line of eval code themselves.

This weekend contributors from LangWatch, Orq, and Red Hat collaborated to add native OpenTelemetry instrumentation to OpenClaw, including support for the OTEL GenAI semantic conventions.

You can now trace agent execution steps, LLM calls, tool usage, and token-level costs in a standard OTEL pipeline instead of custom logging.

The idea: if you wouldn’t deploy an autonomous vehicle without simulation, why would you deploy an AI agent without pressure-testing it first?

Scenario lets you: Describe multi-turn user flows (e.g. “book a flight and cancel it”) Set success criteria ("user got confirmation + refund info") Have one agent simulate the user, testing how another agent performs the task Debug regressions, track failures, iterate on behavior

It’s like writing unit tests, but for conversations.

Why? Traditional evals fall short when testing multi-step flows, memory handling, tool use, or goal alignment. Inspired by simulation in autonomous vehicle testing, where cars are exposed to rare, edge-case situations at scale, Scenario allows similar validation loops for AI agents.

Instead of hoping your support bot or agent workflow handles complexity… you simulate it:

Works with any agent-framework you define