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pre[class*=language-]{background-color:rgba(var(--ui-12-rgb),.5);color:var(--text-01);text-shadow:none;padding:var(--spacing-00);border-radius:var(--spacing-00);font-size:smaller}","isGSAPEnabled":false,":type":"snowflake-site/components/markup-editor"},"responsivegrid":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"quickstart_hero":"aem-GridColumn aem-GridColumn--default--12","flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12","markup_editor":"aem-GridColumn aem-GridColumn--default--12"},"columnCount":12,":items":{"quickstart_hero":{"id":"quickstart-hero-70daec625a","quickstartHeroFirstCertifiedTag":{"tagText":"Quickstart","tagColor":"#29B5E8","tagPath":"/content/cq:tags/snowflake-site/taxonomy/solution-center/certification/quickstart","tagIcon":""},"isDeveloperGuidesPage":false,"quickstartHeroTitle":{"lines":["Build a Cortex Agent from Scratch with 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-->\n","\u003Ch2\u003EOverview\u003C/h2\u003E\n","\u003Cp\u003EEvery organization sits on two kinds of data: \u003Cstrong\u003Estructured data\u003C/strong\u003E (numbers in tables like sales figures, inventory counts, and transaction logs) and \u003Cstrong\u003Eunstructured data\u003C/strong\u003E (text in documents like product manuals, troubleshooting guides, and policy documents). Traditionally, getting answers from these two worlds required completely different tools and skills. Want to know last quarter's revenue? Write a SQL query. Need to find the assembly instructions for a product? Search through a document repository. Want both in one conversation? Good luck stitching those workflows together manually.\u003C/p\u003E\n","\u003Cp\u003EThis is the problem \u003Cstrong\u003EAI agents\u003C/strong\u003E solve. An AI agent doesn't just generate text like a basic LLM call. It \u003Cem\u003Ereasons\u003C/em\u003E about your question, \u003Cem\u003Edecides\u003C/em\u003E which tool to use, \u003Cem\u003Eexecutes\u003C/em\u003E that tool, and \u003Cem\u003Esynthesizes\u003C/em\u003E the results into a coherent answer. Ask it "What are total sales by region?" and it routes to a SQL engine. Ask it "How do I fix laptop overheating?" and it searches your documentation. Ask both in the same conversation, and it handles each seamlessly.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ECortex Agents\u003C/strong\u003E bring this capability directly into Snowflake. You don't need to set up external orchestration frameworks, manage API keys for third-party services, or write complex routing logic. Everything (the agent, its tools, and the data it accesses) lives inside your Snowflake account, governed by the same roles and permissions you already use.\u003C/p\u003E\n","\u003Cp\u003EIn this guide, you'll build an end-to-end pipeline from scratch, starting with the data. You'll create and load sample data into tables, create a \u003Cstrong\u003Esemantic view\u003C/strong\u003E that lets the agent translate natural language into SQL, build a \u003Cstrong\u003ECortex Search service\u003C/strong\u003E that lets it retrieve relevant documentation, and wire both into a \u003Cstrong\u003ECortex Agent\u003C/strong\u003E that answers questions about sales data and product documentation, all with standard SQL.\u003C/p\u003E\n","\u003Ch3\u003EWhat You'll Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EWhat Cortex Agents are and how they orchestrate across structured and unstructured data\u003C/li\u003E\u003Cli\u003EWhat semantic views are and how they enable natural language to SQL translation\u003C/li\u003E\u003Cli\u003EWhat Cortex Search services are and how they power retrieval-augmented generation (RAG)\u003C/li\u003E\u003Cli\u003EHow to create an agent with full tool configuration using \u003Ccode\u003ECREATE AGENT ... FROM SPECIFICATION\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EHow to test an agent with \u003Ccode\u003EDATA_AGENT_RUN\u003C/code\u003E and parse its responses with Python\u003C/li\u003E\u003Cli\u003EHow to interact with an agent through Snowflake CoWork's chat interface\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Build\u003C/h3\u003E\n","\u003Cp\u003EA Cortex Agent with two tools:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ECortex Analyst\u003C/strong\u003E: takes natural language questions like "What are total sales by region?" and automatically converts them into SQL queries that run against your sales data\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ECortex Search\u003C/strong\u003E: takes questions like "How do I assemble the standing desk?" and retrieves the most relevant product documentation using semantic search\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EThe agent automatically decides which tool to use based on what the user asks. You don't write any routing logic; the agent figures it out.\u003C/p\u003E\n<!-- Workflow diagram (editable): https://excalidraw.com/#json=MioFuiqlV9qvS_486Ezdl,ui60jdFQ8rz79OBXlQABcg -->\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-a-cortex-agent-from-scratch-with-snowflake/diagram.png?v=c44e93c1\" alt=\"Cortex Agent workflow diagram\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\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 (if you don't have one, you can sign up for a free trial)\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E role (or a role with \u003Ccode\u003ECREATE AGENT\u003C/code\u003E, \u003Ccode\u003ECREATE SEMANTIC VIEW\u003C/code\u003E, and \u003Ccode\u003ECREATE CORTEX SEARCH SERVICE\u003C/code\u003E privileges)\u003C/li\u003E\u003Cli\u003EA running warehouse (this guide uses \u003Ccode\u003ECOMPUTE_WH\u003C/code\u003E, but any warehouse will work)\u003C/li\u003E\u003Cli\u003ECortex AI enabled on your account (available in most Snowflake regions)\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESetup Environment\u003C/h2\u003E\n","\u003Cp\u003EBefore building anything, you need a workspace in Snowflake to hold all the objects you'll create (tables, semantic views, search services, and the agent itself). Think of a \u003Cstrong\u003Edatabase\u003C/strong\u003E as a top-level folder and a \u003Cstrong\u003Eschema\u003C/strong\u003E as a subfolder within it.\u003C/p\u003E\n","\u003Cp\u003EYou'll also verify that Cortex AI is available on your account, since the agent depends on it.\u003C/p\u003E\n","\u003Ch3\u003EWhere to Run SQL\u003C/h3\u003E\n","\u003Cp\u003EYou can run all the SQL in this guide in either:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ESQL Worksheet\u003C/strong\u003E: In Snowsight, click \u003Cstrong\u003E+ > SQL Worksheet\u003C/strong\u003E in the top left\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESnowflake Notebook\u003C/strong\u003E: In Snowsight, click \u003Cstrong\u003E+ > Notebook\u003C/strong\u003E (useful if you want to mix SQL and Python cells, since you'll need Python for the testing section later)\u003C/li\u003E\u003C/ul\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWant everything in one notebook?\u003C/strong\u003E Download the \u003Ca href=\"https://github.com/Snowflake-Labs/snowflake-demo-notebooks/blob/main/Build-a-Cortex-Agent-from-Scratch-with-Snowflake/build-a-cortex-agent-from-scratch-with-snowflake.ipynb\"\u003Ecompanion notebook\u003C/a\u003E and import it into Snowsight (\u003Cstrong\u003E+ > Notebook > Import .ipynb file\u003C/strong\u003E).\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003ESet Up Database and Schema\u003C/h3\u003E\n","\u003Cp\u003ECopy and paste the following SQL and run it. Each line is explained below:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Use the ACCOUNTADMIN role, which has full privileges\nUSE ROLE ACCOUNTADMIN;\n\n-- Select a warehouse (compute resource) to run queries\nUSE WAREHOUSE COMPUTE_WH;\n\n-- Create a new database to hold all tutorial objects\nCREATE DATABASE IF NOT EXISTS CORTEX_AGENTS_LAB;\nUSE DATABASE CORTEX_AGENTS_LAB;\n\n-- Create a schema (subfolder) inside the database\nCREATE SCHEMA IF NOT EXISTS TUTORIAL;\nUSE SCHEMA TUTORIAL;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWhat each command does:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ccode\u003EUSE ROLE ACCOUNTADMIN\u003C/code\u003E sets your active role. \u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E has all privileges, which simplifies this tutorial. In production, you'd use a more restricted role.\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003EUSE WAREHOUSE COMPUTE_WH\u003C/code\u003E selects which compute resource runs your queries. If your warehouse has a different name, replace \u003Ccode\u003ECOMPUTE_WH\u003C/code\u003E with your warehouse name.\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003ECREATE DATABASE\u003C/code\u003E / \u003Ccode\u003ECREATE SCHEMA\u003C/code\u003E creates the containers for all the objects you'll build. \u003Ccode\u003EIF NOT EXISTS\u003C/code\u003E means it won't error if they already exist.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EVerify Cortex Access\u003C/h3\u003E\n","\u003Cp\u003EBefore going further, confirm that Cortex AI is working on your account. Run this simple test that asks an LLM to respond:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT SNOWFLAKE.CORTEX.COMPLETE('claude-3-5-sonnet', 'Say hello in one word');\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWhat this does:\u003C/strong\u003E \u003Ccode\u003ESNOWFLAKE.CORTEX.COMPLETE()\u003C/code\u003E is a built-in function that sends a prompt to an LLM and returns the response. Here, we're using \u003Ccode\u003Eclaude-3-5-sonnet\u003C/code\u003E as the model. If it returns something like "Hello", you're all set.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EIf you get an error:\u003C/strong\u003E Cortex AI may not be enabled in your account's region. Check the \u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/llm-functions#availability\"\u003ECortex AI availability documentation\u003C/a\u003E for supported regions.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECreate Sample Data\u003C/h2\u003E\n","\u003Cp\u003ENow you'll create the data that your agent will work with. You need two types:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\u003Cstrong\u003EStructured data\u003C/strong\u003E (a sales table with numbers and categories) that Cortex Analyst will query with SQL\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EUnstructured data\u003C/strong\u003E (product documentation as free-form text) that Cortex Search will index and retrieve\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EYou'll also create an inventory table that's useful for exploring the data, though the agent in this tutorial focuses on sales and documentation.\u003C/p\u003E\n","\u003Ch3\u003ECreate the Sales Table\u003C/h3\u003E\n","\u003Cp\u003EThis table represents transaction-level sales data for a retail business. Each row is one sale, with information about what was sold, where, for how much, and to what type of customer.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE TABLE sales (\n    sale_id NUMBER AUTOINCREMENT,\n    sale_date DATE,\n    product_name VARCHAR,\n    category VARCHAR,\n    region VARCHAR,\n    quantity NUMBER,\n    unit_price NUMBER(10,2),\n    total_amount NUMBER(10,2),\n    customer_segment VARCHAR\n);\n\nINSERT INTO sales (sale_date, product_name, category, region, quantity, unit_price, total_amount, customer_segment)\nVALUES\n    ('2024-01-15', 'Laptop Pro', 'Electronics', 'North America', 10, 1299.99, 12999.90, 'Enterprise'),\n    ('2024-01-16', 'Wireless Mouse', 'Electronics', 'Europe', 50, 29.99, 1499.50, 'SMB'),\n    ('2024-01-17', 'Office Chair', 'Furniture', 'North America', 20, 299.99, 5999.80, 'Enterprise'),\n    ('2024-01-18', 'Standing Desk', 'Furniture', 'Asia Pacific', 15, 499.99, 7499.85, 'SMB'),\n    ('2024-01-19', 'Monitor 27"', 'Electronics', 'Europe', 30, 399.99, 11999.70, 'Enterprise'),\n    ('2024-01-20', 'Keyboard Pro', 'Electronics', 'North America', 100, 149.99, 14999.00, 'Consumer'),\n    ('2024-02-01', 'Laptop Pro', 'Electronics', 'Asia Pacific', 25, 1299.99, 32499.75, 'Enterprise'),\n    ('2024-02-05', 'Webcam HD', 'Electronics', 'North America', 75, 79.99, 5999.25, 'SMB'),\n    ('2024-02-10', 'Office Chair', 'Furniture', 'Europe', 40, 299.99, 11999.60, 'Enterprise'),\n    ('2024-02-15', 'Headphones', 'Electronics', 'North America', 60, 199.99, 11999.40, 'Consumer'),\n    ('2024-03-01', 'Standing Desk', 'Furniture', 'North America', 35, 499.99, 17499.65, 'Enterprise'),\n    ('2024-03-10', 'Laptop Pro', 'Electronics', 'Europe', 20, 1299.99, 25999.80, 'SMB');\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWhat this creates:\u003C/strong\u003E 12 sales transactions across 3 regions (North America, Europe, Asia Pacific), 2 categories (Electronics, Furniture), and 3 customer segments (Enterprise, SMB, Consumer). The \u003Ccode\u003EAUTOINCREMENT\u003C/code\u003E on \u003Ccode\u003Esale_id\u003C/code\u003E means Snowflake automatically assigns an incrementing ID to each row.