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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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Deep Agents v0.6의 새로운 기능
New in Deep Agents v0.6
Deep Agents 0.6 ships a code interpreter, harness profiles, streaming v3, delta channels, and ContextHub, making agents faster, cheaper, and more scalable.
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델타 채널: 장시간 실행 에이전트를 위한 런타임 진화
Delta Channels: How We’re Evolving our Runtime for Long-Running Agents
Long-running agents have a storage problem: checkpointing full state at every step grows at O(N²). DeltaChannel is a new primitive in LangGraph 1.2 that checkpoints only the diff e…
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법률 에이전트를 위한 효율적인 검증기 설계
Designing Efficient Verifiers for Legal Agents
A Harvey and LangChain Labs study on making LLM verifiers cheaper and more reliable for legal-agent evaluation and post-training.
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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 Engine 소개
Introducing Langsmith Engine
LangSmith Engine watches your production traces, clusters failures into named issues, and proposes targeted fixes and eval coverage. Stop manually triaging agent failures.
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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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쿠버네티스 기반 자체 호스팅 LangSmith를 위한 미션 컨트롤
Mission Control for Self-Hosted LangSmith on Kubernetes
How Mission Control helps teams operate self-hosted LangSmith on Kubernetes with in-cluster config, preflight checks, health views, releases, and diagnostics.
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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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Auth Proxy가 LangSmith 에이전트 샌드박스를 보호하는 방법
How Auth Proxy secures LangSmith agent sandboxes
Agents need credentials and network access to do useful work, but those capabilities create new security risks. This post explains how Auth Proxy keeps secrets out of LangSmith San…
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토큰 스트림에서 에이전트 스트림으로
From Token Streams to Agent Streams
Move beyond token streaming. Learn how the latest streaming primitives in Deep Agents, LangChain, and LangGraph enable typed events, scoped subscriptions, subagent visibility, mult…
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딥 에이전트의 인터프리터: 도구 호출과 샌드박스 사이의 코드
Interpreters in Deep Agents: Code Between Tool Calls and Sandboxes
Deep Agents now supports interpreters: small embedded runtimes where agents write code to coordinate tools, hold working state, and decide what enters model context.
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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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다양한 모델에 맞춰 딥 에이전트 조정하기
Tuning Deep Agents to Work Well with Different Models
Deep Agents was previously designed in a generic way to work well across model families. Today we’re adding model-specific profiles to adjust prompts, tools, and middleware. We shi…
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Interrupt에서 공개한 모든 제품들
Everything we shipped at Interrupt
From autonomous debugging to one-line deploys, here's every product LangChain launched at Interrupt 2026 to help teams build, test, and ship agents in production.
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우리는 SmithDB를 개발했습니다: 에이전트 옵저버빌리티를 위한 데이터 레이어
We built SmithDB, the data layer for agent observability
Introducing SmithDB: LangSmith's purpose-built distributed database for agent observability, delivering up to 12x faster performance with full portability.
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LangSmith LLM 게이트웨이: 에이전트 생명주기에 내장된 런타임 거버넌스
LangSmith LLM Gateway: runtime governance built into the agent lifecycle
Introducing LangSmith LLM Gateway: runtime governance for AI agents with spend limits, PII redaction, and trace continuity, built directly into LangSmith.
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관리형 Deep 에이전트: 프로덕션 Deep 에이전트를 가장 빠르게 배포하는 방법
Managed Deep Agents: the fastest way to ship a production deep agent
Run deep agents in production with durable execution, sandboxes, tool access, and LangSmith observability, without building the runtime yourself. Now in private beta
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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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에이전트 관찰성: 학습을 강화하기 위한 피드백의 필요성
https://www.langchain.com/blog/agent-observability-needs-feedback-to-power-learning
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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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오픈 모델이 임계점을 돌파했다
Open Models have crossed a threshold
Open models like GLM-5 and MiniMax M2.7 now match closed frontier models on core agent tasks — file operations, tool use, and instruction following — at a fraction of the cost and …