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href=\"https://docs.snowflake.com/ko/user-guide/snowflake-cortex/cortex-agents\"\u003ECortex Agents\u003C/a\u003E는 Snowflake 내에서 AI 애플리케이션을 구축할 수 있도록 관리형 런타임을 처음부터 제공해 왔습니다. 지난 1년 동안 고객들은 첫 번째 에이전트를 배포하는 단계에서 엔터프라이즈 배포를 관리하는 단계로 빠르게 이동했습니다. 이러한 전환은 오케스트레이션, 실행, 거버넌스를 둘러싼 새로운 운영 과제를 드러냈습니다.\u003C/p\u003E\n\u003Cp\u003E이에 Snowflake는 오늘 Cortex Agents에 새로운 기능을 추가해, 엔터프라이즈 AI 에이전트를 대규모로 구축하고, 배포하며, 운영하는 과정에서 발생하는 운영상의 복잡성을 보다 쉽게 관리할 수 있도록 지원합니다.\u003C/p\u003E\n\u003Ch2\u003E한눈에 보기\u003C/h2\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cb\u003E구축\u003C/b\u003E\u003Cul\u003E\n\u003Cli\u003E\u003Cb\u003ECoding Agent(곧 퍼블릭 프리뷰 제공):\u003C/b\u003E CoCo를 구동하는 것과 동일한 런타임 기반의 관리형 코딩 에이전트를 구축하고 모든 애플리케이션에 배포할 수 있습니다.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003ESkills Package(곧 퍼블릭 프리뷰 제공):\u003C/b\u003E 엄선된 스킬 세트를 하나의 오브젝트로 패키지하고 단일 URI로 에이전트에서 참조할 수 있습니다.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EAgent Toolset(곧 퍼블릭 프리뷰 제공):\u003C/b\u003E 중복된 도구 정의를 다시 작성하고 유지 관리하는 대신, 현재 에이전트 사양에서 다른 에이전트의 도구를 참조할 수 있습니다.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003ETool Search(곧 퍼블릭 프리뷰 제공):\u003C/b\u003E 모든 정의를 미리 로드하지 않고도 필요한 도구를 온디맨드로 검색할 수 있습니다.\u003C/li\u003E\n\u003C/ul\u003E\n\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cb\u003E실행\u003C/b\u003E\u003Cul\u003E\n\u003Cli\u003E\u003Cb\u003EAsync Agent API(곧 GA 제공):\u003C/b\u003E 연결을 계속 유지하지 않아도 장시간 실행되는 작업을 백그라운드에서 처리할 수 있습니다.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003ECode Execution Tool(곧 퍼블릭 프리뷰 제공):\u003C/b\u003E 샌드박스화된 Python 환경에서 데이터를 처리하고 완성도 높은 결과물을 생성할 수 있습니다.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EInterrupt and Resume(곧 GA 제공):\u003C/b\u003E 실행 중인 에이전트를 중단하고 방향을 수정한 뒤 중단 지점부터 다시 이어갈 수 있습니다.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EPartial Access(곧 퍼블릭 프리뷰 제공):\u003C/b\u003E 하나의 관리형 에이전트로 서로 다른 권한 수준의 사용자에게 서비스를 제공할 수 있습니다.\u003C/li\u003E\n\u003C/ul\u003E\n\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cb\u003E관리\u003C/b\u003E\u003Cul\u003E\n\u003Cli\u003E\u003Cb\u003EVersioning UI(퍼블릭 프리뷰 제공 중):\u003C/b\u003E YAML을 직접 수정하지 않고도 에이전트 구성을 비교하고, 이전 버전으로 롤백하거나, 새로운 버전을 운영 환경으로 승격할 수 있습니다.