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an intelligent sales assistant using Snowflake Cortex Agents that combines Cortex Search and Analyst for both structured and unstructured data.","paragraphs":["\u003Ch2\u003EOverview\u003C/h2\u003E\n","\u003Cp\u003EModern organizations face the challenge of managing two types of data:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EStructured Data: Fixed schema that fits neatly into rows and columns (like metrics and KPIs)\u003C/li\u003E\u003Cli\u003EUnstructured Data: No fixed schema that can have a more complex format (such as customer conversations, emails, and meeting transcripts)\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EThe ability to analyze and derive insights from both types of data is crucial for understanding customer needs, improving processes, and driving business growth.\u003C/p\u003E\n","\u003Cp\u003EIn this quickstart, you'll learn how to build an Intelligent Sales Assistant that leverages Snowflake's capabilities for analyzing both sales conversations and metrics. Using \u003Cstrong\u003ECortex Code CLI\u003C/strong\u003E — Snowflake's AI-powered command-line assistant — we'll create, configure, and test the agent entirely from the terminal.\u003C/p\u003E\n","\u003Ch3\u003EWhat is Snowflake Cortex AI?\u003C/h3\u003E\n","\u003Cp\u003ESnowflake Cortex AI allows you to turn your conversations, documents and images into intelligent insights with AI next to your data. You can access industry-leading LLMs at scale directly in SQL or via APIs, analyze multimodal data and build agents — all within Snowflake's secure perimeter. The platform leverages powerful capabilities:\u003C/p\u003E\n","\u003Ch4\u003ECortex Analyst\u003C/h4\u003E\n","\u003Cp\u003EUse AI to accurately convert natural language to SQL; Simple to use, cost-efficient to scale.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EConverts natural language questions into SQL queries\u003C/li\u003E\u003Cli\u003EUnderstands semantic models defined in YAML files\u003C/li\u003E\u003Cli\u003EEnables querying data without writing SQL manually\u003C/li\u003E\u003Cli\u003EHandles complex analytical questions about sales metrics\u003C/li\u003E\u003Cli\u003EAchieves over 90% accuracy through user-generated semantic models that capture domain knowledge and business context\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch4\u003ECortex Search\u003C/h4\u003E\n","\u003Cp\u003EFind information by asking questions within a given set of documents with fully managed text embedding, hybrid search (semantic + keyword) and retrieval.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EDelivers best-in-class search performance through a hybrid approach combining semantic and keyword search\u003C/li\u003E\u003Cli\u003ELeverages an advanced embedding model (E5) to understand complex semantic relationships\u003C/li\u003E\u003Cli\u003EEnables searching across unstructured data with exceptional accuracy and speed\u003C/li\u003E\u003Cli\u003ESupports real-time indexing and querying of large-scale text data\u003C/li\u003E\u003Cli\u003EReturns contextually relevant results ranked by relevance scores\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch4\u003ECortex Agents\u003C/h4\u003E\n","\u003Cp\u003EOrchestrate across both structured and unstructured data to retrieve and synthesize high-quality data insights. The Cortex Agents is a stateless REST API endpoint that:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ESeamlessly combines Cortex Search's hybrid search capabilities with Cortex Analyst's 90%+ accurate SQL generation\u003C/li\u003E\u003Cli\u003EStreamlines complex workflows by handling:\n\u003Cul\u003E\u003Cli\u003EContext retrieval through semantic and keyword search\u003C/li\u003E\u003Cli\u003ENatural language to SQL conversion via semantic models\u003C/li\u003E\u003Cli\u003ELLM orchestration and prompt management\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003EEnhances response quality through:\n\u003Cul\u003E\u003Cli\u003EIn-line citations to source documents\u003C/li\u003E\u003Cli\u003EBuilt-in answer abstaining for irrelevant questions\u003C/li\u003E\u003Cli\u003EMulti-message conversation context management\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003EOptimizes application development with:\n\u003Cul\u003E\u003Cli\u003ESingle API call integration\u003C/li\u003E\u003Cli\u003EStreamed responses for real-time interactions\u003C/li\u003E\u003Cli\u003EReduced latency through efficient orchestration\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch4\u003ECortex Code CLI\u003C/h4\u003E\n","\u003Cp\u003ECortex Code is Snowflake's AI-powered command-line interface for building, testing, and managing Snowflake resources conversationally. It includes built-in \u003Cstrong\u003Eskills\u003C/strong\u003E — structured workflows that guide you through complex tasks like agent creation, evaluation, and optimization — all from your terminal.