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EQuick check:\u003C/strong\u003E Preview the data to see what you loaded:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT * FROM sales ORDER BY sale_date LIMIT 5;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ECreate the Inventory Table\u003C/h3\u003E\n","\u003Cp\u003EThis table tracks stock levels for each product. It's not directly used by the agent in this tutorial, but it's included so you can explore the data and potentially extend the agent later.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE TABLE inventory (\n    product_name VARCHAR,\n    sku VARCHAR,\n    quantity_in_stock NUMBER,\n    reorder_level NUMBER,\n    unit_cost NUMBER(10,2),\n    last_restocked DATE\n);\n\nINSERT INTO inventory VALUES\n    ('Laptop Pro', 'LP-001', 45, 20, 899.99, '2024-03-01'),\n    ('Wireless Mouse', 'WM-002', 500, 100, 12.99, '2024-02-15'),\n    ('Office Chair', 'OC-003', 75, 25, 149.99, '2024-02-20'),\n    ('Standing Desk', 'SD-004', 30, 15, 299.99, '2024-03-05'),\n    ('Monitor 27"', 'MN-005', 60, 20, 249.99, '2024-02-28'),\n    ('Keyboard Pro', 'KP-006', 200, 50, 79.99, '2024-03-10');\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ECreate Product Documentation\u003C/h3\u003E\n","\u003Cp\u003EThis is the \u003Cstrong\u003Eunstructured data\u003C/strong\u003E that Cortex Search will index. Each row contains a text document about a product, covering things like specifications, troubleshooting guides, assembly instructions, and care tips.\u003C/p\u003E\n","\u003Cp\u003EUnlike the sales table (which has clean numeric columns you can aggregate), this data is free-form text that requires semantic search to be useful.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE TABLE product_docs (\n    doc_id NUMBER AUTOINCREMENT,\n    product_name VARCHAR,\n    doc_type VARCHAR,\n    content VARCHAR,\n    last_updated DATE\n);\n\nINSERT INTO product_docs (product_name, doc_type, content, last_updated)\nVALUES\n    ('Laptop Pro', 'specifications', \n     'The Laptop Pro features a 15.6-inch 4K display, Intel i9 processor, 32GB RAM, and 1TB SSD. Battery life is up to 12 hours. Includes Thunderbolt 4 ports and Wi-Fi 6E. Weight: 4.2 lbs. Warranty: 3 years standard, extendable to 5 years.',\n     '2024-01-01'),\n    ('Laptop Pro', 'troubleshooting',\n     'Common issues: 1) Battery drain - check background apps and reduce screen brightness. 2) Overheating - ensure vents are not blocked, use on hard surface. 3) Slow performance - check for updates, run disk cleanup. 4) Wi-Fi issues - update network drivers, reset network settings.',\n     '2024-01-15'),\n    ('Standing Desk', 'specifications',\n     'Electric standing desk with memory presets. Height range: 28-48 inches. Desktop size: 60x30 inches. Weight capacity: 300 lbs. Motor: dual motor system for stability. Includes cable management tray and anti-collision sensor.',\n     '2024-01-01'),\n    ('Standing Desk', 'assembly',\n     'Assembly instructions: 1) Attach legs to frame using provided bolts. 2) Connect motor cables to control box. 3) Mount desktop to frame. 4) Connect power cord. 5) Program height presets using control panel. Assembly time: approximately 45 minutes. Tools needed: Phillips screwdriver.',\n     '2024-01-10'),\n    ('Office Chair', 'specifications',\n     'Ergonomic office chair with lumbar support. Adjustable armrests, seat height, and tilt tension. Breathable mesh back. Seat dimensions: 20x19 inches. Weight capacity: 275 lbs. Warranty: 5 years on frame, 2 years on upholstery.',\n     '2024-01-01'),\n    ('Office Chair', 'care',\n     'Care instructions: Clean mesh with mild soap and water. Lubricate casters annually. Check and tighten bolts every 6 months. Do not exceed weight capacity. Store in dry environment. Replace gas cylinder if chair sinks.',\n     '2024-02-01');\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWhat this creates:\u003C/strong\u003E 6 documents covering 3 products. Notice the \u003Ccode\u003Econtent\u003C/code\u003E column: it contains plain English text, not structured data. This is exactly the kind of data that's hard to query with SQL (you can't \u003Ccode\u003ESUM\u003C/code\u003E or \u003Ccode\u003EGROUP BY\u003C/code\u003E a paragraph of text) but perfect for semantic search.\u003C/p\u003E\n","\u003Ch3\u003EVerify All Data\u003C/h3\u003E\n","\u003Cp\u003ERun this query to confirm all three tables loaded correctly:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT 'sales' as table_name, COUNT(*) as row_count FROM sales\nUNION ALL SELECT 'inventory', COUNT(*) FROM inventory\nUNION ALL SELECT 'product_docs', COUNT(*) FROM product_docs;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EExpected results:\u003C/strong\u003E 12 sales rows, 6 inventory rows, and 6 product docs rows. If any count is off, re-run the \u003Ccode\u003EINSERT\u003C/code\u003E statements for that table.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECreate Semantic View\u003C/h2\u003E\n","\u003Cp\u003ENow you'll create the first tool the agent will use: \u003Cstrong\u003ECortex Analyst\u003C/strong\u003E, which converts natural language questions into SQL queries.\u003C/p\u003E\n","\u003Cp\u003EBut there's a challenge: how does an LLM know that "revenue" means \u003Ccode\u003ESUM(total_amount)\u003C/code\u003E or that "region" refers to the \u003Ccode\u003Eregion\u003C/code\u003E column? It doesn't, unless you tell it. That's what a \u003Cstrong\u003Esemantic view\u003C/strong\u003E does.\u003C/p\u003E\n","\u003Cp\u003EA semantic view is a layer on top of your table that defines the \u003Cstrong\u003Ebusiness meaning\u003C/strong\u003E of your data:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EDimensions\u003C/strong\u003E: the categorical columns you group by (product name, region, customer segment)\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EMetrics\u003C/strong\u003E: the calculations users care about (total sales, units sold, transaction count)\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EComments\u003C/strong\u003E: plain English descriptions that help the LLM understand each field\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EThink of it as a data dictionary that the LLM reads to write correct SQL.\u003C/p\u003E\n","\u003Ch3\u003ECreate the Semantic View\u003C/h3\u003E\n","\u003Cp\u003ESemantic views are defined with SQL using \u003Ccode\u003ETABLES\u003C/code\u003E, \u003Ccode\u003EDIMENSIONS\u003C/code\u003E, and \u003Ccode\u003EMETRICS\u003C/code\u003E clauses. Run the following:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE SEMANTIC VIEW sales_semantic_view\n  TABLES (\n    sales AS CORTEX_AGENTS_LAB.TUTORIAL.SALES\n      PRIMARY KEY (sale_id)\n      COMMENT = 'Transaction-level sales data'\n  )\n  DIMENSIONS (\n    sales.product_name AS product_name\n      COMMENT = 'Name of the product sold',\n    sales.category AS category\n      COMMENT = 'Product category (Electronics, Furniture)',\n    sales.region AS region\n      COMMENT = 'Sales region (North America, Europe, Asia Pacific)',\n    sales.customer_segment AS customer_segment\n      COMMENT = 'Customer type (Enterprise, SMB, Consumer)'\n  )\n  METRICS (\n    sales.total_sales AS SUM(total_amount)\n      COMMENT = 'Total sales amount in USD',\n    sales.total_quantity AS SUM(quantity)\n      COMMENT = 'Total units sold',\n    sales.transaction_count AS COUNT(*)\n      COMMENT = 'Number of sales transactions'\n  )\n  COMMENT = 'Sales data for analyzing revenue, products, and regional performance';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EBreaking this down:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003E\u003Ccode\u003ETABLES\u003C/code\u003E\u003C/strong\u003E tells the semantic view which physical table to query. The \u003Ccode\u003EAS CORTEX_AGENTS_LAB.TUTORIAL.SALES\u003C/code\u003E part uses the fully qualified table name (database.schema.table).\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003E\u003Ccode\u003EDIMENSIONS\u003C/code\u003E\u003C/strong\u003E lists the columns that users can filter or group by. Each one has a \u003Ccode\u003ECOMMENT\u003C/code\u003E that the LLM reads to understand what it represents.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003E\u003Ccode\u003EMETRICS\u003C/code\u003E\u003C/strong\u003E defines named calculations. When a user asks about "total sales," the LLM knows to use \u003Ccode\u003ESUM(total_amount)\u003C/code\u003E. This is where you encode your business logic.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003E\u003Ccode\u003ECOMMENT\u003C/code\u003E\u003C/strong\u003E at the view level describes the overall purpose. This helps the agent decide whether to route a question to this tool.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EVerify the Semantic View\u003C/h3\u003E\n","\u003Cp\u003EConfirm the semantic view was created:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESHOW SEMANTIC VIEWS;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou should see \u003Ccode\u003ESALES_SEMANTIC_VIEW\u003C/code\u003E in the results.\u003C/p\u003E\n","\u003Ch3\u003ETest the Semantic View\u003C/h3\u003E\n","\u003Cp\u003EBefore wiring it into the agent, query the semantic view directly to make sure it works. This is a good debugging practice: if the semantic view doesn't return correct data, the agent won't either.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT * FROM SEMANTIC_VIEW(\n    CORTEX_AGENTS_LAB.TUTORIAL.SALES_SEMANTIC_VIEW\n    METRICS total_sales, total_quantity\n    DIMENSIONS region\n)\nORDER BY total_sales DESC;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWhat this does:\u003C/strong\u003E Uses the \u003Ccode\u003ESEMANTIC_VIEW()\u003C/code\u003E function to query the view just like a table, but using the business names you defined (e.g., \u003Ccode\u003Etotal_sales\u003C/code\u003E instead of \u003Ccode\u003ESUM(total_amount)\u003C/code\u003E). You should see sales totals broken down by region.\u003C/p\u003E\n","\u003Ch3\u003EQuick Cortex Analyst Demo\u003C/h3\u003E\n","\u003Cp\u003ETo see the natural-language-to-SQL translation in action, try asking an LLM to write a query based on the schema:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT SNOWFLAKE.CORTEX.COMPLETE(\n    'claude-4-sonnet',\n    'Given a sales table with columns: product_name, category, region, quantity, unit_price, total_amount, customer_segment - write a SQL query to find total sales by region. Return ONLY the SQL.'\n);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWhy this matters:\u003C/strong\u003E This is essentially what Cortex Analyst does under the hood. It takes your natural language question, reads the semantic view's definitions, and generates a SQL query. The semantic view just makes it far more accurate by giving the LLM explicit business context instead of making it guess.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECreate Search Service\u003C/h2\u003E\n","\u003Cp\u003ENow you'll create the second tool: \u003Cstrong\u003ECortex Search\u003C/strong\u003E, which enables the agent to find relevant product documentation using semantic search.\u003C/p\u003E\n","\u003Ch3\u003EWhat is Semantic Search?\u003C/h3\u003E\n","\u003Cp\u003ETraditional SQL search requires exact matches. \u003Ccode\u003EWHERE content LIKE '%overheating%'\u003C/code\u003E only finds documents containing that exact word. But what if a user asks "my laptop is getting too hot"? A \u003Ccode\u003ELIKE\u003C/code\u003E query would miss the troubleshooting document because it uses the word "overheating," not "too hot."\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ESemantic search\u003C/strong\u003E understands the \u003Cem\u003Emeaning\u003C/em\u003E behind words. It converts text into numerical vectors (embeddings) and finds documents that are conceptually similar to the query, even if they use different words.\u003C/p\u003E\n","\u003Ch3\u003EWhat is RAG?\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003ERetrieval-Augmented Generation (RAG)\u003C/strong\u003E is the pattern where you:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\u003Cstrong\u003ERetrieve\u003C/strong\u003E relevant documents using search\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAugment\u003C/strong\u003E an LLM prompt with those documents as context\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EGenerate\u003C/strong\u003E an answer grounded in the retrieved information\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003ECortex Search handles the retrieval step. The agent handles the augmentation and generation steps automatically.