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This quickstart demonstrates how to build a \u003Cstrong\u003Emulti-agent supervisor architecture\u003C/strong\u003E using LangGraph and Snowflake Cortex Agents for customer intelligence, churn prediction, and business analytics.\u003C/p\u003E\n","\u003Cp\u003EYou can also watch this use case in action on Youtube:\u003C/p\u003E\n","\u003Cp\u003E\u003Ca href=\"https://www.youtube.com/watch?v=LsanBaHbCvE\"\u003ELangGraph And Snowflake Cortex AI: Exploring Multi-Agentic Workflows\u003C/a\u003E\u003C/p\u003E\n","\u003Ch3\u003EWhat is a Multi-Agent Supervisor Architecture?\u003C/h3\u003E\n","\u003Cp\u003EA multi-agent supervisor architecture uses a central "supervisor" LLM as a hub that coordinates specialized agents:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\u003Cstrong\u003EPlans\u003C/strong\u003E an execution strategy with explicit steps (once, immutable)\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ERoutes\u003C/strong\u003E queries to specialized agents based on the plan\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ECoordinates\u003C/strong\u003E data flow between agents when multi-step analysis is needed\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESynthesizes\u003C/strong\u003E results from agents into coherent executive summaries\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode\u003EUser Query → Supervisor (Plan) → Agent → Supervisor (Route) → Agent → Supervisor (Synthesize) → Summary\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EWhy LangGraph\u003C/h3\u003E\n","\u003Cp\u003E\u003Ca href=\"https://github.com/langchain-ai/langgraph\"\u003ELangGraph\u003C/a\u003E provides low-level supporting infrastructure for any long-running, stateful workflow or agent. LangGraph is great for building durable, stateful agents across multiple systems.\u003C/p\u003E\n","\u003Cp\u003EIf you're not building with LangGraph, try this \u003Ca href=\"https://www.snowflake.com/en/developers/guides/multi-agent-orchestration-snowflake-intelligence/\"\u003Eguide\u003C/a\u003E to build a multi-agent system entirely native to Snowflake.\u003C/p\u003E\n","\u003Ch3\u003EArchitecture Overview\u003C/h3\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-and-evaluate-agents-with-langgraph-and-snowflake/architecture.png\" alt=\"Architecture\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EWhat You'll Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to set up Snowflake Cortex Agents with specialized tools\u003C/li\u003E\u003Cli\u003EHow to create Cortex Search services for semantic search\u003C/li\u003E\u003Cli\u003EHow to build Semantic Views for Cortex Analyst text-to-SQL\u003C/li\u003E\u003Cli\u003EHow to build a multi-agent supervisor workflow using LangGraph\u003C/li\u003E\u003Cli\u003EHow to run and debug your workflow with LangGraph Studio\u003C/li\u003E\u003Cli\u003EHow to evaluate agent performance using TruLens with Snowflake\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Build\u003C/h3\u003E\n","\u003Cp\u003EA complete multi-agent customer intelligence system that:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ERoutes queries to specialized agents (Content, Data Analyst, Research)\u003C/li\u003E\u003Cli\u003EUses Cortex Search for semantic search across support tickets\u003C/li\u003E\u003Cli\u003EUses Cortex Analyst for natural language to SQL conversion\u003C/li\u003E\u003Cli\u003EUses custom AI UDFs for sentiment analysis and churn prediction\u003C/li\u003E\u003Cli\u003ECan be run interactively via LangGraph Studio or programmatically via notebook\u003C/li\u003E\u003Cli\u003EEvaluates responses using Agent Goal-Plan-Action alignment metrics via TruLens\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://signup.snowflake.com/\"\u003ESnowflake Account\u003C/a\u003E with Cortex AI features enabled\u003C/li\u003E\u003Cli\u003EPython 3.9+ installed locally\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://git-scm.com/book/en/v2/Getting-Started-Installing-Git\"\u003EGit\u003C/a\u003E installed\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://langchain-ai.github.io/langgraph/how-tos/local-studio/\"\u003ELangGraph CLI\u003C/a\u003E (\u003Ccode\u003Epip install langgraph-cli\u003C/code\u003E) for LangGraph Studio\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://smith.langchain.com/\"\u003ELangSmith Account\u003C/a\u003E (optional, for tracing)\u003C/li\u003E\u003Cli\u003EBasic familiarity with LangGraph and LangChain concepts (optional)\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch2\u003ESetup Git Integration\u003C/h2\u003E\n","\u003Cp\u003EThe first step is to clone the repository into Snowflake using Git Integration. This gives you access to all the SQL scripts and demo data files.