\u003C/p\u003E\n","\u003Cp\u003EThese capabilities work together to:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003ESearch through sales conversations for relevant context\u003C/li\u003E\u003Cli\u003EGo from Text to SQL to answer analytical questions\u003C/li\u003E\u003Cli\u003ECombine structured and unstructured data analysis\u003C/li\u003E\u003Cli\u003EProvide natural language interactions with your data\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003EWhat You'll Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to use Cortex Code CLI to set up a sales intelligence database\u003C/li\u003E\u003Cli\u003EHow to create and configure Cortex Search services via SQL\u003C/li\u003E\u003Cli\u003EHow to create a Cortex Agent using the Cortex Code \u003Ccode\u003Ecortex-agent\u003C/code\u003E skill\u003C/li\u003E\u003Cli\u003EHow to test your agent directly from Cortex Code\u003C/li\u003E\u003Cli\u003EHow to build a Streamlit interface for external access via the REST API\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Build\u003C/h3\u003E\n","\u003Cp\u003EA full-stack application that enables users to:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ESearch through sales conversations using semantic similarity\u003C/li\u003E\u003Cli\u003EAnalyze sales metrics and patterns\u003C/li\u003E\u003Cli\u003EAsk questions about sales data and get AI-powered responses\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/cortex-code/cortex-code-cli\"\u003ECortex Code CLI\u003C/a\u003E installed\u003C/li\u003E\u003Cli\u003EA \u003Ca href=\"https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides\"\u003ESnowflake account\u003C/a\u003E with a role with sufficient privileges to create databases, schemas, tables, and agents\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ECortex Agents Access\u003C/strong\u003E: You will need access to Snowflake Cortex AI, including \u003Ca href=\"https://docs.snowflake.com/user-guide/snowflake-cortex/cortex-agents\"\u003E\u003Cstrong\u003ECortex Agents\u003C/strong\u003E\u003C/a\u003E, \u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-search/cortex-search-overview\"\u003E\u003Cstrong\u003ECortex Search\u003C/strong\u003E\u003C/a\u003E, and \u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-analyst\"\u003E\u003Cstrong\u003ECortex Analyst\u003C/strong\u003E\u003C/a\u003E features.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch2\u003EInstall Cortex Code CLI\u003C/h2\u003E\n","\u003Cp\u003EIf you don't already have Cortex Code CLI installed, run the following in your terminal:\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EmacOS and Linux (including WSL):\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Ecurl -LsS https://ai.snowflake.com/static/cc-scripts/install.sh | sh\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWindows (PowerShell):\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-powershell\"\u003Eirm https://ai.snowflake.com/static/cc-scripts/install.ps1 | iex\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EAfter installation, verify it works and connect to your Snowflake account:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Ecortex\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EOn first launch, Cortex Code will guide you through connecting to your Snowflake account. Ensure your connection uses a role with privileges to create databases, schemas, tables, stages, Cortex Search services, and agents.\u003C/p\u003E\n","\u003Ch2\u003ESetup Data\u003C/h2\u003E\n","\u003Cp\u003EWe'll use Cortex Code to execute the SQL that creates our sales intelligence database. Clone the companion repository first:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Egit clone https://github.com/Snowflake-Labs/sfguide-getting-started-with-cortex-agents.git\ncd sfguide-getting-started-with-cortex-agents\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENow launch Cortex Code from the repo directory:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Ecortex\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EStep 1.\u003C/strong\u003E Ask Cortex Code to run the setup SQL. You can paste this prompt:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ERun the SQL in setup.sql to create the sales intelligence database, tables, \nsample data, and Cortex Search service.\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ECortex Code will execute each statement from \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-getting-started-with-cortex-agents/blob/main/setup.sql\"\u003Esetup.sql\u003C/a\u003E, which:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ECreates the \u003Ccode\u003ESALES_INTELLIGENCE\u003C/code\u003E database, \u003Ccode\u003EDATA\u003C/code\u003E schema, and \u003Ccode\u003ESALES_INTELLIGENCE_WH\u003C/code\u003E warehouse\u003C/li\u003E\u003Cli\u003ECreates tables for sales conversations and metrics\u003C/li\u003E\u003Cli\u003ELoads sample sales data\u003C/li\u003E\u003Cli\u003EEnables change tracking for real-time updates\u003C/li\u003E\u003Cli\u003EConfigures the \u003Ccode\u003ESALES_CONVERSATION_SEARCH\u003C/code\u003E Cortex Search service\u003C/li\u003E\u003Cli\u003ECreates a \u003Ccode\u003EMODELS\u003C/code\u003E stage for semantic models\u003C/li\u003E\u003Cli\u003EGrants the necessary permissions\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EStep 2.