\u003C/p\u003E\n","\u003Ch3\u003ECreate the Cortex Search Service\u003C/h3\u003E\n","\u003Cp\u003ERun the following to create a search service that indexes your product documentation:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE CORTEX SEARCH SERVICE product_search_service\n  ON content\n  ATTRIBUTES product_name, doc_type\n  WAREHOUSE = COMPUTE_WH\n  TARGET_LAG = '1 hour'\n  AS (\n    SELECT \n      doc_id,\n      product_name,\n      doc_type,\n      content\n    FROM product_docs\n  );\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EBreaking this down:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003E\u003Ccode\u003EON content\u003C/code\u003E\u003C/strong\u003E tells Cortex Search which column contains the text to index for semantic search. This is the column users will be searching against.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003E\u003Ccode\u003EATTRIBUTES product_name, doc_type\u003C/code\u003E\u003C/strong\u003E are additional columns that can be returned alongside search results and used for filtering (e.g., "only show results for Laptop Pro").\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003E\u003Ccode\u003EWAREHOUSE = COMPUTE_WH\u003C/code\u003E\u003C/strong\u003E is the compute resource used to build and refresh the search index.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003E\u003Ccode\u003ETARGET_LAG = '1 hour'\u003C/code\u003E\u003C/strong\u003E controls how frequently the index refreshes when the underlying data changes. For this tutorial, 1 hour is fine. In production, you might set this shorter for frequently updated data.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003E\u003Ccode\u003EAS (SELECT ...)\u003C/code\u003E\u003C/strong\u003E is the source query that feeds data into the index.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EVerify the Search Service\u003C/h3\u003E\n","\u003Cp\u003EConfirm the service was created:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESHOW CORTEX SEARCH SERVICES;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou should see \u003Ccode\u003EPRODUCT_SEARCH_SERVICE\u003C/code\u003E in the results. Note: the service takes a moment to build the initial index.\u003C/p\u003E\n","\u003Ch3\u003ETest the Search Service\u003C/h3\u003E\n","\u003Cp\u003ENow test it by searching for troubleshooting information about laptop overheating. This uses the \u003Ccode\u003ESEARCH_PREVIEW\u003C/code\u003E function, which returns results as JSON:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT \n  r.value:product_name::STRING AS product_name,\n  r.value:content::STRING AS content\nFROM TABLE(FLATTEN(\n  PARSE_JSON(\n    SNOWFLAKE.CORTEX.SEARCH_PREVIEW(\n      'CORTEX_AGENTS_LAB.TUTORIAL.PRODUCT_SEARCH_SERVICE',\n      '{"query": "laptop overheating fix", "columns": ["product_name", "content"], "limit": 3}'\n    )\n  )['results']\n)) r;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWhat's happening here (inside out):\u003C/strong\u003E\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\u003Ccode\u003ESEARCH_PREVIEW()\u003C/code\u003E sends the query "laptop overheating fix" to the search service and returns matching documents as a JSON string\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003EPARSE_JSON()\u003C/code\u003E converts the JSON string into a Snowflake VARIANT object\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003E['results']\u003C/code\u003E extracts the results array from the JSON\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003ETABLE(FLATTEN(...))\u003C/code\u003E expands the JSON array into rows (one row per search result)\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003Er.value:product_name::STRING\u003C/code\u003E extracts specific fields from each result\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EYou should see the Laptop Pro troubleshooting document returned, even though the search query ("laptop overheating fix") doesn't exactly match the document text.\u003C/p\u003E\n","\u003Ch3\u003EQuick RAG Demo\u003C/h3\u003E\n","\u003Cp\u003ETo see the full RAG pattern in action, take the search results and feed them into an LLM to generate an answer:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT SNOWFLAKE.CORTEX.COMPLETE(\n    'claude-4-sonnet',\n    'Based on this documentation, how do I fix laptop overheating? Documentation: ' ||\n    (SELECT LISTAGG(r.value:content::STRING, ' | ') \n     FROM TABLE(FLATTEN(\n       PARSE_JSON(\n         SNOWFLAKE.CORTEX.SEARCH_PREVIEW(\n           'CORTEX_AGENTS_LAB.TUTORIAL.PRODUCT_SEARCH_SERVICE',\n           '{"query": "laptop overheating", "columns": ["content"], "limit": 2}'\n         )\n       )['results']\n     )) r)\n);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWhat this does:\u003C/strong\u003E Retrieves the top 2 documents matching "laptop overheating," concatenates their content together using \u003Ccode\u003ELISTAGG\u003C/code\u003E, then passes that as context to the LLM along with the question. The LLM's answer is grounded in your actual product documentation rather than its general training data.\u003C/p\u003E\n","\u003Cp\u003EThis is exactly the pattern the Cortex Agent automates. It decides when to search, what to search for, and how to present the results, all without you having to write this query manually each time.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EBuild the Agent\u003C/h2\u003E\n","\u003Cp\u003EYou've now built both tools independently:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EA \u003Cstrong\u003Esemantic view\u003C/strong\u003E that Cortex Analyst uses to translate questions into SQL\u003C/li\u003E\u003Cli\u003EA \u003Cstrong\u003Esearch service\u003C/strong\u003E that Cortex Search uses to find relevant documents\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003ENow you'll create a \u003Cstrong\u003ECortex Agent\u003C/strong\u003E that sits on top of both tools and automatically routes each question to the right one. The agent is the orchestration layer that reads the user's question, decides which tool to call, executes it, and synthesizes a final answer.\u003C/p\u003E\n","\u003Ch3\u003EHow the Agent Works\u003C/h3\u003E\n","\u003Cp\u003EWhen you ask the agent a question, here's what happens behind the scenes:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\u003Cstrong\u003EThinking\u003C/strong\u003E: the agent's LLM reads your question and decides which tool is most appropriate\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ETool call\u003C/strong\u003E: the agent invokes the chosen tool (Analyst for SQL queries, Search for document retrieval)\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ETool result\u003C/strong\u003E: the tool returns its results (SQL + data, or matching documents)\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAnswer\u003C/strong\u003E: the agent synthesizes the tool's results into a natural language response\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EAll of this happens in a single call. You don't need to build this orchestration logic yourself.\u003C/p\u003E\n","\u003Ch3\u003ECreate the Agent\u003C/h3\u003E\n","\u003Cp\u003EThe \u003Ccode\u003ECREATE AGENT ... FROM SPECIFICATION\u003C/code\u003E command defines everything about the agent in a single YAML spec embedded in SQL. Run the following:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE AGENT tutorial_agent\n  COMMENT = 'Tutorial agent that routes questions to Analyst (structured) or Search (unstructured)'\n  FROM SPECIFICATION $$\nmodels:\n  orchestration: claude-4-sonnet\norchestration:\n  budget:\n    seconds: 30\n    tokens: 16000\ninstructions:\n  system: "You are a helpful assistant for a retail business. You can answer questions about sales data and product documentation."\n  orchestration: "Use Analyst for any question about sales, revenue, quantities, or metrics. Use Search for product documentation, troubleshooting, or how-to questions."\n  response: "Be concise and include relevant numbers or details from the tools."\ntools:\n  - tool_spec:\n      type: "cortex_analyst_text_to_sql"\n      name: "Analyst"\n      description: "Queries structured sales data by converting natural language to SQL"\n  - tool_spec:\n      type: "cortex_search"\n      name: "Search"\n      description: "Searches product documentation and troubleshooting guides"\ntool_resources:\n  Analyst:\n    semantic_view: "CORTEX_AGENTS_LAB.TUTORIAL.SALES_SEMANTIC_VIEW"\n    execution_environment:\n      type: "warehouse"\n      warehouse: "COMPUTE_WH"\n  Search:\n    name: "CORTEX_AGENTS_LAB.TUTORIAL.PRODUCT_SEARCH_SERVICE"\n    max_results: "3"\n$$;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EBreaking down the YAML spec section by section:\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E\u003Ccode\u003Emodels\u003C/code\u003E\u003C/strong\u003E specifies which LLM the agent uses for reasoning and tool selection:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ccode\u003Eorchestration: claude-4-sonnet\u003C/code\u003E is the model that decides which tool to call and synthesizes final answers\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003E\u003Ccode\u003Eorchestration\u003C/code\u003E\u003C/strong\u003E sets resource limits to prevent runaway queries:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ccode\u003Ebudget.seconds: 30\u003C/code\u003E is the maximum time the agent can spend on a single request\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003Ebudget.tokens: 16000\u003C/code\u003E is the maximum tokens the LLM can generate\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003E\u003Ccode\u003Einstructions\u003C/code\u003E\u003C/strong\u003E contains three types of instructions that shape the agent's behavior:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ccode\u003Esystem\u003C/code\u003E defines the agent's persona ("You are a helpful assistant for a retail business")\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003Eorchestration\u003C/code\u003E tells the agent \u003Cstrong\u003Ewhen to use each tool\u003C/strong\u003E. This is the most important instruction because it's how the agent knows that "total sales" should go to Analyst while "assembly instructions" should go to Search\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003Eresponse\u003C/code\u003E controls the output format ("Be concise and include relevant numbers")\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003E\u003Ccode\u003Etools\u003C/code\u003E\u003C/strong\u003E declares what tools are available. Each tool has:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ccode\u003Etype\u003C/code\u003E: the kind of tool (\u003Ccode\u003Ecortex_analyst_text_to_sql\u003C/code\u003E or \u003Ccode\u003Ecortex_search\u003C/code\u003E)\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003Ename\u003C/code\u003E: a label used to reference the tool in \u003Ccode\u003Etool_resources\u003C/code\u003E and \u003Ccode\u003Einstructions\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003Edescription\u003C/code\u003E: helps the LLM understand what the tool does\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003E\u003Ccode\u003Etool_resources\u003C/code\u003E\u003C/strong\u003E connects each tool to its underlying data source:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EAnalyst\u003C/strong\u003E points to the semantic view and specifies an \u003Ccode\u003Eexecution_environment\u003C/code\u003E (the warehouse that will run the generated SQL)\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESearch\u003C/strong\u003E points to the search service and sets \u003Ccode\u003Emax_results\u003C/code\u003E to limit how many documents are returned\u003C/li\u003E\u003C/ul\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ECommon mistake\u003C/strong\u003E: The \u003Ccode\u003Eexecution_environment\u003C/code\u003E block under Analyst is \u003Cstrong\u003Erequired\u003C/strong\u003E. If you use a bare \u003Ccode\u003Ewarehouse: "COMPUTE_WH"\u003C/code\u003E key instead of the nested \u003Ccode\u003Eexecution_environment\u003C/code\u003E block, the agent will fail with error 399504: "The Analyst tool is missing an execution environment."\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EVerify the Agent\u003C/h3\u003E\n","\u003Cp\u003EConfirm the agent was created:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESHOW AGENTS;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou should see \u003Ccode\u003ETUTORIAL_AGENT\u003C/code\u003E listed. The agent is now a first-class Snowflake object, just like a table or view.