\u003C/p\u003E\n","\u003Ch3\u003ECreate Database and Clone Repository\u003C/h3\u003E\n","\u003Cp\u003EOpen Snowsight and create a new SQL Worksheet. Run the following SQL:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE ACCOUNTADMIN;\n\n-- Create database and schema\nCREATE DATABASE IF NOT EXISTS CUSTOMER_INTELLIGENCE_DB;\nUSE DATABASE CUSTOMER_INTELLIGENCE_DB;\nCREATE SCHEMA IF NOT EXISTS PUBLIC;\nUSE SCHEMA PUBLIC;\n\n-- Create API integration for GitHub\nCREATE API INTEGRATION IF NOT EXISTS github_api_integration\n    API_PROVIDER = git_https_api\n    API_ALLOWED_PREFIXES = ('https://github.com/Snowflake-Labs/')\n    ENABLED = TRUE;\n\n-- Clone the GitHub repository\nCREATE OR REPLACE GIT REPOSITORY customer_intelligence_demo\n    API_INTEGRATION = github_api_integration\n    ORIGIN = 'https://github.com/Snowflake-Labs/sfguide-build-and-evaluate-agents-with-snowflake-and-langgraph.git';\n\n-- Fetch latest from GitHub\nALTER GIT REPOSITORY customer_intelligence_demo FETCH;\n\n-- Verify repository contents\nLS @customer_intelligence_demo/branches/main/;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou should see the SQL scripts and CSV files in the repository listing.\u003C/p\u003E\n","\u003Ch3\u003ERun Setup Scripts\u003C/h3\u003E\n","\u003Cp\u003EAfter the Git integration is set up, you can run each SQL script directly from the repository. Navigate to \u003Cstrong\u003EData » Databases » CUSTOMER_INTELLIGENCE_DB » PUBLIC » Git Repositories » customer_intelligence_demo » branches » main » sql\u003C/strong\u003E and execute each script in order:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EOrder\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EScript\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EPurpose\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E1\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E01_setup_database_and_load_data.sql\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECreates tables and loads CSV data\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E2\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E02_setup_cortex_search.sql\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECreates Cortex Search services\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E3\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E03_setup_semantic_views.sql\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECreates Semantic Views for Cortex Analyst\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E4\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E04_setup_udfs.sql\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECreates AI UDFs (tools for agents)\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E5\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E05_setup_cortex_agents.sql\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECreates the three Cortex Agents\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote:\u003C/strong\u003E Run scripts in order as later scripts depend on earlier ones.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EThe following sections explain what each script does in detail.