\u003C/strong\u003E Upload the semantic model. The Agent needs rules to interpret your structured data. This semantic model defines key business terms to ensure the Agent generates highly accurate SQL. Ask Cortex Code:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EUpload sales_metrics_model.yaml to the @SALES_INTELLIGENCE.DATA.MODELS stage.\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ECortex Code will run:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EPUT file://sales_metrics_model.yaml @SALES_INTELLIGENCE.DATA.MODELS AUTO_COMPRESS=FALSE OVERWRITE=TRUE;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003ECreate Agent\u003C/h2\u003E\n","\u003Cp\u003ENow we'll use Cortex Code's built-in \u003Cstrong\u003Ecortex-agent\u003C/strong\u003E skill to create the Sales Intelligence Agent. This skill provides a guided, interactive workflow for agent creation.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EStep 1.\u003C/strong\u003E In Cortex Code, ask it to create the agent:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ECreate an agent called SALES_INTELLIGENCE_AGENT in SALES_INTELLIGENCE.DATA\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ECortex Code will load the \u003Ccode\u003Ecortex-agent\u003C/code\u003E skill and walk you through the creation workflow interactively.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EStep 2.\u003C/strong\u003E When prompted for \u003Cstrong\u003Eadministrative setup\u003C/strong\u003E, provide:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EDatabase\u003C/strong\u003E: \u003Ccode\u003ESALES_INTELLIGENCE\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESchema\u003C/strong\u003E: \u003Ccode\u003EDATA\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAgent Name\u003C/strong\u003E: \u003Ccode\u003ESALES_INTELLIGENCE_AGENT\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ERole\u003C/strong\u003E: \u003Ccode\u003ESALES_INTELLIGENCE_ROLE\u003C/code\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EStep 3.\u003C/strong\u003E When prompted for the agent's \u003Cstrong\u003Epurpose and requirements\u003C/strong\u003E, explain:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EThis agent orchestrates between sales data for analyzing sales conversations \nusing Cortex Search and metrics using Cortex Analyst. It should answer questions \nabout both structured sales metrics (revenue, deals, KPIs) and unstructured \nsales conversations (call transcripts, meeting notes).\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EStep 4.\u003C/strong\u003E When prompted for \u003Cstrong\u003Etools\u003C/strong\u003E, select the existing tools in the \u003Ccode\u003ESALES_INTELLIGENCE.DATA\u003C/code\u003E schema:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ECortex Search Service\u003C/strong\u003E: \u003Ccode\u003ESALES_CONVERSATION_SEARCH\u003C/code\u003E — for searching through sales conversation transcripts\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ECortex Analyst (Semantic Model)\u003C/strong\u003E: \u003Ccode\u003Esales_metrics_model.yaml\u003C/code\u003E on the \u003Ccode\u003EMODELS\u003C/code\u003E stage — for querying structured sales metrics\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EStep 5.\u003C/strong\u003E When prompted for \u003Cstrong\u003Eresponse instructions\u003C/strong\u003E, provide:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EYou are a specialized analytics assistant focused on providing concise responses \nabout sales metrics and sales conversations. Your primary objectives are:\n\nFor structured data queries (metrics, KPIs, sales figures):\n- Use the Cortex Analyst which is YAML-based functionality\n- Provide direct, numerical answers with minimal explanation\n- Format responses clearly with relevant units and time periods\n- Only include essential context needed to understand the metric\n\nFor unstructured content from sales conversations:\n- Utilize the Cortex Search service SALES_INTELLIGENCE.DATA.SALES_CONVERSATION_SEARCH\n- Extract relevant information from conversations\n- Summarize findings in brief, focused responses\n- Maintain context from the original sales conversations\n\nOperating guidelines:\n- Always identify whether you're using Cortex Analyst or Cortex Search for each response\n- Keep responses under 3-4 sentences when possible\n- Present numerical data in a structured format\n- Don't speculate beyond available data\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EStep 6.