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ETest the Agent\u003C/h2\u003E\n","\u003Cp\u003ENow that the agent exists, you'll test it by asking questions and examining the full response to understand what's happening at each step.\u003C/p\u003E\n","\u003Ch3\u003EHow to Call the Agent\u003C/h3\u003E\n","\u003Cp\u003EYou call the agent using the \u003Ccode\u003ESNOWFLAKE.CORTEX.DATA_AGENT_RUN()\u003C/code\u003E function. It takes two arguments:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EThe fully qualified agent name (e.g., \u003Ccode\u003E'CORTEX_AGENTS_LAB.TUTORIAL.TUTORIAL_AGENT'\u003C/code\u003E)\u003C/li\u003E\u003Cli\u003EA JSON string containing the conversation messages\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EThe function returns a JSON string with the agent's complete response, including its thinking process, tool calls, and final answer.\u003C/p\u003E\n","\u003Ch3\u003EUnderstanding the Response Format\u003C/h3\u003E\n","\u003Cp\u003EThe response JSON has a \u003Ccode\u003Econtent\u003C/code\u003E array where each item has a \u003Ccode\u003Etype\u003C/code\u003E field. Here's what each type means:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EType\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EWhat It Contains\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EExample\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Ethinking\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EThe agent's reasoning about which tool to use\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E"The user is asking about sales metrics, so I should use the Analyst tool"\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Etool_use\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EThe tool call the agent decided to make\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ETool name and parameters\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Etool_result\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ERaw results from the tool\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EGenerated SQL, query results, or retrieved documents\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Etext\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EThe final human-readable answer\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E"Total sales by region: North America $69,497..."\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Ch3\u003ETest with a Structured Data Question\u003C/h3\u003E\n","\u003Cp\u003EThis test asks about sales data, which should route to the \u003Cstrong\u003EAnalyst\u003C/strong\u003E tool. Run this in a Snowflake Notebook Python cell:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eimport json\nfrom snowflake.snowpark.context import get_active_session\n\nsession = get_active_session()\n\n# Ask a structured data question (routes to Cortex Analyst)\nresult = session.sql("""\n  SELECT SNOWFLAKE.CORTEX.DATA_AGENT_RUN(\n    'CORTEX_AGENTS_LAB.TUTORIAL.TUTORIAL_AGENT',\n    $${"messages": [{"role": "user", "content": [{"type": "text", "text": "What are total sales by region?"}]}]}$$\n  ) AS resp\n""").collect()\n\n# Parse the JSON response\nresp = json.loads(result[0]["RESP"])\n\n# Walk through each item in the content array\nfor item in resp.get("content", []):\n    item_type = item.get("type")\n\n    if item_type == "thinking":\n        print("=== THINKING ===")\n        print(item["thinking"]["text"])\n        print()\n\n    elif item_type == "tool_use":\n        print(f"=== TOOL CALL: {item['tool_use'].get('name', '')} ({item['tool_use'].get('type', '')}) ===")\n        print()\n\n    elif item_type == "tool_result":\n        tr = item["tool_result"]\n        print(f"=== TOOL RESULT: {tr.get('name', '')} ===")\n        # Extract the JSON content from the tool result\n        content_json = tr.get("content", [{}])[0].get("json", {})\n        # Show the generated SQL if present (Analyst tool)\n        if content_json.get("sql"):\n            print(f"Generated SQL:\\n{content_json['sql']}\\n")\n        if content_json.get("sql_explanation"):\n            print(f"Explanation: {content_json['sql_explanation']}\\n")\n        # Show the result data if present\n        if content_json.get("result_set", {}).get("data"):\n            meta = content_json["result_set"].get("resultSetMetaData", {})\n            cols = [col["name"] for col in meta.get("rowType", [])]\n            data = content_json["result_set"]["data"]\n            if cols:\n                print(" | ".join(cols))\n                print("-" * (len(" | ".join(cols))))\n            for row in data:\n                print(" | ".join(str(v) for v in row))\n            print()\n\n    elif item_type == "text":\n        print("=== ANSWER ===")\n        print(item["text"])\n        print()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWhat to expect in the output:\u003C/strong\u003E\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\u003Cstrong\u003ETHINKING\u003C/strong\u003E: the agent recognizes this is a sales metrics question and chooses the Analyst tool\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ETOOL CALL\u003C/strong\u003E: shows the agent called the "Analyst" tool\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ETOOL RESULT\u003C/strong\u003E: displays the SQL query the Analyst generated (e.g., \u003Ccode\u003ESELECT region, SUM(total_amount) FROM sales GROUP BY region\u003C/code\u003E) and the actual data rows\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EANSWER\u003C/strong\u003E: a natural language summary like "Total sales by region: North America had the highest at $69,497..."\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003ETest with a Documentation Question\u003C/h3\u003E\n","\u003Cp\u003ENow ask about product documentation, which should route to the \u003Cstrong\u003ESearch\u003C/strong\u003E tool:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E# Ask an unstructured data question (routes to Cortex Search)\nresult = session.sql("""\n  SELECT SNOWFLAKE.CORTEX.DATA_AGENT_RUN(\n    'CORTEX_AGENTS_LAB.TUTORIAL.TUTORIAL_AGENT',\n    $${"messages": [{"role": "user", "content": [{"type": "text", "text": "How do I fix laptop overheating?"}]}]}$$\n  ) AS resp\n""").collect()\n\nresp = json.loads(result[0]["RESP"])\n\nfor item in resp.get("content", []):\n    item_type = item.get("type")\n\n    if item_type == "thinking":\n        print("=== THINKING ===")\n        print(item["thinking"]["text"])\n        print()\n\n    elif item_type == "tool_use":\n        print(f"=== TOOL CALL: {item['tool_use'].get('name', '')} ({item['tool_use'].get('type', '')}) ===")\n        print()\n\n    elif item_type == "tool_result":\n        tr = item["tool_result"]\n        print(f"=== TOOL RESULT: {tr.get('name', '')} (status: {tr.get('status', '')}) ===")\n        print()\n\n    elif item_type == "text":\n        print("=== ANSWER ===")\n        print(item["text"])\n        print()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWhat to expect:\u003C/strong\u003E This time the agent recognizes this is a documentation question and routes to the Search tool instead of Analyst. The THINKING section will show the agent reasoning about this differently, and the ANSWER will contain troubleshooting steps retrieved from the product documentation you loaded earlier.\u003C/p\u003E\n","\u003Ch3\u003EWhat Just Happened?\u003C/h3\u003E\n","\u003Cp\u003EWith the same agent and no routing code on your part:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003E"What are total sales by region?"\u003C/strong\u003E triggered SQL generation, query execution, and data summarization\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003E"How do I fix laptop overheating?"\u003C/strong\u003E triggered semantic search, document retrieval, and answer synthesis\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EThe agent handled the routing, tool execution, and response formatting automatically based on the instructions you provided in the YAML spec.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EAsk Questions with Snowflake CoWork\u003C/h2\u003E\n","\u003Cp\u003ESo far you've tested the agent programmatically with Python. But you don't always need to write code to use an agent. \u003Cstrong\u003ESnowflake CoWork\u003C/strong\u003E provides a chat interface where you can talk to any agent created with \u003Ccode\u003ECREATE AGENT\u003C/code\u003E, with no additional setup required.\u003C/p\u003E\n","\u003Cp\u003EThis is especially useful for non-technical users (business analysts, product managers, support teams) who want to ask questions without writing SQL or Python.\u003C/p\u003E\n","\u003Ch3\u003EOpen Snowflake CoWork\u003C/h3\u003E\n","\u003Cp\u003EFollow these steps to connect to your agent:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EIn Snowsight, navigate to \u003Cstrong\u003EAI & ML > Snowflake CoWork\u003C/strong\u003E in the left sidebar\u003C/li\u003E\u003Cli\u003EIn the chat bar at the bottom, click the \u003Cstrong\u003Eagent picker\u003C/strong\u003E (it may say "General purpose" by default)\u003C/li\u003E\u003Cli\u003ESelect \u003Cstrong\u003ETUTORIAL_AGENT\u003C/strong\u003E from the list\u003C/li\u003E\u003Cli\u003EType a question and press Enter\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003ETry These Questions\u003C/h3\u003E\n","\u003Cp\u003ETest both tools to see the agent route questions automatically:\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EStructured data questions\u003C/strong\u003E (routes to Cortex Analyst):\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E"What are total sales by region?"\u003C/li\u003E\u003Cli\u003E"Which product has the highest revenue?"\u003C/li\u003E\u003Cli\u003E"How many units did we sell in Europe?"\u003C/li\u003E\u003Cli\u003E"Show me sales broken down by customer segment"\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EDocumentation questions\u003C/strong\u003E (routes to Cortex Search):\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E"How do I assemble the standing desk?"\u003C/li\u003E\u003Cli\u003E"What are the specs for the Office Chair?"\u003C/li\u003E\u003Cli\u003E"How do I fix laptop overheating?"\u003C/li\u003E\u003Cli\u003E"What are the care instructions for the office chair?"\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003ESnowflake CoWork can present the agent's responses as tables or charts depending on the question:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-a-cortex-agent-from-scratch-with-snowflake/si-1.png?v=c44e93c1\" alt=\"Snowflake CoWork response with tabular data\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-a-cortex-agent-from-scratch-with-snowflake/si-2.png?v=c44e93c1\" alt=\"Snowflake CoWork response with a chart\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETry a follow-up question\u003C/strong\u003E to test multi-turn conversation:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EFirst ask: "What are total sales by region?"\u003C/li\u003E\u003Cli\u003EThen follow up: "Which region had the lowest?" The agent remembers the previous context\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003EWhat Snowflake CoWork Provides\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EStreaming responses\u003C/strong\u003E: watch the agent think and call tools in real time, so you can see its reasoning process\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EMulti-turn conversations\u003C/strong\u003E: ask follow-up questions that build on previous context without re-stating the original question\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ETool call visibility\u003C/strong\u003E: expand the tool call section to see which tool was chosen, the generated SQL, or the search results\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EShareable\u003C/strong\u003E: other users with access to the agent can use the same interface\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EThis is the fastest way to interact with any agent built with \u003Ccode\u003ECREATE AGENT\u003C/code\u003E, and it's what most end users will use in practice.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EAsk Questions with Snowflake CoCo\u003C/h2\u003E\n","\u003Cp\u003EYou can also interact with the objects you built in this guide directly from a Snowflake Notebook using \u003Cstrong\u003ESnowflake CoCo\u003C/strong\u003E, an AI coding assistant built into Snowsight (also available as \u003Ca href=\"https://docs.snowflake.com/en/user-guide/cortex-code/cortex-code\"\u003ESnowflake CoCo CLI\u003C/a\u003E for terminal-based workflows).\u003C/p\u003E\n","\u003Cp\u003EBecause the agent, semantic view, search service, and tables all persist as first-class objects in your Snowflake account, Snowflake CoCo is context-aware and can discover and use them to answer questions without any extra configuration.