\u003C/p\u003E\n","\u003Ch2\u003ESetup Database and Load Data\u003C/h2\u003E\n","\u003Cp\u003EThe \u003Ccode\u003E01_setup_database_and_load_data.sql\u003C/code\u003E script creates the tables and loads demo data. Here's what it does:\u003C/p\u003E\n","\u003Ch3\u003ECreate Tables\u003C/h3\u003E\n","\u003Cp\u003ECreate the four tables that will store customer data:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- CUSTOMERS TABLE\nCREATE OR REPLACE TABLE CUSTOMERS (\n    customer_id VARCHAR(50) PRIMARY KEY,\n    signup_date DATE NOT NULL,\n    plan_type VARCHAR(20) NOT NULL,\n    company_size VARCHAR(20) NOT NULL,\n    industry VARCHAR(30) NOT NULL,\n    status VARCHAR(20) NOT NULL DEFAULT 'active',\n    monthly_revenue DECIMAL(10,2),\n    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP()\n);\n\n-- USAGE_EVENTS TABLE\nCREATE OR REPLACE TABLE USAGE_EVENTS (\n    event_id VARCHAR(50) PRIMARY KEY,\n    customer_id VARCHAR(50) NOT NULL,\n    event_date DATE NOT NULL,\n    feature_used VARCHAR(50) NOT NULL,\n    session_duration_minutes INTEGER NOT NULL,\n    actions_count INTEGER NOT NULL,\n    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP()\n);\n\n-- SUPPORT_TICKETS TABLE\nCREATE OR REPLACE TABLE SUPPORT_TICKETS (\n    ticket_id VARCHAR(50) PRIMARY KEY,\n    customer_id VARCHAR(50) NOT NULL,\n    created_date DATE NOT NULL,\n    category VARCHAR(30) NOT NULL,\n    priority VARCHAR(10) NOT NULL,\n    status VARCHAR(20) NOT NULL,\n    resolution_time_hours INTEGER,\n    satisfaction_score INTEGER,\n    ticket_text TEXT NOT NULL,\n    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP()\n);\n\n-- CHURN_EVENTS TABLE\nCREATE OR REPLACE TABLE CHURN_EVENTS (\n    churn_id VARCHAR(50) PRIMARY KEY,\n    customer_id VARCHAR(50) NOT NULL,\n    churn_date DATE NOT NULL,\n    churn_reason VARCHAR(50) NOT NULL,\n    days_since_signup INTEGER NOT NULL,\n    final_plan_type VARCHAR(20),\n    final_monthly_revenue DECIMAL(10,2),\n    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP()\n);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStep 3: Load Demo Data\u003C/h3\u003E\n","\u003Cp\u003ELoad the CSV files from the Git repository:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Create file format\nCREATE OR REPLACE FILE FORMAT csv_format\n    TYPE = CSV \n    SKIP_HEADER = 1 \n    FIELD_OPTIONALLY_ENCLOSED_BY = '"'\n    NULL_IF = ('', 'NULL', 'null') \n    EMPTY_FIELD_AS_NULL = TRUE;\n\n-- Load CUSTOMERS\nINSERT INTO CUSTOMERS (customer_id, signup_date, plan_type, company_size, industry, status, monthly_revenue)\nSELECT $1,$2,$3,$4,$5,$6,$7 \nFROM @customer_intelligence_demo/branches/main/demo_customers.csv (FILE_FORMAT=>csv_format);\n\n-- Load USAGE_EVENTS  \nINSERT INTO USAGE_EVENTS (event_id, customer_id, event_date, feature_used, session_duration_minutes, actions_count)\nSELECT $1,$2,$3,$4,$5,$6 \nFROM @customer_intelligence_demo/branches/main/demo_usage_events.csv (FILE_FORMAT=>csv_format);\n\n-- Load SUPPORT_TICKETS\nINSERT INTO SUPPORT_TICKETS (ticket_id, customer_id, created_date, category, priority, status, resolution_time_hours, satisfaction_score, ticket_text)\nSELECT $1,$2,$3,$4,$5,$6,$7,$8,$9 \nFROM @customer_intelligence_demo/branches/main/demo_support_tickets.csv (FILE_FORMAT=>csv_format);\n\n-- Load CHURN_EVENTS\nINSERT INTO CHURN_EVENTS (churn_id, customer_id, churn_date, churn_reason, days_since_signup, final_plan_type, final_monthly_revenue)\nSELECT $1,$2,$3,$4,$5,$6,$7 \nFROM @customer_intelligence_demo/branches/main/demo_churn_events.csv (FILE_FORMAT=>csv_format);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStep 4: Verify Data Loaded\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT 'CUSTOMERS' as table_name, COUNT(*) as row_count FROM CUSTOMERS\nUNION ALL SELECT 'USAGE_EVENTS', COUNT(*) FROM USAGE_EVENTS\nUNION ALL SELECT 'SUPPORT_TICKETS', COUNT(*) FROM SUPPORT_TICKETS\nUNION ALL SELECT 'CHURN_EVENTS', COUNT(*) FROM CHURN_EVENTS;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou should see data in all four tables.\u003C/p\u003E\n","\u003Ch2\u003ESetup Cortex Search Services\u003C/h2\u003E\n","\u003Cp\u003ECortex Search provides hybrid search (semantic + keyword) capabilities for unstructured text data. We'll create search services that the agents will use to find relevant customer feedback and support tickets.