\u003C/strong\u003E When prompted for \u003Cstrong\u003Eorchestration instructions\u003C/strong\u003E, provide:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EIf a query spans both structured and unstructured data, clearly separate the sources. \nFor any query, first determine whether it requires:\n(a) Structured data analysis → Use YAML/Cortex Analyst\n(b) Report content/context → Use Cortex Search\n(c) Both → Combine both services with clear source attribution\n\nPlease confirm which approach you'll use before providing each response.\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EStep 7.\u003C/strong\u003E Cortex Code will create the agent via the REST API and verify its configuration. You can confirm the agent was created by asking:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EList agents in SALES_INTELLIGENCE.DATA\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EStep 8.\u003C/strong\u003E Grant access to the role. Ask Cortex Code:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EGrant usage on agent SALES_INTELLIGENCE.DATA.SALES_INTELLIGENCE_AGENT to role SALES_INTELLIGENCE_ROLE\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003ETest the Agent\u003C/h2\u003E\n","\u003Cp\u003ENow that your Agent is fully configured, let's test it directly from Cortex Code. Ask Cortex Code to chat with the agent:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EChat with agent SALES_INTELLIGENCE.DATA.SALES_INTELLIGENCE_AGENT\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ECortex Code will load the chat-with-agent capability. Try these questions to test both structured and unstructured data:\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETest 1 — Structured data (Cortex Analyst):\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EHow many deals did Sarah Johnson win compared to deals she lost?\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis question requires the Agent to run a SQL query against the sales metrics table using Cortex Analyst.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETest 2 — Unstructured data (Cortex Search):\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ETell me about the call with Securebank?\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis question requires the Agent to look up contextual information within the long text transcripts in the sales conversations table using Cortex Search.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETest 3 — Combined:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EWhat were Sarah Johnson's total deal values, and what feedback came up in her conversations?\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis tests the agent's ability to orchestrate across both tools.\u003C/p\u003E\n","\u003Ch2\u003EAgent REST API\u003C/h2\u003E\n","\u003Cp\u003EIn addition to testing in Cortex Code, you can integrate your Agent into custom applications. You can interact with the Agent by calling the Snowflake REST API at \u003Ccode\u003E/api/v2/databases/{DATABASE}/schemas/{SCHEMA}/agents/{AGENT}:run\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003EWe've created a simple Streamlit application that interacts with the REST API \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-getting-started-with-cortex-agents/blob/main/data_agent_demo.py\"\u003Ehere\u003C/a\u003E. You can use it as a building block to build your own applications.\u003C/p\u003E\n","\u003Cp\u003ESince this API call is made outside of Snowsight, we need a secure key to authenticate your identity and grant the application permission to run the Agent. This key is a Programmatic Access Token (PAT).\u003C/p\u003E\n","\u003Cp\u003ELet's run the Streamlit app and call the API locally:\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EStep 1.\u003C/strong\u003E Create a \u003Ca href=\"https://docs.snowflake.com/en/user-guide/programmatic-access-tokens\"\u003EProgrammatic Access Token\u003C/a\u003E. You can do this via Cortex Code:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ECreate a programmatic access token for the SALES_INTELLIGENCE_ROLE role\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EOr manually in Snowsight: Click on your profile (bottom left corner) → Settings → Authentication → Under \u003Ccode\u003EProgrammatic access tokens\u003C/code\u003E, click \u003Ccode\u003EGenerate new token\u003C/code\u003E → Select \u003Ccode\u003ESingle Role\u003C/code\u003E and select \u003Ccode\u003Esales_intelligence_role\u003C/code\u003E. Copy and save the token.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EStep 2.\u003C/strong\u003E Find your account URL. Ask Cortex Code:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EWhat is my account URL?\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EOr in Snowsight: Click on your profile (bottom left corner) → Account → View account details.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EStep 3.