\u003C/li\u003E\n\u003C/ul\u003E\n\u003C/li\u003E\n\u003C/ul\u003E\n\u003Ch2\u003ECortex Agents 핵심 정리\u003C/h2\u003E\n\u003Cp\u003ECortex Agent는 Snowflake의 거버넌스 경계 안에서 엔터프라이즈 데이터를 추론하고, 도구를 호출하고, 코드를 실행하며, 연결된 시스템 전반에서 작업을 수행할 수 있는 AI 에이전트입니다. 또한 모든 작업에는 Snowflake 데이터에 적용되는 것과 동일한 액세스 제어 및 감사 추적이 적용됩니다.\u003C/p\u003E\n\u003Cp\u003ECortex Agent는 오케스트레이션 모델, 도구, 지침을 포함하는 선언적 방식으로 정의합니다. 애플리케이션은 REST API를 통해 이를 호출하고, Snowflake는 모델 라우팅, 도구 호출, 결과 조립을 처리합니다.\u003C/p\u003E\n\u003Ch2\u003E실제 운영 환경의 과제\u003C/h2\u003E\n\u003Cp\u003E대부분의 AI 에이전트 배포는 하나의 에이전트와 몇 가지 도구, 그리고 짧은 작업으로 시작합니다. 이러한 구성은 개념 검증(PoC)이나 소규모 배포에는 충분히 효과적일 수 있습니다.\u003C/p\u003E\n\u003Cp\u003E하지만 엔터프라이즈 규모의 실제 운영 환경은 다릅니다. 워크플로우는 여러 전문 에이전트로 확장되고, 도구 라이브러리는 수십 개의 통합으로 늘어나며, 요청은 밀리초가 아니라 수분 단위로 실행됩니다. 또한 거버넌스 팀은 각 에이전트가 정확히 어떤 작업을 수행하도록 승인되어 있는지에 대한 가시성을 필요로 합니다. 이는 더 나은 프롬프트나 더 빠른 모델만으로 해결할 수 있는 문제가 아닙니다. 본질적으로 운영의 문제이며, 아래에서 소개하는 새로운 기능들은 바로 이러한 과제를 해결하도록 설계되었습니다.\u003C/p\u003E\n\u003Ch2\u003E에이전트 구축\u003C/h2\u003E\n\u003Ch4\u003ESnowflake CoCo의 코딩 역량을 모든 애플리케이션으로 확장\u003C/h4\u003E\n\u003Cp\u003ESnowflake CoCo는 진정한 데이터 네이티브 AI 코딩 에이전트로, 최신 데이터 스택 전반에서 가치 실현 시간을 단축하도록 설계되었습니다. CoCo는 기업의 데이터, 컴퓨팅, 거버넌스, 운영 시맨틱을 깊이 이해하고, 복잡한 데이터 엔지니어링, 분석, 머신러닝, 에이전트 구축 작업을 정확성과 신뢰를 갖춘 간결하고 맥락 있는 대화로 전환합니다.\u003C/p\u003E\n\u003Cp\u003E이제 Cortex Agents REST API를 사용하면 동일한 \u003Cb\u003E코딩 기능(곧 퍼블릭 프리뷰 제공)\u003C/b\u003E을 자체 애플리케이션과 인터페이스에 적용하는 관리형 에이전트를 구축할 수 있습니다. CoCo 도구 세트(샌드박스 포함)로 에이전트를 구성하고, 지침과 데이터 그라운딩의 범위를 조직의 환경에 맞게 지정한 뒤, 표준 REST 엔드포인트로 노출할 수 있습니다. 코드 생성, SQL 실행, 데이터 변환 또는 파이프라인 자동화가 필요한 모든 쿼리는 샌드박스화된 런타임 안에서 처리됩니다. CoCo를 구동하는 바로 그 기반을 이제 자체 에이전틱 워크플로우의 구성 요소로 활용할 수 있습니다.\u003C/p\u003E\n\u003Cp\u003E예를 들어 Snowflake 엔지니어링 팀은 모든 Cortex Agent 요청의 근본 원인을 분석하는 내부 도구 \"Debug with CoCo\"를 20분 이내에 구축했습니다. 이 앱은 수백 줄 정도의 간단한 코드로 구성되어 있습니다. UI를 통해 request_id를 받고, 전용 프롬프트로 Cortex Agent API를 한 번 호출하는 것이 전부입니다. 그러면 관리형 API가 CoCo 샌드박스를 프로비저닝하고, 로그를 에이전트의 그라운딩으로 마운트하며, CoCo 도구 세트를 통해 bash와 SQL을 실행합니다. 이후 근거가 포함된 근본 원인 분석 결과를 브라우저로 스트리밍합니다. 즉, 별도의 코딩 에이전트 런타임도, Snowflake 밖으로 나가는 데이터도 없습니다. 필요한 것은 REST 호출뿐입니다.