\u003C/p\u003E\n","\u003Ch3\u003ECreate Support Tickets Search Service\u003C/h3\u003E\n","\u003Cp\u003EThis service enables semantic search across all customer support tickets:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE DATABASE CUSTOMER_INTELLIGENCE_DB;\nUSE SCHEMA PUBLIC;\n\nCREATE OR REPLACE CORTEX SEARCH SERVICE SUPPORT_TICKETS_SEARCH\nON ticket_text\nATTRIBUTES customer_id, ticket_id, category, priority, status, created_date\nWAREHOUSE = COMPUTE_WH\nTARGET_LAG = '1 hour'\nAS (\n    SELECT \n        ticket_id,\n        customer_id,\n        category,\n        priority,\n        status,\n        created_date,\n        ticket_text\n    FROM SUPPORT_TICKETS\n);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ECreate Customers Search Service (Optional)\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE CORTEX SEARCH SERVICE CUSTOMERS_SEARCH\nON industry\nATTRIBUTES customer_id, plan_type, company_size, status, monthly_revenue\nWAREHOUSE = COMPUTE_WH\nTARGET_LAG = '1 hour'\nAS (\n    SELECT \n        customer_id,\n        plan_type,\n        company_size,\n        industry,\n        status,\n        monthly_revenue\n    FROM CUSTOMERS\n);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EVerify Search Services\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESHOW CORTEX SEARCH SERVICES IN SCHEMA CUSTOMER_INTELLIGENCE_DB.PUBLIC;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003ESetup Semantic Views\u003C/h2\u003E\n","\u003Cp\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/views-semantic/overview\"\u003ESemantic Views\u003C/a\u003E power Cortex Analyst, enabling natural language to SQL conversion. They define the schema, relationships, and business metrics that the AI uses to generate accurate SQL queries.\u003C/p\u003E\n","\u003Ch3\u003ECreate Customer Behavior Analyst View\u003C/h3\u003E\n","\u003Cp\u003EThis semantic view is used by the DATA_ANALYST_AGENT for customer behavior analytics:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE SEMANTIC VIEW CUSTOMER_BEHAVIOR_ANALYST\n  TABLES (\n    CUSTOMERS AS CUSTOMER_INTELLIGENCE_DB.PUBLIC.CUSTOMERS\n      PRIMARY KEY (CUSTOMER_ID)\n      COMMENT = 'Customer master data',\n    USAGE_EVENTS AS CUSTOMER_INTELLIGENCE_DB.PUBLIC.USAGE_EVENTS\n      COMMENT = 'Customer usage events',\n    SUPPORT_TICKETS AS CUSTOMER_INTELLIGENCE_DB.PUBLIC.SUPPORT_TICKETS\n      COMMENT = 'Support tickets',\n    CHURN_EVENTS AS CUSTOMER_INTELLIGENCE_DB.PUBLIC.CHURN_EVENTS\n      COMMENT = 'Churn events'\n  )\n  RELATIONSHIPS (\n    CUSTOMER_TO_USAGE AS USAGE_EVENTS (CUSTOMER_ID) REFERENCES CUSTOMERS (CUSTOMER_ID),\n    CUSTOMER_TO_SUPPORT AS SUPPORT_TICKETS (CUSTOMER_ID) REFERENCES CUSTOMERS (CUSTOMER_ID),\n    CUSTOMER_TO_CHURN AS CHURN_EVENTS (CUSTOMER_ID) REFERENCES CUSTOMERS (CUSTOMER_ID)\n  )\n  FACTS (\n    CUSTOMERS.MONTHLY_REVENUE AS CUSTOMERS.MONTHLY_REVENUE\n      WITH SYNONYMS = ('MRR', 'monthly recurring revenue')\n      COMMENT = 'Monthly revenue per customer',\n    USAGE_EVENTS.SESSION_DURATION_MINUTES AS USAGE_EVENTS.SESSION_DURATION_MINUTES\n      WITH SYNONYMS = ('session length')\n      COMMENT = 'Session duration in minutes',\n    USAGE_EVENTS.ACTIONS_COUNT AS USAGE_EVENTS.ACTIONS_COUNT\n      COMMENT = 'Number of actions in session',\n    SUPPORT_TICKETS.SATISFACTION_SCORE AS SUPPORT_TICKETS.SATISFACTION_SCORE\n      WITH SYNONYMS = ('CSAT')\n      COMMENT = 'Customer satisfaction score'\n  )\n  DIMENSIONS (\n    CUSTOMERS.CUSTOMER_ID AS CUSTOMERS.CUSTOMER_ID\n      WITH SYNONYMS = ('customer identifier', 'account id')\n      COMMENT = 'Unique customer identifier',\n    CUSTOMERS.INDUSTRY AS CUSTOMERS.INDUSTRY\n      WITH SYNONYMS = ('sector', 'vertical')\n      COMMENT = 'Customer industry',\n    CUSTOMERS.PLAN_TYPE AS CUSTOMERS.PLAN_TYPE\n      WITH SYNONYMS = ('subscription', 'tier')\n      COMMENT = 'Subscription plan level'\n  )\n  METRICS (\n    CUSTOMERS.TOTAL_CUSTOMERS AS COUNT(CUSTOMERS.CUSTOMER_ID)\n      WITH SYNONYMS = ('customer count')\n      COMMENT = 'Total number of customers',\n    CUSTOMERS.ARPU AS AVG(CUSTOMERS.MONTHLY_REVENUE)\n      WITH SYNONYMS = ('average revenue per user')\n      COMMENT = 'Average revenue per customer',\n    USAGE_EVENTS.AVG_SESSION_DURATION AS AVG(USAGE_EVENTS.SESSION_DURATION_MINUTES)\n      COMMENT = 'Average session duration'\n  )\n  COMMENT = 'Customer behavior analytics for usage patterns, churn analysis, and retention insights';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ECreate Strategic Research Analyst View\u003C/h3\u003E\n","\u003Cp\u003EThis semantic view is used by the RESEARCH_AGENT for market intelligence:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE SEMANTIC VIEW STRATEGIC_RESEARCH_ANALYST\n  TABLES (\n    CUSTOMERS AS CUSTOMER_INTELLIGENCE_DB.PUBLIC.CUSTOMERS\n      PRIMARY KEY (CUSTOMER_ID)\n      