\u003C/strong\u003E Set up and run the Streamlit app. In the cloned repository directory, run:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Epython3 -m venv venv\nsource venv/bin/activate\npip3 install -r requirements.txt\n\nCORTEX_AGENT_DEMO_PAT=<PAT> \\\nCORTEX_AGENT_DEMO_HOST=<ACCOUNT_URL> \\\nCORTEX_AGENT_DEMO_DATABASE="SALES_INTELLIGENCE" \\\nCORTEX_AGENT_DEMO_SCHEMA="DATA" \\\nCORTEX_AGENT_DEMO_AGENT="SALES_INTELLIGENCE_AGENT" \\\nstreamlit run data_agent_demo.py\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EReplace \u003Ccode\u003E<PAT>\u003C/code\u003E with the token from Step 1 and \u003Ccode\u003E<ACCOUNT_URL>\u003C/code\u003E with your account URL from Step 2.\u003C/p\u003E\n","\u003Cp\u003EWhen you're done using the application, press \u003Ccode\u003ECtrl+C\u003C/code\u003E in the terminal to shut down the Streamlit server.\u003C/p\u003E\n","\u003Ch2\u003EConclusion And Resources\u003C/h2\u003E\n","\u003Cp\u003ECongratulations! You've successfully built an Intelligent Sales Assistant using Snowflake Cortex Agents — entirely from the command line with Cortex Code CLI. This application demonstrates the power of combining structured and unstructured data analysis through:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ENatural language interactions with your sales data\u003C/li\u003E\u003Cli\u003ESemantic search across sales conversations\u003C/li\u003E\u003Cli\u003EAutomated SQL generation for analytics\u003C/li\u003E\u003Cli\u003EInteractive agent testing directly in the terminal\u003C/li\u003E\u003Cli\u003EExternal application access via the REST API with Streamlit\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You Learned\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ECortex Code CLI\u003C/strong\u003E: How to use Snowflake's AI-powered CLI to create and manage agents conversationally\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ECortex Agents\u003C/strong\u003E: How to create and configure agents using the \u003Ccode\u003Ecortex-agent\u003C/code\u003E skill for combining search and analysis capabilities\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ECortex Search\u003C/strong\u003E: How to leverage hybrid search combining semantic and keyword approaches for more accurate results\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ECortex Analyst\u003C/strong\u003E: How to convert natural language to SQL using semantic models for high-accuracy analytics\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ETesting\u003C/strong\u003E: How to chat with your agent directly from Cortex Code to validate behavior\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EIntegration\u003C/strong\u003E: How to connect external applications via the REST API using Streamlit\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ERelated Resources\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/cortex-code/cortex-code-cli\"\u003ECortex Code CLI Documentation\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/user-guide/snowflake-cortex/cortex-agents\"\u003ECortex Agents Guide\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-search/cortex-search-overview\"\u003ECortex Search Overview\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-analyst\"\u003ECortex Analyst Overview\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/llm-functions\"\u003ESnowflake Cortex Documentation\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/snowflake-intelligence\"\u003ESnowflake Intelligence Documentation\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E"],"title":"Getting Started with Cortex Agents","elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"## Overview\n\nModern organizations face the challenge of managing two types of data: \n- Structured Data: Fixed schema that fits neatly into rows and columns (like metrics and KPIs)\n- Unstructured Data: No fixed schema that can have a more complex format (such as customer conversations, emails, and meeting transcripts)\n\nThe ability to analyze and derive insights from both types of data is crucial for understanding customer needs, improving processes, and driving business growth. \n\nIn this quickstart, you'll learn how to build an Intelligent Sales Assistant that leverages Snowflake's capabilities for analyzing both sales conversations and metrics. Using **Cortex Code CLI** — Snowflake's AI-powered command-line assistant — we'll create, configure, and test the agent entirely from the terminal.\n\n### What is Snowflake Cortex AI?\nSnowflake Cortex AI allows you to turn your conversations, documents and images into intelligent insights with AI next to your data. You can access industry-leading LLMs at scale directly in SQL or via APIs, analyze multimodal data and build agents — all within Snowflake's secure perimeter. The platform leverages powerful capabilities:\n\n#### Cortex Analyst\nUse AI to accurately convert natural language to SQL; Simple to use, cost-efficient to scale.