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All digests tagged LangSmith
Centralized LLM Gateways are critical for governing autonomous agents, managing cost, ensuring compliance (GDPR, HIPAA), and providing reliability through fallbacks and rate limits across the entire Agent Development Lifecycle.
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We're going to use LangSmith managed deep agents and Stagehand V4 tools. This allows us to build an agent that can interact with web pages by scrolling, clicking, and navigating across pages.
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Harmonic quadrupled user retention by shifting from brittle query graphs to Deep Agents. Learn how a model-plus-tools loop and file system abstractions solve context overload.
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This session demonstrates tracing a voice agent built with Google ADK and Gemini Live into LangSmith. Tracing provides visibility into tool calls, audio streams, interruption events, and token-level cost breakdowns for production readiness.
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Learn Loop Engineering: The art of building reliable, self-improving AI agents. Discover how Core, Verification, Event-Driven, and Self-Improvement loops transform non-deterministic models into production-ready systems.
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Mastering the Agent Development Lifecycle requires moving beyond demos to systematic iteration across Build, Test, Deploy, Monitor, and Govern. Learn how advanced tooling uses tracing and online evals to ensure agents behave reliably at scale.
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dcode introduces the /goal command for long-running tasks. It establishes visible acceptance criteria and uses a 'goal loop' to guide agents over hours, allowing mid-run steering and inspection via LangSmith tracing.
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Learn how LinkedIn built a hiring agent using LangGraph to cut time-to-interview by 60%. We cover the shift from static workflows to advanced plan-execute-replan loops and essential harness engineering techniques.
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Learn how to trace every Cursor agent turn into LangSmith by installing the plugin, setting three environment variables, and running an end-to-end task.
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