COMMENT = 'Customer master data',\n    USAGE_EVENTS AS CUSTOMER_INTELLIGENCE_DB.PUBLIC.USAGE_EVENTS\n      COMMENT = 'Usage data',\n    SUPPORT_TICKETS AS CUSTOMER_INTELLIGENCE_DB.PUBLIC.SUPPORT_TICKETS\n      COMMENT = 'Support data',\n    CHURN_EVENTS AS CUSTOMER_INTELLIGENCE_DB.PUBLIC.CHURN_EVENTS\n      COMMENT = 'Churn data'\n  )\n  RELATIONSHIPS (\n    CUSTOMER_TO_USAGE AS USAGE_EVENTS (CUSTOMER_ID) REFERENCES CUSTOMERS (CUSTOMER_ID),\n    CUSTOMER_TO_SUPPORT AS SUPPORT_TICKETS (CUSTOMER_ID) REFERENCES CUSTOMERS (CUSTOMER_ID),\n    CUSTOMER_TO_CHURN AS CHURN_EVENTS (CUSTOMER_ID) REFERENCES CUSTOMERS (CUSTOMER_ID)\n  )\n  METRICS (\n    CUSTOMERS.TOTAL_MRR AS SUM(CUSTOMERS.MONTHLY_REVENUE)\n      WITH SYNONYMS = ('total recurring revenue')\n      COMMENT = 'Total monthly recurring revenue',\n    CHURN_EVENTS.LOST_REVENUE AS SUM(CHURN_EVENTS.FINAL_MONTHLY_REVENUE)\n      COMMENT = 'Total lost revenue'\n  )\n  COMMENT = 'Strategic research and market intelligence for executive-level business analysis';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EVerify Semantic Views\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESHOW SEMANTIC VIEWS IN SCHEMA CUSTOMER_INTELLIGENCE_DB.PUBLIC;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003ESetup Custom AI UDFs\u003C/h2\u003E\n","\u003Cp\u003ECustom UDFs (User-Defined Functions) extend agent capabilities with specialized AI analysis. These functions use Snowflake Cortex AI to perform sentiment analysis, behavior analysis, and strategic insights.\u003C/p\u003E\n","\u003Ch3\u003ECreate Customer Content Analyzer UDF\u003C/h3\u003E\n","\u003Cp\u003EUsed by the CONTENT_AGENT for sentiment and feedback analysis:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE FUNCTION AI_ANALYZE_CUSTOMER_CONTENT(\n    "CUSTOMER_IDS_STRING" VARCHAR, \n    "ANALYSIS_TYPE" VARCHAR DEFAULT 'recent_support_tickets'\n)\nRETURNS VARIANT\nLANGUAGE SQL\nAS '\nWITH parsed_customer_ids AS (\n  SELECT TRIM(VALUE) as customer_id\n  FROM TABLE(SPLIT_TO_TABLE(customer_ids_string, '',''))\n),\ntarget_tickets AS (\n  SELECT \n    st.customer_id,\n    st.ticket_text,\n    st.category,\n    st.priority,\n    st.satisfaction_score,\n    st.created_date\n  FROM support_tickets st\n  INNER JOIN parsed_customer_ids pci ON st.customer_id = pci.customer_id\n  ORDER BY st.created_date DESC\n  LIMIT 10\n),\ncontent_summary AS (\n  SELECT \n    COUNT(*) as total_tickets,\n    COUNT(DISTINCT customer_id) as customers_with_tickets,\n    ROUND(AVG(satisfaction_score), 2) as avg_satisfaction,\n    COUNT(CASE WHEN priority IN (''high'', ''critical'') THEN 1 END) as urgent_tickets,\n    LISTAGG(DISTINCT category, '', '') as ticket_categories,\n    SUBSTRING(LISTAGG(ticket_text, '' | ''), 1, 1000) as combined_ticket_text\n  FROM target_tickets\n),\nai_content_analysis AS (\n  SELECT \n    cs.*,\n    CASE \n      WHEN cs.total_tickets > 0 THEN\n        AI_COMPLETE(\n          ''claude-3-5-sonnet'',\n          CONCAT(\n            ''Analyze customer feedback: '', analysis_type, ''\\\\n'',\n            ''Tickets: '', cs.total_tickets, '' ('', cs.urgent_tickets, '' urgent)\\\\n'',\n            ''Satisfaction: '', cs.avg_satisfaction, ''/5\\\\n'',\n            ''Categories: '', cs.ticket_categories, ''\\\\n'',\n            ''Content: '', cs.combined_ticket_text, ''\\\\n\\\\n'',\n            ''JSON: {"sentiment":"positive/neutral/negative","urgency":"low/medium/high","key_issue":"main_problem","action_needed":"recommendation"}''\n          )\n        )\n      ELSE NULL\n    END as ai_content_insights\n  FROM content_summary cs\n)\nSELECT OBJECT_CONSTRUCT(\n    ''analysis_type'', analysis_type,\n    ''customer_ids_input'', customer_ids_string,\n    ''content_metrics'', OBJECT_CONSTRUCT(\n      ''total_tickets'', total_tickets,\n      ''customers_affected'', customers_with_tickets,\n      ''avg_satisfaction'', avg_satisfaction,\n      ''urgent_tickets'', urgent_tickets\n    ),\n    ''ai_content_insights'', TRY_PARSE_JSON(ai_content_insights),\n    ''analysis_timestamp'', CURRENT_TIMESTAMP()\n)::VARIANT as result\nFROM ai_content_analysis\n';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ECreate Customer Segment Analyzer UDF\u003C/h3\u003E\n","\u003Cp\u003EUsed by the DATA_ANALYST_AGENT to analyze customer behavior, metrics and analytics.