\n- Converts natural language questions into SQL queries\n- Understands semantic models defined in YAML files\n- Enables querying data without writing SQL manually\n- Handles complex analytical questions about sales metrics\n- Achieves over 90% accuracy through user-generated semantic models that capture domain knowledge and business context\n\n#### Cortex Search\nFind information by asking questions within a given set of documents with fully managed text embedding, hybrid search (semantic + keyword) and retrieval.\n- Delivers best-in-class search performance through a hybrid approach combining semantic and keyword search\n- Leverages an advanced embedding model (E5) to understand complex semantic relationships\n- Enables searching across unstructured data with exceptional accuracy and speed\n- Supports real-time indexing and querying of large-scale text data\n- Returns contextually relevant results ranked by relevance scores\n\n#### Cortex Agents\nOrchestrate across both structured and unstructured data to retrieve and synthesize high-quality data insights. The Cortex Agents is a stateless REST API endpoint that:\n- Seamlessly combines Cortex Search's hybrid search capabilities with Cortex Analyst's 90%+ accurate SQL generation\n- Streamlines complex workflows by handling:\n  - Context retrieval through semantic and keyword search\n  - Natural language to SQL conversion via semantic models\n  - LLM orchestration and prompt management\n- Enhances response quality through:\n  - In-line citations to source documents\n  - Built-in answer abstaining for irrelevant questions\n  - Multi-message conversation context management\n- Optimizes application development with:\n  - Single API call integration\n  - Streamed responses for real-time interactions\n  - Reduced latency through efficient orchestration\n\n#### Cortex Code CLI\nCortex Code is Snowflake's AI-powered command-line interface for building, testing, and managing Snowflake resources conversationally. It includes built-in **skills** — structured workflows that guide you through complex tasks like agent creation, evaluation, and optimization — all from your terminal.\n\nThese capabilities work together to:\n1. Search through sales conversations for relevant context\n2. Go from Text to SQL to answer analytical questions\n3. Combine structured and unstructured data analysis\n4. Provide natural language interactions with your data\n\n### What You'll Learn\n- How to use Cortex Code CLI to set up a sales intelligence database\n- How to create and configure Cortex Search services via SQL\n- How to create a Cortex Agent using the Cortex Code `cortex-agent` skill\n- How to test your agent directly from Cortex Code\n- How to build a Streamlit interface for external access via the REST API\n\n### What You'll Build\nA full-stack application that enables users to:\n- Search through sales conversations using semantic similarity\n- Analyze sales metrics and patterns\n- Ask questions about sales data and get AI-powered responses\n\n### Prerequisites\n- [Cortex Code CLI](https://docs.snowflake.com/en/user-guide/cortex-code/cortex-code-cli) installed\n- A [Snowflake account](https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides) with a role with sufficient privileges to create databases, schemas, tables, and agents\n- **Cortex Agents Access**: You will need access to Snowflake Cortex AI, including [**Cortex Agents**](https://docs.snowflake.com/user-guide/snowflake-cortex/cortex-agents), [**Cortex Search**](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-search/cortex-search-overview), and [**Cortex Analyst**](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-analyst) features.\n\n## Install Cortex Code CLI\n\nIf you don't already have Cortex Code CLI installed, run the following in your terminal:\n\n**macOS and Linux (including WSL):**\n```bash\ncurl -LsS https://ai.snowflake.com/static/cc-scripts/install.sh | sh\n```\n\n**Windows (PowerShell):**\n```powershell\nirm https://ai.snowflake.com/static/cc-scripts/install.ps1 | iex\n```\n\nAfter installation, verify it works and connect to your Snowflake account:\n```bash\ncortex\n```\n\nOn first launch, Cortex Code will guide you through connecting to your Snowflake account. Ensure your connection uses a role with privileges to create databases, schemas, tables, stages, Cortex Search services, and agents.\n\n## Setup Data\n\nWe'll use Cortex Code to execute the SQL that creates our sales intelligence database. Clone the companion repository first:\n\n```bash\ngit clone https://github.com/Snowflake-Labs/sfguide-getting-started-with-cortex-agents.git\ncd sfguide-getting-started-with-cortex-agents\n```\n\nNow launch Cortex Code from the repo directory:\n```bash\ncortex\n```\n\n**Step 1.** Ask Cortex Code to run the setup SQL. You can paste this prompt:\n\n```\nRun the SQL in setup.sql to create the sales intelligence database, tables, \nsample data, and Cortex Search service.