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image":{"id":"image-c80e2d54c2","height":"1068","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--d170173b-251f-4daf-ab35-1d50bb97c1ee/agent-request-debugger.png?preferwebp=true&quality=85","alt":"agent request debugger","lazyEnabled":true,"isLcpImage":true,"width":"1820",":type":"snowflake-site/components/image"},"blog_text_1859485776":{"id":"blog-text-586e2da448","text":"\u003Cp\u003E\u003Csub\u003E\u003Ci\u003E자료 1:  CoCo 런타임을 기반으로 관리형 에이전트 API로 구축한 엔지니어링 도구 Debug with CoCo\u003C/i\u003E\u003C/sub\u003E\u003C/p\u003E\n\u003Ch4\u003E에이전트에 공통 역량 계층 제공\u003C/h4\u003E\n\u003Cp\u003E에이전트 배포가 확대될수록 스킬 관리는 새로운 병목이 됩니다. 새로운 에이전트를 만들 때마다 필요한 기능 목록을 각각 정의해야 하고, 기능이 변경되면 모든 에이전트를 개별적으로 수정해야 합니다. 또한 스킬 라이브러리가 커질수록 에이전트 구성은 유지 관리가 어려워지고 동기화도 쉽게 어긋납니다.\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003ESkills Package(곧 퍼블릭 프리뷰 제공)\u003C/b\u003E를 사용하면 선별된 스킬 집합을 한 번 정의하고, 단일 URI로 전체 컬렉션을 어떤 에이전트 사양에서든 참조할 수 있습니다. 패키지가 업데이트되면 이를 참조하는 모든 에이전트에 변경 사항이 자동으로 반영됩니다. 이를 통해 팀은 표준 데이터 워크플로우, 도메인별 도구, 승인된 통합으로 구성된 공유 역량 계층을 구축하고, 별도의 사본을 중복 생성하거나 유지 관리하지 않고도 그 위에 새 에이전트를 구성할 수 있습니다.\u003C/p\u003E\n\u003Ch4\u003E다시 작성하지 않고 에이전트 간 도구 재사용\u003C/h4\u003E\n\u003Cp\u003E엔터프라이즈 에이전트 배포는 대개 잘 정의된 하나의 에이전트에서 시작합니다. 엄선된 도구, 검증된 통합, 최적화된 구성을 갖춘 에이전트입니다. 하지만 두 번째 에이전트가 동일한 도구를 사용하려면 지금까지는 도구 정의를 그대로 복사해야 했습니다. 즉, 에이전트 구성을 복제하고, 두 개의 사본을 각각 관리하며, 변경 사항이 생길 때마다 모두 수정해야 했습니다\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003EAgent Toolset(곧 퍼블릭 프리뷰 제공)\u003C/b\u003E을 사용하면 다른 에이전트의 도구를 현재 에이전트 사양에서 직접 참조할 수 있습니다. 소스 에이전트를 한 줄만 지정하면 해당 에이전트의 모든 도구를 즉시 사용할 수 있으며, 별도로 다시 정의할 필요가 없습니다. 또한 소스 에이전트의 도구가 업데이트되면 해당 도구 세트를 참조하는 모든 에이전트가 변경 사항을 자동으로 상속합니다. 이를 통해 조직은 공통 데이터 액세스 계층, 표준 검색 통합 집합, 공통 MCP 구성과 같은 표준 도구 세트를 한 번만 정의하고, 중복된 정의나 버전 불일치 없이 그 위에 새 에이전트를 구성할 수 있습니다.\u003C/p\u003E\n\u003Ch4\u003E에이전트를 위한 컨텍스트 구성 최적화\u003C/h4\u003E\n\u003Cp\u003EGitHub, Slack, Jira, Salesforce 및 내부 MCP 서버에 연결된 대규모 엔터프라이즈 배포에서는 수십 개의 도구를 사용하는 것이 일반적입니다. 에이전트는 단 하나의 질문에 답하기 전에도 도구 정의를 컨텍스트에 로드하는 데 수만 개의 토큰을 사용할 수 있습니다.\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003ETool Search(곧 퍼블릭 프리뷰 제공)\u003C/b\u003E는 점진적 공개 방식을 통해 이러한 문제를 해결합니다. 처음부터 모든 도구 정의를 로드하는 대신, 에이전트는 추론 시점에 도구 라이브러리를 검색하고 현재 작업과 관련된 정의만 로드합니다. 대규모 환경에서는 이는 단순한 성능 최적화를 넘어섭니다. 컨텍스트 윈도우의 부담을 줄여 정확도를 높일 수 있으며, 한 번에 모두 로드하기에는 현실적으로 어려운 대규모 도구 생태계에서도 에이전트를 효과적으로 운영할 수 있게 합니다.\u003C/p\u003E\n\u003Ch2\u003E에이전트 실행\u003C/h2\u003E\n\u003Ch4\u003E장시간 실행되는 작업도 애플리케이션을 멈추지 않고 처리\u003C/h4\u003E\n\u003Cp\u003E일부 작업은 밀리초가 아니라 몇 분이 걸립니다. 