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE FUNCTION AI_ANALYZE_CUSTOMER_SEGMENT("CUSTOMER_IDS_STRING" VARCHAR, "SEGMENT_NAME" VARCHAR DEFAULT 'high_value_customers')\nRETURNS VARIANT\nLANGUAGE SQL\nAS '\nWITH parsed_customer_ids AS (\n  -- Parse comma-separated string into individual customer IDs\n  SELECT \n    TRIM(VALUE) as customer_id\n  FROM TABLE(SPLIT_TO_TABLE(customer_ids_string, '',''))\n),\ntarget_customers AS (\n  SELECT \n    c.customer_id,\n    c.plan_type,\n    c.monthly_revenue,\n    c.company_size,\n    c.industry,\n    c.status,\n    DATEDIFF(''day'', c.signup_date, CURRENT_DATE()) as customer_age_days\n  FROM customers c\n  INNER JOIN parsed_customer_ids pci ON c.customer_id = pci.customer_id\n),\nsegment_usage AS (\n  SELECT \n    COUNT(DISTINCT u.customer_id) as active_users,\n    COUNT(*) as total_events,\n    ROUND(AVG(u.session_duration_minutes), 2) as avg_session_duration,\n    SUM(u.actions_count) as total_actions\n  FROM usage_events u\n  INNER JOIN parsed_customer_ids pci ON u.customer_id = pci.customer_id\n  WHERE u.event_date >= CURRENT_DATE() - 365 - 90  -- Handle data offset\n),\nsegment_support AS (\n  SELECT \n    COUNT(*) as total_tickets,\n    ROUND(AVG(satisfaction_score), 2) as avg_satisfaction,\n    COUNT(CASE WHEN priority IN (''high'', ''critical'') THEN 1 END) as high_priority_tickets\n  FROM support_tickets s\n  INNER JOIN parsed_customer_ids pci ON s.customer_id = pci.customer_id\n  WHERE s.created_date >= CURRENT_DATE() - 365 - 90  -- Handle data offset\n),\nsegment_summary AS (\n  SELECT \n    tc.customer_count,\n    tc.total_mrr,\n    tc.avg_revenue,\n    tc.plan_mix,\n    su.active_users,\n    su.total_events,\n    su.avg_session_duration,\n    ss.total_tickets,\n    ss.avg_satisfaction,\n    -- AI analysis with proper input validation\n    CASE \n      WHEN tc.customer_count > 0 THEN\n        AI_COMPLETE(\n          ''claude-3-5-sonnet'',\n          CONCAT(\n            ''Analyze customer segment and respond with valid JSON only:\\\\n'',\n            ''Segment: '', segment_name, ''\\\\n'',\n            ''Customers: '', tc.customer_count, ''\\\\n'',\n            ''MRR: $'', tc.total_mrr, '' (avg $'', tc.avg_revenue, '')\\\\n'',\n            ''Usage: '', COALESCE(su.active_users, 0), '' active, '', COALESCE(su.total_events, 0), '' events\\\\n'',\n            ''Support: '', COALESCE(ss.total_tickets, 0), '' tickets, '', COALESCE(ss.avg_satisfaction, 0), ''/5 satisfaction\\\\n\\\\n'',\n            ''JSON: {"risk_level":"low/medium/high/critical","key_insight":"main_finding","recommendation":"action"}''\n          )\n        )\n      ELSE NULL\n    END as ai_segment_analysis\n  FROM (\n    SELECT \n      COUNT(*) as customer_count,\n      SUM(monthly_revenue) as total_mrr,\n      ROUND(AVG(monthly_revenue), 0) as avg_revenue,\n      LISTAGG(DISTINCT plan_type, '', '') as plan_mix\n    FROM target_customers\n  ) tc\n  CROSS JOIN segment_usage su\n  CROSS JOIN segment_support ss\n)\nSELECT \n  (OBJECT_CONSTRUCT(\n    ''segment_name'', segment_name,\n    ''customer_ids_input'', customer_ids_string,\n    ''segment_metrics'', OBJECT_CONSTRUCT(\n      ''customer_count'', customer_count,\n      ''total_mrr'', total_mrr,\n      ''avg_revenue_per_customer'', avg_revenue,\n      ''plan_mix'', plan_mix,\n      ''active_users'', COALESCE(active_users, 0),\n      ''avg_session_duration'', COALESCE(avg_session_duration, 0),\n      ''support_satisfaction'', COALESCE(avg_satisfaction, 0)\n    ),\n    ''ai_insights'', TRY_PARSE_JSON(ai_segment_analysis),\n    ''raw_ai_response'', ai_segment_analysis,\n    ''processing_mode'', ''agent_compatible_analysis'',\n    ''node_name'', ''ai_customer_segment_analyzer'',\n    ''analysis_timestamp'', CURRENT_TIMESTAMP()\n  ))::VARIANT as result\nFROM segment_summary\n';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ECreate Churn Risk Predictor UDF\u003C/h3\u003E\n","\u003Cp\u003EUsed by the DATA_ANALYST_AGENT for engagement and churn risk prediction:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE FUNCTION AI_PREDICT_CHURN_RISK(\n    "CUSTOMER_IDS_STRING" VARCHAR, \n    "ANALYSIS_DAYS" NUMBER(38,0) DEFAULT 30\n)\nRETURNS VARIANT\nLANGUAGE SQL\nAS '\nWITH parsed_customer_ids AS (\n  SELECT