\n```\n\nCortex Code will execute each statement from [setup.sql](https://github.com/Snowflake-Labs/sfguide-getting-started-with-cortex-agents/blob/main/setup.sql), which:\n- Creates the `SALES_INTELLIGENCE` database, `DATA` schema, and `SALES_INTELLIGENCE_WH` warehouse\n- Creates tables for sales conversations and metrics\n- Loads sample sales data\n- Enables change tracking for real-time updates\n- Configures the `SALES_CONVERSATION_SEARCH` Cortex Search service\n- Creates a `MODELS` stage for semantic models\n- Grants the necessary permissions\n\n**Step 2.** Upload the semantic model. The Agent needs rules to interpret your structured data. This semantic model defines key business terms to ensure the Agent generates highly accurate SQL. Ask Cortex Code:\n\n```\nUpload sales_metrics_model.yaml to the @SALES_INTELLIGENCE.DATA.MODELS stage.\n```\n\nCortex Code will run:\n```sql\nPUT file://sales_metrics_model.yaml @SALES_INTELLIGENCE.DATA.MODELS AUTO_COMPRESS=FALSE OVERWRITE=TRUE;\n```\n\n## Create Agent\n\nNow we'll use Cortex Code's built-in **cortex-agent** skill to create the Sales Intelligence Agent. This skill provides a guided, interactive workflow for agent creation.\n\n**Step 1.** In Cortex Code, ask it to create the agent:\n\n```\nCreate an agent called SALES_INTELLIGENCE_AGENT in SALES_INTELLIGENCE.DATA\n```\n\nCortex Code will load the `cortex-agent` skill and walk you through the creation workflow interactively.\n\n**Step 2.** When prompted for **administrative setup**, provide:\n- **Database**: `SALES_INTELLIGENCE`\n- **Schema**: `DATA`\n- **Agent Name**: `SALES_INTELLIGENCE_AGENT`\n- **Role**: `SALES_INTELLIGENCE_ROLE`\n\n**Step 3.** When prompted for the agent's **purpose and requirements**, explain:\n\n```\nThis agent orchestrates between sales data for analyzing sales conversations \nusing Cortex Search and metrics using Cortex Analyst. It should answer questions \nabout both structured sales metrics (revenue, deals, KPIs) and unstructured \nsales conversations (call transcripts, meeting notes).\n```\n\n**Step 4.** When prompted for **tools**, select the existing tools in the `SALES_INTELLIGENCE.DATA` schema:\n- **Cortex Search Service**: `SALES_CONVERSATION_SEARCH` — for searching through sales conversation transcripts\n- **Cortex Analyst (Semantic Model)**: `sales_metrics_model.yaml` on the `MODELS` stage — for querying structured sales metrics\n\n**Step 5.** When prompted for **response instructions**, provide:\n\n```\nYou are a specialized analytics assistant focused on providing concise responses \nabout sales metrics and sales conversations. Your primary objectives are:\n\nFor structured data queries (metrics, KPIs, sales figures):\n- Use the Cortex Analyst which is YAML-based functionality\n- Provide direct, numerical answers with minimal explanation\n- Format responses clearly with relevant units and time periods\n- Only include essential context needed to understand the metric\n\nFor unstructured content from sales conversations:\n- Utilize the Cortex Search service SALES_INTELLIGENCE.DATA.SALES_CONVERSATION_SEARCH\n- Extract relevant information from conversations\n- Summarize findings in brief, focused responses\n- Maintain context from the original sales conversations\n\nOperating guidelines:\n- Always identify whether you're using Cortex Analyst or Cortex Search for each response\n- Keep responses under 3-4 sentences when possible\n- Present numerical data in a structured format\n- Don't speculate beyond available data\n```\n\n**Step 6.** When prompted for **orchestration instructions**, provide:\n\n```\nIf a query spans both structured and unstructured data, clearly separate the sources. \nFor any query, first determine whether it requires:\n(a) Structured data analysis → Use YAML/Cortex Analyst\n(b) Report content/context → Use Cortex Search\n(c) Both → Combine both services with clear source attribution\n\nPlease confirm which approach you'll use before providing each response.\n```\n\n**Step 7.** Cortex Code will create the agent via the REST API and verify its configuration. You can confirm the agent was created by asking:\n\n```\nList agents in SALES_INTELLIGENCE.DATA\n```\n\n**Step 8.** Grant access to the role. Ask Cortex Code:\n\n```\nGrant usage on agent SALES_INTELLIGENCE.DATA.SALES_INTELLIGENCE_AGENT to role SALES_INTELLIGENCE_ROLE\n```\n\n## Test the Agent\n\nNow that your Agent is fully configured, let's test it directly from Cortex Code. Ask Cortex Code to chat with the agent:\n\n```\nChat with agent SALES_INTELLIGENCE.DATA.SALES_INTELLIGENCE_AGENT\n```\n\nCortex Code will load the chat-with-agent capability. Try these questions to test both structured and unstructured data:\n\n**Test 1 — Structured data (Cortex Analyst):**\n```\nHow many deals did Sarah Johnson win compared to deals she lost?\n```\nThis question requires the Agent to run a SQL query against the sales metrics table using Cortex Analyst.\n\n**Test 2 — Unstructured data (Cortex Search):**\n```\nTell me about the call with Securebank?\n```\nThis question requires the Agent to look up contextual information within the long text transcripts in the sales conversations table using Cortex Search.