월별 재무 데이터를 조정하고, 종합 리서치 보고서를 생성하며, 대량의 고객 계약서 배치를 처리하는 작업은 HTTP 연결을 계속 열어 둔 채 기다릴 수 있는 일이 아닙니다.\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003EAsync Agent API(곧 GA 제공)\u003C/b\u003E를 사용하면 에이전트가 이러한 작업을 백그라운드에서 실행할 수 있습니다. 호출자는 즉시 run_id를 반환받고, 작업이 완료되면 응답을 가져옵니다.\u003C/p\u003E\n\u003Cp\u003E에이전트는 완료될 때까지 실행되며, 사용자 입력이 필요한 경우에는 일시 중지됩니다. 결과는 준비되는 즉시 사용할 수 있습니다. 이제 장시간 실행되는 에이전트 워크플로우를 관리형 런타임 안에서 완전히 실행할 수 있으므로, 사용자 지정 큐잉이나 오케스트레이션 인프라를 별도로 구축할 필요가 없습니다.\u003C/p\u003E\n\u003Ch4\u003E가공 전 데이터에서 완성도 높은 결과물까지, 한 번의 에이전트 실행으로 안전하게\u003C/h4\u003E\n\u003Cp\u003E\u003Cb\u003ECode Execution Tool(곧 퍼블릭 프리뷰 제공)\u003C/b\u003E은 Cortex Agents에 안전한 샌드박스 기반 Python 실행 환경을 제공합니다. 이를 통해 Snowflake를 벗어나지 않고도 데이터를 처리하고, 계산을 수행하며, 시각화를 생성하고, PDF, PowerPoint, 차트와 같은 완성도 높은 결과물을 만들 수 있습니다.\u003C/p\u003E\n\u003Cp\u003E샌드박스는 Python 3.12를 실행하며 기본적으로 numpy와 pandas를 포함합니다. 추가 패키지는 Artifact Repository를 통해 PyPI에서 가져올 수 있습니다. 외부 네트워크 액세스는 명명된 통합을 통해 범위 지정할 수 있습니다. \u003Ci\u003E모든 실행은 격리된 환경에서 이루어집니다. 명시적 권한 없이는 에이전트가 생성한 코드가 데이터에 액세스할 수 없습니다.\u003C/i\u003E 이를 통해 에이전트는 데이터 세트 분석부터 형식이 적용된 PDF 보고서 생성까지 전체 워크플로우를 단일 실행 안에서 완료할 수 있습니다.\u003C/p\u003E\n\u003Ch4\u003E워크플로우를 일시 중단하고 수정한 뒤 계속 이어서 실행\u003C/h4\u003E\n\u003Cp\u003E예를 들어 에이전트에게 2년치 재무 데이터를 분석하도록 요청했는데, 중간에 5년치 데이터가 필요하다는 사실을 알게 될 수도 있습니다. 또는 에이전트가 도움이 되지 않는 방향으로 진행되고 있음을 알아차리고, 작업이 완료되기 전에 경로를 바로잡고 싶을 수도 있습니다.\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003EInterrupt and Resume(곧 GA 제공)\u003C/b\u003E은 이러한 상황을 위해 사람이 AI 에이전트를 직접 제어할 수 있는 API를 제공합니다. 에이전트를 일시 중단하고 수정 지시를 보내면, 에이전트는 중단된 지점부터 다시 이어갑니다.\u003C/p\u003E\n\u003Cp\u003E에이전트는 현재까지의 작업 상태를 저장한 뒤 다음 지시를 기다리므로, 개발자가 중단된 워크플로를 위해 별도의 상태 관리나 복구 로직을 구현할 필요가 없습니다.\u003C/p\u003E\n\u003Ch4\u003E하나의 관리형 에이전트로 다양한 권한 수준 지원\u003C/h4\u003E\n\u003Cp\u003E대부분의 조직에서는 사용자마다 권한이 다릅니다. 예를 들어 영업팀은 고객 상태 지표에 액세스할 수 있고, 재무팀은 여기에 더해 청구 정보까지 확인할 수 있습니다. 이러한 차이를 지원하려면 같은 에이전트의 여러 버전을 구축하고 유지해야 하는 경우가 많습니다.\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003EPartial Access(곧 퍼블릭 프리뷰 제공)\u003C/b\u003E를 사용하면 하나의 관리형 에이전트가 서로 다른 권한 수준의 요청을 처리할 수 있습니다. 요청이 들어오면 에이전트는 사용자가 액세스 권한이 있는 도구만 사용할 수 있도록 판단하고, 해당 범위 안에서 요청을 처리합니다. 이러한 접근 제어는 Snowflake 계층에서 적용되며, 나머지 데이터를 관리하는 기존 RBAC와도 일관되게 작동합니다. 