\u003C/p\u003E\n","\u003Ch3\u003EOpen Snowflake CoCo\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003EOpen any Snowflake Notebook (or the companion notebook from this guide)\u003C/li\u003E\u003Cli\u003EClick the \u003Cstrong\u003ESnowflake CoCo toggle\u003C/strong\u003E in the bottom-right corner of the notebook\u003C/li\u003E\u003Cli\u003EType a question in the chat panel\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-a-cortex-agent-from-scratch-with-snowflake/cortex-code.png?v=c44e93c1\" alt=\"Snowflake CoCo in a Snowflake Notebook\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EProvide Context in Your Questions\u003C/h3\u003E\n","\u003Cp\u003ESnowflake CoCo works best when your question includes enough context for it to identify the right objects. Mention the relevant table name, semantic view, or agent so Snowflake CoCo can route your request accurately.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EExamples with context:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E"Using the \u003Ccode\u003Etutorial_agent\u003C/code\u003E agent, what are total sales by region?"\u003C/li\u003E\u003Cli\u003E"Query the \u003Ccode\u003Esales_semantic_view\u003C/code\u003E semantic view to show revenue by product category"\u003C/li\u003E\u003Cli\u003E"What product documentation is indexed in the \u003Ccode\u003Eproduct_search_service\u003C/code\u003E search service?"\u003C/li\u003E\u003Cli\u003E"What are the top-selling products in the \u003Ccode\u003ECORTEX_AGENTS_LAB.TUTORIAL.SALES\u003C/code\u003E table?"\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhy This Works\u003C/h3\u003E\n","\u003Cp\u003EWhen you created the agent, semantic view, and search service earlier in this guide, those objects became persistent resources in your Snowflake account. Snowflake CoCo can detect these objects and use them to answer your questions, just like Snowflake CoWork does. The difference is that Snowflake CoCo lives inside the notebook environment, so you can ask questions and get answers without leaving your development workflow.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion And Resources\u003C/h2\u003E\n","\u003Cp\u003EYou've built a fully functional Cortex Agent from scratch. Starting from raw data, you created the infrastructure (semantic view, search service) and the orchestration layer (agent) that ties it all together.\u003C/p\u003E\n","\u003Ch3\u003EWhat You Learned\u003C/h3\u003E\n","\u003Cp\u003EHere's a recap of the key concepts and skills you picked up:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ESemantic views\u003C/strong\u003E define the business meaning of your data (dimensions, metrics, comments) so that an LLM can translate natural language questions into accurate SQL\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ECortex Search services\u003C/strong\u003E index unstructured text for semantic search, enabling retrieval-augmented generation (RAG) where answers are grounded in your actual documents\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ECortex Agents\u003C/strong\u003E orchestrate across multiple tools automatically. You write the instructions, and the agent handles routing, execution, and response synthesis\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003E\u003Ccode\u003ECREATE AGENT ... FROM SPECIFICATION\u003C/code\u003E\u003C/strong\u003E lets you define an entire agent (model, instructions, tools, resources) in a single SQL statement with embedded YAML\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003E\u003Ccode\u003EDATA_AGENT_RUN\u003C/code\u003E\u003C/strong\u003E lets you call the agent programmatically and parse the full response (thinking, tool calls, results, answer) with Python\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESnowflake CoWork\u003C/strong\u003E provides a no-code chat interface for any agent, making it accessible to non-technical users\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You Built\u003C/h3\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EComponent\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EWhat It Does\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EWhy It Matters\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003ESales table\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EStores 12 structured transactions\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EProvides the data Cortex Analyst queries\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EProduct docs table\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EStores 6 unstructured documents\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EProvides the text Cortex Search indexes\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003ESemantic View\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EMaps business terms to SQL expressions\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EEnables accurate natural language to SQL\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003ESearch Service\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EIndexes documents for semantic search\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EEnables RAG over product documentation\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003ECortex Agent\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EOrchestrates Analyst + Search tools\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ERoutes questions to the right tool automatically\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EPython parser\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EExtracts thinking, SQL, results, answers\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EGives full visibility into agent behavior\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Ch3\u003ENext Steps\u003C/h3\u003E\n","\u003Cp\u003ENow that you have a working agent, here are ways to extend it:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EAdd custom tools\u003C/strong\u003E: create stored procedures for business logic (e.g., inventory lookups, price calculations) and register them as generic tools in the agent spec\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAdd web search\u003C/strong\u003E: enable the agent to search the web for real-time external information\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EBuild a Streamlit app\u003C/strong\u003E: use \u003Ccode\u003EDATA_AGENT_RUN\u003C/code\u003E as the backend for a custom chat UI built with Streamlit\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EConfigure access control\u003C/strong\u003E: use Snowflake's role-based access control (RBAC) to manage who can use the agent and what data they can access through it\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ECleanup\u003C/h3\u003E\n","\u003Cp\u003ETo remove all objects created in this guide, run the following. The commented-out lines at the bottom drop the entire database and schema; uncomment them only if you want to remove everything.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Drop the agent and its tools\nDROP AGENT IF EXISTS tutorial_agent;\nDROP CORTEX SEARCH SERVICE IF EXISTS product_search_service;\nDROP SEMANTIC VIEW IF EXISTS sales_semantic_view;\n\n-- Drop the data tables\nDROP TABLE IF EXISTS sales;\nDROP TABLE IF EXISTS inventory;\nDROP TABLE IF EXISTS product_docs;\n\n-- Uncomment these to remove the database and schema entirely:\n-- DROP SCHEMA IF EXISTS TUTORIAL;\n-- DROP DATABASE IF EXISTS CORTEX_AGENTS_LAB;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ERelated Resources\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents\"\u003ECortex Agents\u003C/a\u003E: concepts and architecture of Cortex Agents\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/sql-reference/sql/create-agent\"\u003ECREATE AGENT\u003C/a\u003E: create Cortex Agents\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/sql-reference/sql/create-semantic-view\"\u003ECREATE SEMANTIC VIEW\u003C/a\u003E: define semantic views\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-search/cortex-search-overview\"\u003ECortex Search\u003C/a\u003E: build Cortex Search services\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/user-guide/snowflake-cortex/snowflake-intelligence\"\u003EOverview of Snowflake CoWork\u003C/a\u003E: chat interface for agents\u003C/li\u003E\u003C/ul\u003E"],"description":"","title":"Build a Cortex Agent from Scratch with Snowflake","isDeveloperGuidesPage":false,":type":"snowflake-site/components/contentfragment",":items":{},":itemsOrder":[],"elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\u003C!-- ------------------------ --\u003E\n## Overview\n\nEvery organization sits on two kinds of data: **structured data** (numbers in tables like sales figures, inventory counts, and transaction logs) and **unstructured data** (text in documents like product manuals, troubleshooting guides, and policy documents). Traditionally, getting answers from these two worlds required completely different tools and skills. Want to know last quarter's revenue? Write a SQL query. Need to find the assembly instructions for a product? Search through a document repository. Want both in one conversation? Good luck stitching those workflows together manually.\n\nThis is the problem **AI agents** solve. An AI agent doesn't just generate text like a basic LLM call. It *reasons* about your question, *decides* which tool to use, *executes* that tool, and *synthesizes* the results into a coherent answer. Ask it \"What are total sales by region?\" and it routes to a SQL engine. Ask it \"How do I fix laptop overheating?\" and it searches your documentation. Ask both in the same conversation, and it handles each seamlessly.\n\n**Cortex Agents** bring this capability directly into Snowflake. You don't need to set up external orchestration frameworks, manage API keys for third-party services, or write complex routing logic. Everything (the agent, its tools, and the data it accesses) lives inside your Snowflake account, governed by the same roles and permissions you already use.\n\nIn this guide, you'll build an end-to-end pipeline from scratch, starting with the data. You'll create and load sample data into tables, create a **semantic view** that lets the agent translate natural language into SQL, build a **Cortex Search service** that lets it retrieve relevant documentation, and wire both into a **Cortex Agent** that answers questions about sales data and product documentation, all with standard SQL.\n\n### What You'll Learn\n- What Cortex Agents are and how they orchestrate across structured and unstructured data\n- What semantic views are and how they enable natural language to SQL translation\n- What Cortex Search services are and how they power retrieval-augmented generation (RAG)\n- How to create an agent with full tool configuration using `CREATE AGENT ... FROM SPECIFICATION`\n- How to test an agent with `DATA_AGENT_RUN` and parse its responses with Python\n- How to interact with an agent through Snowflake CoWork's chat interface\n\n### What You'll Build\nA Cortex Agent with two tools:\n- **Cortex Analyst**: takes natural language questions like \"What are total sales by region?\" and automatically converts them into SQL queries that run against your sales data\n- **Cortex Search**: takes questions like \"How do I assemble the standing desk?\" and retrieves the most relevant product documentation using semantic search\n\nThe agent automatically decides which tool to use based on what the user asks. You don't write any routing logic; the agent figures it out.\n\n\u003C!-- Workflow diagram (editable): https://excalidraw.com/#json=MioFuiqlV9qvS_486Ezdl,ui60jdFQ8rz79OBXlQABcg --\u003E\n![Cortex Agent workflow diagram](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-a-cortex-agent-from-scratch-with-snowflake/diagram.png?v=c44e93c1)\n\n### Prerequisites\n- A [Snowflake account](https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides) (if you don't have one, you can sign up for a free trial)\n- `ACCOUNTADMIN` role (or a role with `CREATE AGENT`, `CREATE SEMANTIC VIEW`, and `CREATE CORTEX SEARCH SERVICE` privileges)\n- A running warehouse (this guide uses `COMPUTE_WH`, but any warehouse will work)\n- Cortex AI enabled on your account (available in most Snowflake regions)\n\n\u003C!-- ------------------------ --\u003E\n## Setup Environment\n\nBefore building anything, you need a workspace in Snowflake to hold all the objects you'll create (tables, semantic views, search services, and the agent itself). Think of a **database** as a top-level folder and a **schema** as a subfolder within it.\n\nYou'll also verify that Cortex AI is available on your account, since the agent depends on it.