\n\n**Test 3 — Combined:**\n```\nWhat were Sarah Johnson's total deal values, and what feedback came up in her conversations?\n```\nThis tests the agent's ability to orchestrate across both tools.\n\n## Agent REST API\n\nIn addition to testing in Cortex Code, you can integrate your Agent into custom applications. You can interact with the Agent by calling the Snowflake REST API at `/api/v2/databases/{DATABASE}/schemas/{SCHEMA}/agents/{AGENT}:run`.\n\nWe've created a simple Streamlit application that interacts with the REST API [here](https://github.com/Snowflake-Labs/sfguide-getting-started-with-cortex-agents/blob/main/data_agent_demo.py). You can use it as a building block to build your own applications. \n\nSince this API call is made outside of Snowsight, we need a secure key to authenticate your identity and grant the application permission to run the Agent. This key is a Programmatic Access Token (PAT).\n\nLet's run the Streamlit app and call the API locally:\n\n**Step 1.** Create a [Programmatic Access Token](https://docs.snowflake.com/en/user-guide/programmatic-access-tokens). You can do this via Cortex Code:\n\n```\nCreate a programmatic access token for the SALES_INTELLIGENCE_ROLE role\n```\n\nOr manually in Snowsight: Click on your profile (bottom left corner) → Settings → Authentication → Under `Programmatic access tokens`, click `Generate new token` → Select `Single Role` and select `sales_intelligence_role`. Copy and save the token.\n\n**Step 2.** Find your account URL. Ask Cortex Code:\n\n```\nWhat is my account URL?\n```\n\nOr in Snowsight: Click on your profile (bottom left corner) → Account → View account details.\n\n**Step 3.** Set up and run the Streamlit app. In the cloned repository directory, run:\n\n```bash\npython3 -m venv venv\nsource venv/bin/activate\npip3 install -r requirements.txt\n\nCORTEX_AGENT_DEMO_PAT=\u003CPAT\u003E \\\nCORTEX_AGENT_DEMO_HOST=\u003CACCOUNT_URL\u003E \\\nCORTEX_AGENT_DEMO_DATABASE=\"SALES_INTELLIGENCE\" \\\nCORTEX_AGENT_DEMO_SCHEMA=\"DATA\" \\\nCORTEX_AGENT_DEMO_AGENT=\"SALES_INTELLIGENCE_AGENT\" \\\nstreamlit run data_agent_demo.py\n```\n\nReplace `\u003CPAT\u003E` with the token from Step 1 and `\u003CACCOUNT_URL\u003E` with your account URL from Step 2.\n\nWhen you're done using the application, press `Ctrl+C` in the terminal to shut down the Streamlit server.\n\n## Conclusion And Resources\n\nCongratulations! You've successfully built an Intelligent Sales Assistant using Snowflake Cortex Agents — entirely from the command line with Cortex Code CLI. This application demonstrates the power of combining structured and unstructured data analysis through:\n- Natural language interactions with your sales data\n- Semantic search across sales conversations\n- Automated SQL generation for analytics\n- Interactive agent testing directly in the terminal\n- External application access via the REST API with Streamlit\n\n### What You Learned\n- **Cortex Code CLI**: How to use Snowflake's AI-powered CLI to create and manage agents conversationally\n- **Cortex Agents**: How to create and configure agents using the `cortex-agent` skill for combining search and analysis capabilities\n- **Cortex Search**: How to leverage hybrid search combining semantic and keyword approaches for more accurate results\n- **Cortex Analyst**: How to convert natural language to SQL using semantic models for high-accuracy analytics\n- **Testing**: How to chat with your agent directly from Cortex Code to validate behavior\n- **Integration**: How to connect external applications via the REST API using Streamlit\n\n### Related Resources\n- [Cortex Code CLI Documentation](https://docs.snowflake.com/en/user-guide/cortex-code/cortex-code-cli)\n- [Cortex Agents Guide](https://docs.snowflake.com/user-guide/snowflake-cortex/cortex-agents)\n- [Cortex Search Overview](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-search/cortex-search-overview)\n- [Cortex Analyst Overview](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-analyst)\n- [Snowflake Cortex Documentation](https://docs.snowflake.com/en/user-guide/snowflake-cortex/llm-functions)\n- [Snowflake Intelligence Documentation](https://docs.snowflake.com/en/user-guide/snowflake-cortex/snowflake-intelligence)\n","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],":type":"snowflake-site/components/contentfragment","isDeveloperGuidesPage":false,":items":{},":itemsOrder":[],"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-e6cb4c404d","type":"2-column-75-25","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"none","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-15c97ac6d1",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-556c53b34c","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2026-06-17",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-149fb2428d","additionalClasses":"qs-disclaimer-text","text":"\u003Cp\u003E\u003Cspan style=\"color: #666;\"\u003EThis content is provided as is, and is not maintained on an ongoing basis. 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