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LangyNewThe automated AI engineer: reads your traces, writes the tests, and opens the pull requestAgentic AI TestingRun realistic user scenarios against your agent to catch issues before productionLLM EvaluationMeasure response quality and accuracy so you ship agents that hold up in productionLLM ObservabilityTrace every agent step and monitor cost and latency with full production visibilityAI GovernanceNewGovern every model, key, and tool. Virtual keys with budgets, routing policies, and a full audit trailPrompt ManagementVersion, deploy, and A/B test prompts as code, with full history and GitHub syncVoice AITest and simulate your voice AI agents at scale, before they talk to customersLLM Red-teamingSimulated attacks that uncover safety and security gaps in AI agentsTrack your Claude Code UsageFull trace history and token spend for Claude Code, Codex, and every coding agent
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LangWatch Research

Measured studies of how AI agents behave in production, written up in full so the method and the numbers can be checked rather than taken on trust.

  • Finding the Optimal Context Window for Coding Agents: A Case Study

    Rogerio Chaves · August 2, 2026

    287,748 API calls · 2,451 agents · 162 days · 873 compactions · 201 audited steps

    A case study measuring what context costs, how much of it gets used, and what compaction destroys, across 162 days of coding-agent telemetry.

    Read the paper →
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