\n\n### Where to Run SQL\n\nYou can run all the SQL in this guide in either:\n- **SQL Worksheet**: In Snowsight, click **+ \u003E SQL Worksheet** in the top left\n- **Snowflake Notebook**: In Snowsight, click **+ \u003E Notebook** (useful if you want to mix SQL and Python cells, since you'll need Python for the testing section later)\n\n\u003E **Want everything in one notebook?** Download the [companion notebook](https://github.com/Snowflake-Labs/snowflake-demo-notebooks/blob/main/Build-a-Cortex-Agent-from-Scratch-with-Snowflake/build-a-cortex-agent-from-scratch-with-snowflake.ipynb) and import it into Snowsight (**+ \u003E Notebook \u003E Import .ipynb file**).\n\n### Set Up Database and Schema\n\nCopy and paste the following SQL and run it. Each line is explained below:\n\n```sql\n-- Use the ACCOUNTADMIN role, which has full privileges\nUSE ROLE ACCOUNTADMIN;\n\n-- Select a warehouse (compute resource) to run queries\nUSE WAREHOUSE COMPUTE_WH;\n\n-- Create a new database to hold all tutorial objects\nCREATE DATABASE IF NOT EXISTS CORTEX_AGENTS_LAB;\nUSE DATABASE CORTEX_AGENTS_LAB;\n\n-- Create a schema (subfolder) inside the database\nCREATE SCHEMA IF NOT EXISTS TUTORIAL;\nUSE SCHEMA TUTORIAL;\n```\n\n**What each command does:**\n- `USE ROLE ACCOUNTADMIN` sets your active role. `ACCOUNTADMIN` has all privileges, which simplifies this tutorial. In production, you'd use a more restricted role.\n- `USE WAREHOUSE COMPUTE_WH` selects which compute resource runs your queries. If your warehouse has a different name, replace `COMPUTE_WH` with your warehouse name.\n- `CREATE DATABASE` / `CREATE SCHEMA` creates the containers for all the objects you'll build. `IF NOT EXISTS` means it won't error if they already exist.\n\n### Verify Cortex Access\n\nBefore going further, confirm that Cortex AI is working on your account. Run this simple test that asks an LLM to respond:\n\n```sql\nSELECT SNOWFLAKE.CORTEX.COMPLETE('claude-3-5-sonnet', 'Say hello in one word');\n```\n\n**What this does:** `SNOWFLAKE.CORTEX.COMPLETE()` is a built-in function that sends a prompt to an LLM and returns the response. Here, we're using `claude-3-5-sonnet` as the model. If it returns something like \"Hello\", you're all set.\n\n**If you get an error:** Cortex AI may not be enabled in your account's region. Check the [Cortex AI availability documentation](https://docs.snowflake.com/en/user-guide/snowflake-cortex/llm-functions#availability) for supported regions.\n\n\u003C!-- ------------------------ --\u003E\n## Create Sample Data\n\nNow you'll create the data that your agent will work with. You need two types:\n\n1. **Structured data** (a sales table with numbers and categories) that Cortex Analyst will query with SQL\n2. **Unstructured data** (product documentation as free-form text) that Cortex Search will index and retrieve\n\nYou'll also create an inventory table that's useful for exploring the data, though the agent in this tutorial focuses on sales and documentation.\n\n### Create the Sales Table\n\nThis table represents transaction-level sales data for a retail business. Each row is one sale, with information about what was sold, where, for how much, and to what type of customer.\n\n```sql\nCREATE OR REPLACE TABLE sales (\n    sale_id NUMBER AUTOINCREMENT,\n    sale_date DATE,\n    product_name VARCHAR,\n    category VARCHAR,\n    region VARCHAR,\n    quantity NUMBER,\n    unit_price NUMBER(10,2),\n    total_amount NUMBER(10,2),\n    customer_segment VARCHAR\n);\n\nINSERT INTO sales (sale_date, product_name, category, region, quantity, unit_price, total_amount, customer_segment)\nVALUES\n    ('2024-01-15', 'Laptop Pro', 'Electronics', 'North America', 10, 1299.99, 12999.90, 'Enterprise'),\n    ('2024-01-16', 'Wireless Mouse', 'Electronics', 'Europe', 50, 29.99, 1499.50, 'SMB'),\n    ('2024-01-17', 'Office Chair', 'Furniture', 'North America', 20, 299.99, 5999.80, 'Enterprise'),\n    ('2024-01-18', 'Standing Desk', 'Furniture', 'Asia Pacific', 15, 499.99, 7499.85, 'SMB'),\n    ('2024-01-19', 'Monitor 27\"', 'Electronics', 'Europe', 30, 399.99, 11999.70, 'Enterprise'),\n    ('2024-01-20', 'Keyboard Pro', 'Electronics', 'North America', 100, 149.99, 14999.00, 'Consumer'),\n    ('2024-02-01', 'Laptop Pro', 'Electronics', 'Asia Pacific', 25, 1299.99, 32499.75, 'Enterprise'),\n    ('2024-02-05', 'Webcam HD', 'Electronics', 'North America', 75, 79.99, 5999.25, 'SMB'),\n    ('2024-02-10', 'Office Chair', 'Furniture', 'Europe', 40, 299.99, 11999.60, 'Enterprise'),\n    ('2024-02-15', 'Headphones', 'Electronics', 'North America', 60, 199.99, 11999.40, 'Consumer'),\n    ('2024-03-01', 'Standing Desk', 'Furniture', 'North America', 35, 499.99, 17499.65, 'Enterprise'),\n    ('2024-03-10', 'Laptop Pro', 'Electronics', 'Europe', 20, 1299.99, 25999.80, 'SMB');\n```\n\n**What this creates:** 12 sales transactions across 3 regions (North America, Europe, Asia Pacific), 2 categories (Electronics, Furniture), and 3 customer segments (Enterprise, SMB, Consumer). The `AUTOINCREMENT` on `sale_id` means Snowflake automatically assigns an incrementing ID to each row.\n\n**Quick check:** Preview the data to see what you loaded:\n\n```sql\nSELECT * FROM sales ORDER BY sale_date LIMIT 5;\n```\n\n### Create the Inventory Table\n\nThis table tracks stock levels for each product. It's not directly used by the agent in this tutorial, but it's included so you can explore the data and potentially extend the agent later.\n\n```sql\nCREATE OR REPLACE TABLE inventory (\n    product_name VARCHAR,\n    sku VARCHAR,\n    quantity_in_stock NUMBER,\n    reorder_level NUMBER,\n    unit_cost NUMBER(10,2),\n    last_restocked DATE\n);\n\nINSERT INTO inventory VALUES\n    ('Laptop Pro', 'LP-001', 45, 20, 899.99, '2024-03-01'),\n    ('Wireless Mouse', 'WM-002', 500, 100, 12.99, '2024-02-15'),\n    ('Office Chair', 'OC-003', 75, 25, 149.99, '2024-02-20'),\n    ('Standing Desk', 'SD-004', 30, 15, 299.99, '2024-03-05'),\n    ('Monitor 27\"', 'MN-005', 60, 20, 249.99, '2024-02-28'),\n    ('Keyboard Pro', 'KP-006', 200, 50, 79.99, '2024-03-10');\n```\n\n### Create Product Documentation\n\nThis is the **unstructured data** that Cortex Search will index. Each row contains a text document about a product, covering things like specifications, troubleshooting guides, assembly instructions, and care tips.\n\nUnlike the sales table (which has clean numeric columns you can aggregate), this data is free-form text that requires semantic search to be useful.\n\n```sql\nCREATE OR REPLACE TABLE product_docs (\n    doc_id NUMBER AUTOINCREMENT,\n    product_name VARCHAR,\n    doc_type VARCHAR,\n    content VARCHAR,\n    last_updated DATE\n);\n\nINSERT INTO product_docs (product_name, doc_type, content, last_updated)\nVALUES\n    ('Laptop Pro', 'specifications', \n     'The Laptop Pro features a 15.6-inch 4K display, Intel i9 processor, 32GB RAM, and 1TB SSD. Battery life is up to 12 hours. Includes Thunderbolt 4 ports and Wi-Fi 6E. Weight: 4.2 lbs. Warranty: 3 years standard, extendable to 5 years.',\n     '2024-01-01'),\n    ('Laptop Pro', 'troubleshooting',\n     'Common issues: 1) Battery drain - check background apps and reduce screen brightness. 2) Overheating - ensure vents are not blocked, use on hard surface. 3) Slow performance - check for updates, run disk cleanup. 4) Wi-Fi issues - update network drivers, reset network settings.',\n     '2024-01-15'),\n    ('Standing Desk', 'specifications',\n     'Electric standing desk with memory presets. Height range: 28-48 inches. Desktop size: 60x30 inches. Weight capacity: 300 lbs. Motor: dual motor system for stability. Includes cable management tray and anti-collision sensor.',\n     '2024-01-01'),\n    ('Standing Desk', 'assembly',\n     'Assembly instructions: 1) Attach legs to frame using provided bolts. 2) Connect motor cables to control box. 3) Mount desktop to frame. 4) Connect power cord. 5) Program height presets using control panel. Assembly time: approximately 45 minutes. Tools needed: Phillips screwdriver.',\n     '2024-01-10'),\n    ('Office Chair', 'specifications',\n     'Ergonomic office chair with lumbar support. Adjustable armrests, seat height, and tilt tension. Breathable mesh back. Seat dimensions: 20x19 inches. Weight capacity: 275 lbs. Warranty: 5 years on frame, 2 years on upholstery.',\n     '2024-01-01'),\n    ('Office Chair', 'care',\n     'Care instructions: Clean mesh with mild soap and water. Lubricate casters annually. Check and tighten bolts every 6 months. Do not exceed weight capacity. Store in dry environment. Replace gas cylinder if chair sinks.',\n     '2024-02-01');\n```\n\n**What this creates:** 6 documents covering 3 products. Notice the `content` column: it contains plain English text, not structured data. This is exactly the kind of data that's hard to query with SQL (you can't `SUM` or `GROUP BY` a paragraph of text) but perfect for semantic search.\n\n### Verify All Data\n\nRun this query to confirm all three tables loaded correctly:\n\n```sql\nSELECT 'sales' as table_name, COUNT(*) as row_count FROM sales\nUNION ALL SELECT 'inventory', COUNT(*) FROM inventory\nUNION ALL SELECT 'product_docs', COUNT(*) FROM product_docs;\n```\n\n**Expected results:** 12 sales rows, 6 inventory rows, and 6 product docs rows. If any count is off, re-run the `INSERT` statements for that table.\n\n\u003C!-- ------------------------ --\u003E\n## Create Semantic View\n\nNow you'll create the first tool the agent will use: **Cortex Analyst**, which converts natural language questions into SQL queries.\n\nBut there's a challenge: how does an LLM know that \"revenue\" means `SUM(total_amount)` or that \"region\" refers to the `region` column? It doesn't, unless you tell it. That's what a **semantic view** does.\n\nA semantic view is a layer on top of your table that defines the **business meaning** of your data:\n- **Dimensions**: the categorical columns you group by (product name, region, customer segment)\n- **Metrics**: the calculations users care about (total sales, units sold, transaction count)\n- **Comments**: plain English descriptions that help the LLM understand each field\n\nThink of it as a data dictionary that the LLM reads to write correct SQL.\n\n### Create the Semantic View\n\nSemantic views are defined with SQL using `TABLES`, `DIMENSIONS`, and `METRICS` clauses. Run the following:\n\n```sql\nCREATE OR REPLACE SEMANTIC VIEW sales_semantic_view\n  TABLES (\n    sales AS CORTEX_AGENTS_LAB.TUTORIAL.SALES\n      PRIMARY KEY (sale_id)\n      COMMENT = 'Transaction-level sales data'\n  )\n  DIMENSIONS (\n    sales.product_name AS product_name\n      COMMENT = 'Name of the product sold',\n    sales.category AS category\n      COMMENT = 'Product category (Electronics, Furniture)',\n    sales.region AS region\n      COMMENT = 'Sales region (North America, Europe, Asia Pacific)',\n    sales.customer_segment AS customer_segment\n      COMMENT = 'Customer type (Enterprise, SMB, Consumer)'\n  )\n  METRICS (\n    sales.total_sales AS SUM(total_amount)\n      COMMENT = 'Total sales amount in USD',\n    sales.total_quantity AS SUM(quantity)\n      COMMENT = 'Total units sold',\n    sales.transaction_count AS COUNT(*)\n      COMMENT = 'Number of sales transactions'\n  )\n  COMMENT = 'Sales data for analyzing revenue, products, and regional performance';\n```\n\n**Breaking this down:**\n- **`TABLES`** tells the semantic view which physical table to query. The `AS CORTEX_AGENTS_LAB.TUTORIAL.SALES` part uses the fully qualified table name (database.schema.table).\n- **`DIMENSIONS`** lists the columns that users can filter or group by. Each one has a `COMMENT` that the LLM reads to understand what it represents.\n- **`METRICS`** defines named calculations. When a user asks about \"total sales,\" the LLM knows to use `SUM(total_amount)`. This is where you encode your business logic.\n- **`COMMENT`** at the view level describes the overall purpose. This helps the agent decide whether to route a question to this tool.\n\n### Verify the Semantic View\n\nConfirm the semantic view was created:\n\n```sql\nSHOW SEMANTIC VIEWS;\n```\n\nYou should see `SALES_SEMANTIC_VIEW` in the results.\n\n### Test the Semantic View\n\nBefore wiring it into the agent, query the semantic view directly to make sure it works. This is a good debugging practice: if the semantic view doesn't return correct data, the agent won't either.\n\n```sql\nSELECT * FROM SEMANTIC_VIEW(\n    CORTEX_AGENTS_LAB.TUTORIAL.SALES_SEMANTIC_VIEW\n    METRICS total_sales, total_quantity\n    DIMENSIONS region\n)\nORDER BY total_sales DESC;\n```\n\n**What this does:** Uses the `SEMANTIC_VIEW()` function to query the view just like a table, but using the business names you defined (e.g., `total_sales` instead of `SUM(total_amount)`). You should see sales totals broken down by region.