실제로는 단일 에이전트가 동일한 지식 기반을 바탕으로 직원과 관리자, 또는 분석가와 임원을 모두 지원하면서도 각 그룹별로 서로 다른 액세스 경계를 적용할 수 있습니다.\u003C/p\u003E\n\u003Ch2\u003E에이전트 관리와 업데이트\u003C/h2\u003E\n\u003Ch4\u003E모든 에이전트 구성을 한눈에 확인\u003C/h4\u003E\n\u003Cp\u003E에이전트 구성은 시간이 지나면서 계속 진화합니다. 프롬프트는 개선되고, 새로운 도구가 추가되며, 비즈니스 로직도 달라집니다. 이러한 변경 사항을 체계적으로 관리하지 않으면 프로덕션에서 실행 중인 항목과 테스트 중인 항목 사이에 문서화되지 않은 구성 드리프트가 누적됩니다.\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003EVersioning UI(버전 관리 UI, 퍼블릭 프리뷰)\u003C/b\u003E는 이러한 문제를 해결하기 위해 에이전트 구성의 시각적 변경 이력을 제공합니다. 팀은 각 버전을 나란히 비교하고, 이전 상태로 롤백하거나, YAML을 직접 수정하지 않고도 검증된 버전을 운영 환경에 배포할 수 있습니다. 또한 운영 환경에서는 항상 라이브 버전이 유지되고, 팀은 릴리스 전에 후보 버전을 안전하게 반복 개선할 수 있습니다. 특히 규제가 엄격한 산업에서는 이를 통해 모든 에이전트 구성에 대해 무엇이, 언제, 누구의 승인으로 변경됐는지 모두 확인할 수 있습니다.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image_1660099645":{"id":"image-4ae7eab9c9","height":"692","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--dd90049f-49cf-49d1-8212-dc6321bf64df/cortex-agent-versioning.png?preferwebp=true&quality=85","alt":"cortex","lazyEnabled":true,"isLcpImage":false,"width":"1892",":type":"snowflake-site/components/image"},"blog_text_1528741199":{"id":"blog-text-5906c685eb","text":"\u003Ch2\u003E엔터프라이즈 확장을 위한 설계\u003C/h2\u003E\n\u003Cp\u003E오늘 발표한 기능은 Cortex Agents가 이미 제공하고 있는 관리형 플랫폼을 한층 확장합니다. 오케스트레이션, 실행, 운영 제어 기능은 팀이 첫 프로덕션 배포에서 조직 전반의 도입으로 안정적으로 확장할 수 있도록 지원합니다.\u003C/p\u003E\n\u003Cp\u003ESnowflake가 관리형 에이전트를 제공하면서 가장 중점을 둔 것도 바로 이 지점입니다. 운영 복잡성을 낮추는 플랫폼을 제공해 빌더가 인프라 유지 관리에 쓰는 시간을 줄이고 AI 애플리케이션 구축에 더 집중할 수 있도록 하는 것입니다. 엔터프라이즈 AI 도입이 성숙해질수록 핵심 과제는 에이전트를 구축하는 것에서 운영하는 것으로 이동합니다. Cortex Agents는 바로 이러한 다음 단계를 지원하도록 설계되었습니다.\u003C/p\u003E\n\u003Ch2\u003E시작하기\u003C/h2\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents/code-execution\"\u003E샌드박스 구성 및 패키지 설정을 활용해\u003C/a\u003E Code Execution Tool을 사용해 보세요.\u003C/li\u003E\n\u003Cli\u003E\u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents/multi-agent\"\u003E실행 예제가 포함된 선언형 오케스트레이션 가이드를 활용해\u003C/a\u003E 첫 번째 멀티 에이전트 워크플로우를 구축해 보세요.\u003C/li\u003E\n\u003Cli\u003E\u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://www.snowflake.com/summit/\"\u003ESnowflake Summit 2026 데모\u003C/a\u003E를 통해 이러한 기능이 실제로 실행되는 모습을 확인해 보세요\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Cp\u003E\u003Ci\u003E\u003Csub\u003E미래 전망 진술: 본 블로그 게시물에는 현재 개발 중이거나 아직 정식 출시되지 않았거나 제공되지 않는 제품, 기능 및 역량에 관한 진술을 포함한 미래 전망 진술이 포함되어 있습니다. 