TRIM(VALUE) as customer_id\n  FROM TABLE(SPLIT_TO_TABLE(customer_ids_string, '',''))\n),\ntarget_customers AS (\n  SELECT \n    c.customer_id,\n    c.plan_type,\n    c.monthly_revenue,\n    c.company_size,\n    c.industry,\n    c.status\n  FROM customers c\n  INNER JOIN parsed_customer_ids pci ON c.customer_id = pci.customer_id\n),\nbehavior_metrics AS (\n  SELECT \n    COUNT(DISTINCT u.customer_id) as active_customers,\n    COUNT(*) as total_events,\n    ROUND(AVG(u.session_duration_minutes), 2) as avg_session_duration,\n    SUM(u.actions_count) as total_actions\n  FROM usage_events u\n  INNER JOIN parsed_customer_ids pci ON u.customer_id = pci.customer_id\n),\nanalysis_result AS (\n  SELECT \n    tc.customer_count,\n    tc.total_mrr,\n    bm.active_customers,\n    bm.total_events,\n    bm.avg_session_duration,\n    CASE \n      WHEN tc.customer_count > 0 THEN\n        AI_COMPLETE(\n          ''claude-3-5-sonnet'',\n          CONCAT(\n            ''Analyze customer behavior:\\\\n'',\n            ''Customers: '', tc.customer_count, '', MRR: $'', tc.total_mrr, ''\\\\n'',\n            ''Active: '', COALESCE(bm.active_customers, 0), ''\\\\n'',\n            ''Events: '', COALESCE(bm.total_events, 0), ''\\\\n'',\n            ''JSON: {"engagement":"high/medium/low","churn_risk":"low/medium/high","recommendation":"action"}''\n          )\n        )\n      ELSE NULL\n    END as ai_behavior_insights\n  FROM (\n    SELECT \n      COUNT(*) as customer_count,\n      SUM(monthly_revenue) as total_mrr\n    FROM target_customers\n  ) tc\n  CROSS JOIN behavior_metrics bm\n)\nSELECT OBJECT_CONSTRUCT(\n    ''customer_ids_input'', customer_ids_string,\n    ''behavior_metrics'', OBJECT_CONSTRUCT(\n      ''customer_count'', customer_count,\n      ''total_mrr'', total_mrr,\n      ''active_customers'', COALESCE(active_customers, 0),\n      ''avg_session_duration'', COALESCE(avg_session_duration, 0)\n    ),\n    ''ai_behavior_insights'', TRY_PARSE_JSON(ai_behavior_insights),\n    ''analysis_timestamp'', CURRENT_TIMESTAMP()\n)::VARIANT as result\nFROM analysis_result\n';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EVerify UDFs\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESHOW USER FUNCTIONS IN SCHEMA CUSTOMER_INTELLIGENCE_DB.PUBLIC;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003ECreate Cortex Agents\u003C/h2\u003E\n","\u003Cp\u003ENow we create the three specialized \u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents\"\u003ECortex Agents\u003C/a\u003E that will be orchestrated by the LangGraph supervisor. Each agent has specific tools and instructions for their domain.\u003C/p\u003E\n","\u003Ch3\u003ECreate Agent Database\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE DATABASE IF NOT EXISTS SNOWFLAKE_INTELLIGENCE;\nUSE DATABASE SNOWFLAKE_INTELLIGENCE;\nCREATE SCHEMA IF NOT EXISTS AGENTS;\nUSE SCHEMA AGENTS;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ECreate CONTENT_AGENT\u003C/h3\u003E\n","\u003Cp\u003EThe Content Agent specializes in customer feedback, sentiment analysis, and support intelligence:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE AGENT CONTENT_AGENT\n  COMMENT = 'Customer feedback, sentiment analysis, and communication intelligence specialist'\nFROM SPECIFICATION $$\n{\n    "models": {\n        "orchestration": "claude-4-sonnet"\n    },\n    "orchestration": {\n        "budget": {\n            "seconds": 60,\n            "tokens": 32000\n        }\n    },\n    "instructions": {\n        "system": "The Content Agent specializes in analyzing customer feedback, support interactions, and satisfaction trends. It combines targeted customer analysis with broad pattern recognition to distinguish between isolated incidents and systemic issues.",\n        "response": "Always synthesize data into executive insights. Provide maximum 3-5 key findings with business impact."\n    },\n    "tools": [\n        {\n            "tool_spec": {\n                "type": "cortex_search",\n                "name": "CUSTOMER_FEEDBACK_SEARCH",\n                "description": "Semantic search across all customer support tickets and feedback."