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에이전트 개발 라이프사이클: AI 에이전트 구축, 테스트, 배포 및 모니터링 | LangChain
The Agent Development Lifecycle: Build, Test, Deploy & Monitor AI Agents | LangChain
Learn how leading engineering teams ship AI agents reliably and repeatedly using a four-phase agent development lifecycle: Build, Test, Deploy, and Monitor. Includes guidance on ev…
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루브릭 소개: 자신의 작업을 평가하고 수정하는 에이전트 구축
Introducing Rubrics: Build Agents that Evaluate and Correct Their Work
Deep Agents' RubricMiddleware adds a self-evaluation loop to your agent runs. Set a rubric, configure a grader, and get reliable outputs on tasks where correctness matters.
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Rippling이 Deep Agents와 LangSmith로 6개월 만에 프로덕션 AI를 구축한 방법
How Rippling built production AI in 6 months with Deep Agents and LangSmith
Rippling uses LangChain Deep Agents and LangSmith to run cross-domain AI across HR, IT, finance, payroll, and global operations.
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인터프리터 스킬: 에이전트를 위한 워크플로우 구축
Interpreter Skills: Building Workflows for Agents
Interpreter skills extend agent skills with a TypeScript module the agent can import and run. This lets you build more capable workflows with your agents.
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LangSmith 샌드박스가 정식 출시되었습니다
LangSmith Sandboxes are Generally Available
Run AI agents safely with LangSmith Sandboxes (GA): kernel-isolated microVMs with snapshots, parallel forks, service URLs, and auth proxies. Built for coding agents, CI agents, and…
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LangSmith 엔진 구축 방법: 에이전트 개선을 위한 우리의 에이전트
https://www.langchain.com/blog/how-we-built-langsmith-engine-our-agent-for-improving-agents
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2026년 4월: LangChain 뉴스레터
April 2026: LangChain Newsletter
April means we're officially counting down to Interrupt. We’ve got two more meetups on the agent improvement loop before April officially closes out in New York and San Francisco. …
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프로덕션 에이전트 실패 수정: Interrupt 2026 회고 | LangChain 뉴스레터
Fixing agent failures in production: Interrupt 2026 recap | LangChain Newsletter
Recapping two days of Interrupt 2026 — LangSmith Engine, Sandboxes GA, LangChain Labs, and 23 talks from teams at LinkedIn, Rippling, Cisco, and more. Now on demand.
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Lyft가 LangGraph와 LangSmith로 구축한 자체 서빙 AI 에이전트 플랫폼
How Lyft Built a Self-Serve AI Agent Platform with LangGraph and LangSmith
Lyft used LangGraph and LangSmith to build a self-serve AI agent platform for customer support, cutting agent development from months to weeks.
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에이전트 하네스의 해부
The Anatomy of an Agent Harness
Learn how agent harnesses transform AI models into autonomous work engines. Explore core components: filesystems, sandboxes, and memory.
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LangChain Labs 소개
Introducing LangChain Labs
LangChain Labs is a new applied research effort focused on continual learning for agents, with partners advancing open research on self-improving AI systems.
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LangSmith Context Hub 소개
Introducing LangSmith Context Hub
Introducing Context Hub in LangSmith: a central place to store, version, and collaborate on the files that shape how your AI agents behave.
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딥 에이전트, LangSmith, 병렬 처리를 사용한 회사 실사 에이전트 구축
https://www.langchain.com/blog/building-a-company-due-diligence-agent-with-deep-agents-langsmith-and-parallel
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Open SWE: 내부 코딩 에이전트를 위한 오픈소스 프레임워크
Open SWE: An Open-Source Framework for Internal Coding Agents
Built on Deep Agents and LangGraph, Open SWE provides the core architectural components for internal coding agents.
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Interrupt 2026: 엔터프라이즈 규모의 에이전트
Previewing Interrupt 2026: Agents at Enterprise Scale
This year, we're doing it again. Interrupt 2026 is May 13–14 at The Midway in San Francisco, and the lineup, the format, and the scale have all leveled up.
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AI 에이전트란 무엇인가?
What is an AI agent?
Introducing a new series of musings on AI agents, called "In the Loop".
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에이전틱 엔지니어링: AI 에이전트 스웜이 소프트웨어 엔지니어링을 재정의하는 방법
Agentic Engineering: How Swarms of AI Agents Are Redefining Software Engineering
Multi-agent systems that mirror real engineering teams — not just code faster — can cut debug time by 93% and compress cross-team delivery. Here's the architecture built on LangGra…