\n\n### Quick Cortex Analyst Demo\n\nTo see the natural-language-to-SQL translation in action, try asking an LLM to write a query based on the schema:\n\n```sql\nSELECT SNOWFLAKE.CORTEX.COMPLETE(\n    'claude-4-sonnet',\n    'Given a sales table with columns: product_name, category, region, quantity, unit_price, total_amount, customer_segment - write a SQL query to find total sales by region. Return ONLY the SQL.'\n);\n```\n\n**Why this matters:** This is essentially what Cortex Analyst does under the hood. It takes your natural language question, reads the semantic view's definitions, and generates a SQL query. The semantic view just makes it far more accurate by giving the LLM explicit business context instead of making it guess.\n\n\u003C!-- ------------------------ --\u003E\n## Create Search Service\n\nNow you'll create the second tool: **Cortex Search**, which enables the agent to find relevant product documentation using semantic search.\n\n### What is Semantic Search?\n\nTraditional SQL search requires exact matches. `WHERE content LIKE '%overheating%'` only finds documents containing that exact word. But what if a user asks \"my laptop is getting too hot\"? A `LIKE` query would miss the troubleshooting document because it uses the word \"overheating,\" not \"too hot.\"\n\n**Semantic search** understands the *meaning* behind words. It converts text into numerical vectors (embeddings) and finds documents that are conceptually similar to the query, even if they use different words.\n\n### What is RAG?\n\n**Retrieval-Augmented Generation (RAG)** is the pattern where you:\n1. **Retrieve** relevant documents using search\n2. **Augment** an LLM prompt with those documents as context\n3. **Generate** an answer grounded in the retrieved information\n\nCortex Search handles the retrieval step. The agent handles the augmentation and generation steps automatically.\n\n### Create the Cortex Search Service\n\nRun the following to create a search service that indexes your product documentation:\n\n```sql\nCREATE OR REPLACE CORTEX SEARCH SERVICE product_search_service\n  ON content\n  ATTRIBUTES product_name, doc_type\n  WAREHOUSE = COMPUTE_WH\n  TARGET_LAG = '1 hour'\n  AS (\n    SELECT \n      doc_id,\n      product_name,\n      doc_type,\n      content\n    FROM product_docs\n  );\n```\n\n**Breaking this down:**\n- **`ON content`** tells Cortex Search which column contains the text to index for semantic search. This is the column users will be searching against.\n- **`ATTRIBUTES product_name, doc_type`** are additional columns that can be returned alongside search results and used for filtering (e.g., \"only show results for Laptop Pro\").\n- **`WAREHOUSE = COMPUTE_WH`** is the compute resource used to build and refresh the search index.\n- **`TARGET_LAG = '1 hour'`** controls how frequently the index refreshes when the underlying data changes. For this tutorial, 1 hour is fine. In production, you might set this shorter for frequently updated data.\n- **`AS (SELECT ...)`** is the source query that feeds data into the index.\n\n### Verify the Search Service\n\nConfirm the service was created:\n\n```sql\nSHOW CORTEX SEARCH SERVICES;\n```\n\nYou should see `PRODUCT_SEARCH_SERVICE` in the results. Note: the service takes a moment to build the initial index.\n\n### Test the Search Service\n\nNow test it by searching for troubleshooting information about laptop overheating. This uses the `SEARCH_PREVIEW` function, which returns results as JSON:\n\n```sql\nSELECT \n  r.value:product_name::STRING AS product_name,\n  r.value:content::STRING AS content\nFROM TABLE(FLATTEN(\n  PARSE_JSON(\n    SNOWFLAKE.CORTEX.SEARCH_PREVIEW(\n      'CORTEX_AGENTS_LAB.TUTORIAL.PRODUCT_SEARCH_SERVICE',\n      '{\"query\": \"laptop overheating fix\", \"columns\": [\"product_name\", \"content\"], \"limit\": 3}'\n    )\n  )['results']\n)) r;\n```\n\n**What's happening here (inside out):**\n1. `SEARCH_PREVIEW()` sends the query \"laptop overheating fix\" to the search service and returns matching documents as a JSON string\n2. `PARSE_JSON()` converts the JSON string into a Snowflake VARIANT object\n3. `['results']` extracts the results array from the JSON\n4. `TABLE(FLATTEN(...))` expands the JSON array into rows (one row per search result)\n5. `r.value:product_name::STRING` extracts specific fields from each result\n\nYou should see the Laptop Pro troubleshooting document returned, even though the search query (\"laptop overheating fix\") doesn't exactly match the document text.\n\n### Quick RAG Demo\n\nTo see the full RAG pattern in action, take the search results and feed them into an LLM to generate an answer:\n\n```sql\nSELECT SNOWFLAKE.CORTEX.COMPLETE(\n    'claude-4-sonnet',\n    'Based on this documentation, how do I fix laptop overheating? Documentation: ' ||\n    (SELECT LISTAGG(r.value:content::STRING, ' | ') \n     FROM TABLE(FLATTEN(\n       PARSE_JSON(\n         SNOWFLAKE.CORTEX.SEARCH_PREVIEW(\n           'CORTEX_AGENTS_LAB.TUTORIAL.PRODUCT_SEARCH_SERVICE',\n           '{\"query\": \"laptop overheating\", \"columns\": [\"content\"], \"limit\": 2}'\n         )\n       )['results']\n     )) r)\n);\n```\n\n**What this does:** Retrieves the top 2 documents matching \"laptop overheating,\" concatenates their content together using `LISTAGG`, then passes that as context to the LLM along with the question. The LLM's answer is grounded in your actual product documentation rather than its general training data.\n\nThis is exactly the pattern the Cortex Agent automates. It decides when to search, what to search for, and how to present the results, all without you having to write this query manually each time.\n\n\u003C!-- ------------------------ --\u003E\n## Build the Agent\n\nYou've now built both tools independently:\n- A **semantic view** that Cortex Analyst uses to translate questions into SQL\n- A **search service** that Cortex Search uses to find relevant documents\n\nNow you'll create a **Cortex Agent** that sits on top of both tools and automatically routes each question to the right one. The agent is the orchestration layer that reads the user's question, decides which tool to call, executes it, and synthesizes a final answer.\n\n### How the Agent Works\n\nWhen you ask the agent a question, here's what happens behind the scenes:\n\n1. **Thinking**: the agent's LLM reads your question and decides which tool is most appropriate\n2. **Tool call**: the agent invokes the chosen tool (Analyst for SQL queries, Search for document retrieval)\n3. **Tool result**: the tool returns its results (SQL + data, or matching documents)\n4. **Answer**: the agent synthesizes the tool's results into a natural language response\n\nAll of this happens in a single call. You don't need to build this orchestration logic yourself.\n\n### Create the Agent\n\nThe `CREATE AGENT ... FROM SPECIFICATION` command defines everything about the agent in a single YAML spec embedded in SQL. Run the following:\n\n```sql\nCREATE OR REPLACE AGENT tutorial_agent\n  COMMENT = 'Tutorial agent that routes questions to Analyst (structured) or Search (unstructured)'\n  FROM SPECIFICATION $$\nmodels:\n  orchestration: claude-4-sonnet\norchestration:\n  budget:\n    seconds: 30\n    tokens: 16000\ninstructions:\n  system: \"You are a helpful assistant for a retail business. You can answer questions about sales data and product documentation.\"\n  orchestration: \"Use Analyst for any question about sales, revenue, quantities, or metrics. Use Search for product documentation, troubleshooting, or how-to questions.\"\n  response: \"Be concise and include relevant numbers or details from the tools.\"\ntools:\n  - tool_spec:\n      type: \"cortex_analyst_text_to_sql\"\n      name: \"Analyst\"\n      description: \"Queries structured sales data by converting natural language to SQL\"\n  - tool_spec:\n      type: \"cortex_search\"\n      name: \"Search\"\n      description: \"Searches product documentation and troubleshooting guides\"\ntool_resources:\n  Analyst:\n    semantic_view: \"CORTEX_AGENTS_LAB.TUTORIAL.SALES_SEMANTIC_VIEW\"\n    execution_environment:\n      type: \"warehouse\"\n      warehouse: \"COMPUTE_WH\"\n  Search:\n    name: \"CORTEX_AGENTS_LAB.TUTORIAL.PRODUCT_SEARCH_SERVICE\"\n    max_results: \"3\"\n$$;\n```\n\n**Breaking down the YAML spec section by section:**\n\n**`models`** specifies which LLM the agent uses for reasoning and tool selection:\n- `orchestration: claude-4-sonnet` is the model that decides which tool to call and synthesizes final answers\n\n**`orchestration`** sets resource limits to prevent runaway queries:\n- `budget.seconds: 30` is the maximum time the agent can spend on a single request\n- `budget.tokens: 16000` is the maximum tokens the LLM can generate\n\n**`instructions`** contains three types of instructions that shape the agent's behavior:\n- `system` defines the agent's persona (\"You are a helpful assistant for a retail business\")\n- `orchestration` tells the agent **when to use each tool**. This is the most important instruction because it's how the agent knows that \"total sales\" should go to Analyst while \"assembly instructions\" should go to Search\n- `response` controls the output format (\"Be concise and include relevant numbers\")\n\n**`tools`** declares what tools are available. Each tool has:\n- `type`: the kind of tool (`cortex_analyst_text_to_sql` or `cortex_search`)\n- `name`: a label used to reference the tool in `tool_resources` and `instructions`\n- `description`: helps the LLM understand what the tool does\n\n**`tool_resources`** connects each tool to its underlying data source:\n- **Analyst** points to the semantic view and specifies an `execution_environment` (the warehouse that will run the generated SQL)\n- **Search** points to the search service and sets `max_results` to limit how many documents are returned\n\n\u003E **Common mistake**: The `execution_environment` block under Analyst is **required**. If you use a bare `warehouse: \"COMPUTE_WH\"` key instead of the nested `execution_environment` block, the agent will fail with error 399504: \"The Analyst tool is missing an execution environment.\"\n\n### Verify the Agent\n\nConfirm the agent was created:\n\n```sql\nSHOW AGENTS;\n```\n\nYou should see `TUTORIAL_AGENT` listed. The agent is now a first-class Snowflake object, just like a table or view.\n\n\u003C!-- ------------------------ --\u003E\n## Test the Agent\n\nNow that the agent exists, you'll test it by asking questions and examining the full response to understand what's happening at each step.\n\n### How to Call the Agent\n\nYou call the agent using the `SNOWFLAKE.CORTEX.DATA_AGENT_RUN()` function. It takes two arguments:\n1. The fully qualified agent name (e.g., `'CORTEX_AGENTS_LAB.TUTORIAL.TUTORIAL_AGENT'`)\n2. A JSON string containing the conversation messages\n\nThe function returns a JSON string with the agent's complete response, including its thinking process, tool calls, and final answer.\n\n### Understanding the Response Format\n\nThe response JSON has a `content` array where each item has a `type` field. Here's what each type means:\n\n| Type | What It Contains | Example |\n|------|-----------------|---------|\n| `thinking` | The agent's reasoning about which tool to use | \"The user is asking about sales metrics, so I should use the Analyst tool\" |\n| `tool_use` | The tool call the agent decided to make | Tool name and parameters |\n| `tool_result` | Raw results from the tool | Generated SQL, query results, or retrieved documents |\n| `text` | The final human-readable answer | \"Total sales by region: North America $69,497...\" |\n\n### Test with a Structured Data Question\n\nThis test asks about sales data, which should route to the **Analyst** tool. Run this in a Snowflake Notebook Python cell:\n\n```python\nimport json\nfrom snowflake.snowpark.context import get_active_session\n\nsession = get_active_session()\n\n# Ask a structured data question (routes to Cortex Analyst)\nresult = session.sql(\"\"\"\n  SELECT SNOWFLAKE.CORTEX.DATA_AGENT_RUN(\n    'CORTEX_AGENTS_LAB.TUTORIAL.TUTORIAL_AGENT',\n    $${\"messages\": [{\"role\": \"user\", \"content\": [{\"type\": \"text\", \"text\": \"What are total sales by region?