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하모닉이 딥 에이전트와 랭스미스로 스카우트를 재구축하여 D-리텐션 4배 달성한 방법
https://www.langchain.com/blog/how-harmonic-rebuilt-scout-on-deep-agents-and-4xd-retention-with-langsmith
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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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Endava가 Codex로 에이전틱 조직을 구축하는 방법
How Endava builds an agentic organization with Codex | OpenAI
Learn how Endava uses Codex to build an agentic organization, accelerating software delivery and reducing requirements analysis from weeks to hours.
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Sea가 보는 에이전트 소프트웨어 개발의 미래: Codex와의 협력
Sea's View on the Future of Agentic Software Development with Codex | OpenAI
Sea Limited's CPO explains why the company is deploying Codex across engineering teams to accelerate AI-native software development in Asia.
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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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AgentMemory의 작동 원리와 에이전트와 함께 실제로 사용하는 방법
How agentmemory works, and how to actually use it to with your agent
Trending, 12 hooks, 51 MCP tools, and a triple-stream retrieval pipeline that scores 95.2% R@5 on LongMemEval-S
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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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효과적인 AI 에이전트 구축
Building Effective AI Agents \ Anthropic
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장시간 실행 에이전트를 위한 효과적인 하네스
Effective harnesses for long-running agents Nov 26, 2025
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관리형 에이전트 확장: 두뇌와 손 분리
Scaling Managed Agents: Decoupling the brain from the hands Apr 08, 2026
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Claude Opus 4.8 소개
Introducing Claude Opus 4.8 Product May 28, 2026 An upgrade to our Opus class of models, with stronger performance across coding, agentic tasks, and professional work, and the consistency to handle long-running work.