이러한 미래 전망 진술은 실제 결과가 크게 달라질 수 있는 다양한 위험과 불확실성에 영향을 받을 수 있습니다. 실제 결과가 달라질 수 있는 위험 요인에 대한 자세한 내용은 Snowflake의 SEC 공시 문서를 참조하시기 바랍니다.\u003C/sub\u003E\u003C/i\u003E\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"}},":itemsOrder":["blog_text","image","blog_text_1859485776","image_1660099645","blog_text_1528741199"],":type":"wcm/foundation/components/responsivegrid"},"responsivegrid_premium_content_banner":{"columnCount":12,"columnClassNames":{},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","appliedCssClassNames":"snowflake-responsive-component-top-padding-medium",":items":{},":itemsOrder":[],":type":"wcm/foundation/components/responsivegrid"},"container_author_chip":{"layout":"RESPONSIVE_GRID","columnCount":12,"columnClassNames":{"author_chip":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 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Snowflake"},{"authorImage":{"id":"image-039080c29a","height":"400","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--bab3c432-9d00-4fbb-9785-e632873b6c56/harshal.jpg?preferwebp=true&quality=85","alt":"Harshal Pimpalkhute","lazyEnabled":true,"isLcpImage":false,"width":"400",":type":"snowflake-site/components/image"},"authorCta":{"id":"button-14b5a083b7","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/blog/authors/harshal-pimpalkhute/"},"linkTargetContentType":"DOCUMENT_LEARN",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Harshal Pimpalkhute"},"authorTitle":"Principal Product Manager"},{"authorImage":{"id":"image-972b71acf1","height":"2549","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--3ab8a4fd-dd09-4fd7-b945-2d43de0f11d8/william-allen.jpg?preferwebp=true&quality=85","lazyEnabled":true,"isLcpImage":false,"width":"2549",":type":"snowflake-site/components/image"},"authorCta":{"id":"button-d38642e40d","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/blog/authors/william-allen/"},"linkTargetContentType":"DOCUMENT_LEARN",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"William Allen"},"authorTitle":"Head of Product, AI - Cortex Agents and Snowflake 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