\"}]}]}$$\n  ) AS resp\n\"\"\").collect()\n\n# Parse the JSON response\nresp = json.loads(result[0][\"RESP\"])\n\n# Walk through each item in the content array\nfor item in resp.get(\"content\", []):\n    item_type = item.get(\"type\")\n\n    if item_type == \"thinking\":\n        print(\"=== THINKING ===\")\n        print(item[\"thinking\"][\"text\"])\n        print()\n\n    elif item_type == \"tool_use\":\n        print(f\"=== TOOL CALL: {item['tool_use'].get('name', '')} ({item['tool_use'].get('type', '')}) ===\")\n        print()\n\n    elif item_type == \"tool_result\":\n        tr = item[\"tool_result\"]\n        print(f\"=== TOOL RESULT: {tr.get('name', '')} ===\")\n        # Extract the JSON content from the tool result\n        content_json = tr.get(\"content\", [{}])[0].get(\"json\", {})\n        # Show the generated SQL if present (Analyst tool)\n        if content_json.get(\"sql\"):\n            print(f\"Generated SQL:\\n{content_json['sql']}\\n\")\n        if content_json.get(\"sql_explanation\"):\n            print(f\"Explanation: {content_json['sql_explanation']}\\n\")\n        # Show the result data if present\n        if content_json.get(\"result_set\", {}).get(\"data\"):\n            meta = content_json[\"result_set\"].get(\"resultSetMetaData\", {})\n            cols = [col[\"name\"] for col in meta.get(\"rowType\", [])]\n            data = content_json[\"result_set\"][\"data\"]\n            if cols:\n                print(\" | \".join(cols))\n                print(\"-\" * (len(\" | \".join(cols))))\n            for row in data:\n                print(\" | \".join(str(v) for v in row))\n            print()\n\n    elif item_type == \"text\":\n        print(\"=== ANSWER ===\")\n        print(item[\"text\"])\n        print()\n```\n\n**What to expect in the output:**\n1. **THINKING**: the agent recognizes this is a sales metrics question and chooses the Analyst tool\n2. **TOOL CALL**: shows the agent called the \"Analyst\" tool\n3. **TOOL RESULT**: displays the SQL query the Analyst generated (e.g., `SELECT region, SUM(total_amount) FROM sales GROUP BY region`) and the actual data rows\n4. **ANSWER**: a natural language summary like \"Total sales by region: North America had the highest at $69,497...\"\n\n### Test with a Documentation Question\n\nNow ask about product documentation, which should route to the **Search** tool:\n\n```python\n# Ask an unstructured data question (routes to Cortex Search)\nresult = session.sql(\"\"\"\n  SELECT SNOWFLAKE.CORTEX.DATA_AGENT_RUN(\n    'CORTEX_AGENTS_LAB.TUTORIAL.TUTORIAL_AGENT',\n    $${\"messages\": [{\"role\": \"user\", \"content\": [{\"type\": \"text\", \"text\": \"How do I fix laptop overheating?\"}]}]}$$\n  ) AS resp\n\"\"\").collect()\n\nresp = json.loads(result[0][\"RESP\"])\n\nfor item in resp.get(\"content\", []):\n    item_type = item.get(\"type\")\n\n    if item_type == \"thinking\":\n        print(\"=== THINKING ===\")\n        print(item[\"thinking\"][\"text\"])\n        print()\n\n    elif item_type == \"tool_use\":\n        print(f\"=== TOOL CALL: {item['tool_use'].get('name', '')} ({item['tool_use'].get('type', '')}) ===\")\n        print()\n\n    elif item_type == \"tool_result\":\n        tr = item[\"tool_result\"]\n        print(f\"=== TOOL RESULT: {tr.get('name', '')} (status: {tr.get('status', '')}) ===\")\n        print()\n\n    elif item_type == \"text\":\n        print(\"=== ANSWER ===\")\n        print(item[\"text\"])\n        print()\n```\n\n**What to expect:** This time the agent recognizes this is a documentation question and routes to the Search tool instead of Analyst. The THINKING section will show the agent reasoning about this differently, and the ANSWER will contain troubleshooting steps retrieved from the product documentation you loaded earlier.\n\n### What Just Happened?\n\nWith the same agent and no routing code on your part:\n- **\"What are total sales by region?\"** triggered SQL generation, query execution, and data summarization\n- **\"How do I fix laptop overheating?\"** triggered semantic search, document retrieval, and answer synthesis\n\nThe agent handled the routing, tool execution, and response formatting automatically based on the instructions you provided in the YAML spec.\n\n\u003C!-- ------------------------ --\u003E\n## Ask Questions with Snowflake CoWork\n\nSo far you've tested the agent programmatically with Python. But you don't always need to write code to use an agent. **Snowflake CoWork** provides a chat interface where you can talk to any agent created with `CREATE AGENT`, with no additional setup required.\n\nThis is especially useful for non-technical users (business analysts, product managers, support teams) who want to ask questions without writing SQL or Python.\n\n### Open Snowflake CoWork\n\nFollow these steps to connect to your agent:\n\n1. In Snowsight, navigate to **AI & ML \u003E Snowflake CoWork** in the left sidebar\n2. In the chat bar at the bottom, click the **agent picker** (it may say \"General purpose\" by default)\n3. Select **TUTORIAL_AGENT** from the list\n4. Type a question and press Enter\n\n### Try These Questions\n\nTest both tools to see the agent route questions automatically:\n\n**Structured data questions** (routes to Cortex Analyst):\n- \"What are total sales by region?\"\n- \"Which product has the highest revenue?\"\n- \"How many units did we sell in Europe?\"\n- \"Show me sales broken down by customer segment\"\n\n**Documentation questions** (routes to Cortex Search):\n- \"How do I assemble the standing desk?\"\n- \"What are the specs for the Office Chair?\"\n- \"How do I fix laptop overheating?\"\n- \"What are the care instructions for the office chair?\"\n\nSnowflake CoWork can present the agent's responses as tables or charts depending on the question:\n\n![Snowflake CoWork response with tabular data](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-a-cortex-agent-from-scratch-with-snowflake/si-1.png?v=c44e93c1)\n\n![Snowflake CoWork response with a chart](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-a-cortex-agent-from-scratch-with-snowflake/si-2.png?v=c44e93c1)\n\n**Try a follow-up question** to test multi-turn conversation:\n1. First ask: \"What are total sales by region?\"\n2. Then follow up: \"Which region had the lowest?\" The agent remembers the previous context\n\n### What Snowflake CoWork Provides\n\n- **Streaming responses**: watch the agent think and call tools in real time, so you can see its reasoning process\n- **Multi-turn conversations**: ask follow-up questions that build on previous context without re-stating the original question\n- **Tool call visibility**: expand the tool call section to see which tool was chosen, the generated SQL, or the search results\n- **Shareable**: other users with access to the agent can use the same interface\n\nThis is the fastest way to interact with any agent built with `CREATE AGENT`, and it's what most end users will use in practice.\n\n\u003C!-- ------------------------ --\u003E\n## Ask Questions with Snowflake CoCo\n\nYou can also interact with the objects you built in this guide directly from a Snowflake Notebook using **Snowflake CoCo**, an AI coding assistant built into Snowsight (also available as [Snowflake CoCo CLI](https://docs.snowflake.com/en/user-guide/cortex-code/cortex-code) for terminal-based workflows).\n\nBecause the agent, semantic view, search service, and tables all persist as first-class objects in your Snowflake account, Snowflake CoCo is context-aware and can discover and use them to answer questions without any extra configuration.\n\n### Open Snowflake CoCo\n\n1. Open any Snowflake Notebook (or the companion notebook from this guide)\n2. Click the **Snowflake CoCo toggle** in the bottom-right corner of the notebook\n3. Type a question in the chat panel\n\n![Snowflake CoCo in a Snowflake Notebook](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-a-cortex-agent-from-scratch-with-snowflake/cortex-code.png?v=c44e93c1)\n\n### Provide Context in Your Questions\n\nSnowflake CoCo works best when your question includes enough context for it to identify the right objects. Mention the relevant table name, semantic view, or agent so Snowflake CoCo can route your request accurately.\n\n**Examples with context:**\n\n- \"Using the `tutorial_agent` agent, what are total sales by region?\"\n- \"Query the `sales_semantic_view` semantic view to show revenue by product category\"\n- \"What product documentation is indexed in the `product_search_service` search service?\"\n- \"What are the top-selling products in the `CORTEX_AGENTS_LAB.TUTORIAL.SALES` table?\"\n\n### Why This Works\n\nWhen you created the agent, semantic view, and search service earlier in this guide, those objects became persistent resources in your Snowflake account. Snowflake CoCo can detect these objects and use them to answer your questions, just like Snowflake CoWork does. The difference is that Snowflake CoCo lives inside the notebook environment, so you can ask questions and get answers without leaving your development workflow.\n\n\u003C!-- ------------------------ --\u003E\n## Conclusion And Resources\n\nYou've built a fully functional Cortex Agent from scratch. Starting from raw data, you created the infrastructure (semantic view, search service) and the orchestration layer (agent) that ties it all together.\n\n### What You Learned\n\nHere's a recap of the key concepts and skills you picked up:\n\n- **Semantic views** define the business meaning of your data (dimensions, metrics, comments) so that an LLM can translate natural language questions into accurate SQL\n- **Cortex Search services** index unstructured text for semantic search, enabling retrieval-augmented generation (RAG) where answers are grounded in your actual documents\n- **Cortex Agents** orchestrate across multiple tools automatically. You write the instructions, and the agent handles routing, execution, and response synthesis\n- **`CREATE AGENT ... FROM SPECIFICATION`** lets you define an entire agent (model, instructions, tools, resources) in a single SQL statement with embedded YAML\n- **`DATA_AGENT_RUN`** lets you call the agent programmatically and parse the full response (thinking, tool calls, results, answer) with Python\n- **Snowflake CoWork** provides a no-code chat interface for any agent, making it accessible to non-technical users\n\n### What You Built\n\n| Component | What It Does | Why It Matters |\n|-----------|-------------|----------------|\n| **Sales table** | Stores 12 structured transactions | Provides the data Cortex Analyst queries |\n| **Product docs table** | Stores 6 unstructured documents | Provides the text Cortex Search indexes |\n| **Semantic View** | Maps business terms to SQL expressions | Enables accurate natural language to SQL |\n| **Search Service** | Indexes documents for semantic search | Enables RAG over product documentation |\n| **Cortex Agent** | Orchestrates Analyst + Search tools | Routes questions to the right tool automatically |\n| **Python parser** | Extracts thinking, SQL, results, answers | Gives full visibility into agent behavior |\n\n### Next Steps\n\nNow that you have a working agent, here are ways to extend it:\n\n- **Add custom tools**: create stored procedures for business logic (e.g., inventory lookups, price calculations) and register them as generic tools in the agent spec\n- **Add web search**: enable the agent to search the web for real-time external information\n- **Build a Streamlit app**: use `DATA_AGENT_RUN` as the backend for a custom chat UI built with Streamlit\n- **Configure access control**: use Snowflake's role-based access control (RBAC) to manage who can use the agent and what data they can access through it\n\n### Cleanup\n\nTo remove all objects created in this guide, run the following. The commented-out lines at the bottom drop the entire database and schema; uncomment them only if you want to remove everything.\n\n```sql\n-- Drop the agent and its tools\nDROP AGENT IF EXISTS tutorial_agent;\nDROP CORTEX SEARCH SERVICE IF EXISTS product_search_service;\nDROP SEMANTIC VIEW IF EXISTS sales_semantic_view;\n\n-- Drop the data tables\nDROP TABLE IF EXISTS sales;\nDROP TABLE IF EXISTS inventory;\nDROP TABLE IF EXISTS product_docs;\n\n-- Uncomment these to remove the database and schema entirely:\n-- DROP SCHEMA IF EXISTS TUTORIAL;\n-- DROP DATABASE IF EXISTS CORTEX_AGENTS_LAB;\n```\n\n### Related Resources\n\n- [Cortex Agents](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents): concepts and architecture of Cortex Agents\n- [CREATE AGENT](https://docs.snowflake.com/en/sql-reference/sql/create-agent): create Cortex Agents\n- [CREATE SEMANTIC VIEW](https://docs.snowflake.com/en/sql-reference/sql/create-semantic-view): define semantic views\n- [Cortex Search](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-search/cortex-search-overview): build Cortex Search services\n- [Overview of Snowflake CoWork](https://docs.snowflake.com/user-guide/snowflake-cortex/snowflake-intelligence): chat interface for agents\n",":type":"text/x-markdown","multiValue":false},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image",":type":"text/plain","multiValue":false}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-1cd0b0c439","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-c9c1c228a7",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-4f5fbdf0a4","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2026-03-12",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-dc12662752","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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