\n            }\n        },\n        {\n            "tool_spec": {\n                "type": "generic",\n                "name": "CUSTOMER_CONTENT_ANALYZER",\n                "description": "AI-powered analysis of specific customer feedback and support interactions.",\n                "input_schema": {\n                    "type": "object",\n                    "properties": {\n                        "customer_ids_string": {\n                            "type": "string",\n                            "description": "Comma-separated list of customer IDs to analyze"\n                        }\n                    },\n                    "required": ["customer_ids_string"]\n                }\n            }\n        }\n    ],\n    "tool_resources": {\n        "CUSTOMER_FEEDBACK_SEARCH": {\n            "execution_environment": {\n                "type": "warehouse",\n                "warehouse": "COMPUTE_WH",\n                "query_timeout": 300\n            },\n            "search_service": "CUSTOMER_INTELLIGENCE_DB.PUBLIC.SUPPORT_TICKETS_SEARCH",\n            "id_column": "ticket_id",\n            "title_column": "ticket_text"\n        },\n        "CUSTOMER_CONTENT_ANALYZER": {\n            "type": "procedure",\n            "identifier": "CUSTOMER_INTELLIGENCE_DB.PUBLIC.AI_ANALYZE_CUSTOMER_CONTENT",\n            "execution_environment": {\n                "type": "warehouse",\n                "warehouse": "COMPUTE_WH",\n                "query_timeout": 300\n            }\n        }\n    }\n}\n$$;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ECreate DATA_ANALYST_AGENT\u003C/h3\u003E\n","\u003Cp\u003EThe Data Analyst Agent specializes in customer behavior, metrics, and predictive analytics:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE AGENT DATA_ANALYST_AGENT\n  COMMENT = 'Customer behavior, business metrics, and predictive analytics specialist'\nFROM SPECIFICATION $$\n{\n    "models": {\n        "orchestration": "claude-4-sonnet"\n    },\n    "orchestration": {\n        "budget": {\n            "seconds": 60,\n            "tokens": 32000\n        }\n    },\n    "instructions": {\n        "system": "Expert customer behavior analyst specializing in data-driven insights about customer engagement, usage patterns, churn prediction, and retention strategies.",\n        "response": "Always synthesize data into executive insights. Never return raw data tables."\n    },\n    "tools": [\n        {\n            "tool_spec": {\n                "type": "cortex_analyst_text_to_sql",\n                "name": "BUSINESS_INTELLIGENCE_ANALYST",\n                "description": "Natural language to SQL for comprehensive customer behavior analytics."\n            }\n        },\n        {\n            "tool_spec": {\n                "type": "generic",\n                "name": "CHURN_RISK_PREDICTOR",\n                "description": "AI-powered churn risk prediction and engagement analysis.",\n                "input_schema": {\n                    "type": "object",\n                    "properties": {\n                        "customer_ids_string": {\n                            "type": "string",\n                            "description": "Comma-separated list of customer IDs to analyze"\n                        }\n                    },\n                    "required": ["customer_ids_string"]\n                }\n            }\n        }\n    ],\n    "tool_resources": {\n        "BUSINESS_INTELLIGENCE_ANALYST": {\n            "execution_environment": {\n                "type": "warehouse",\n                "warehouse": "COMPUTE_WH",\n                "query_timeout": 300\n            },\n            "semantic_view": "CUSTOMER_INTELLIGENCE_DB.PUBLIC.CUSTOMER_BEHAVIOR_ANALYST"\n        },\n        "CHURN_RISK_PREDICTOR": {\n            "type": "procedure",\n            "identifier": "CUSTOMER_INTELLIGENCE_DB.PUBLIC.AI_PREDICT_CHURN_RISK",\n            "execution_environment": {\n                "type": "warehouse",\n                "warehouse": "COMPUTE_WH",\n                "query_timeout": 300\n            }\n        }\n    }\n}\n$$;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ECreate RESEARCH_AGENT\u003C/h3\u003E\n","\u003Cp\u003EThe Research Agent specializes in market intelligence and strategic analysis:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE AGENT RESEARCH_AGENT\n  COMMENT = 'Market intelligence, strategic analysis, and competitive insights specialist'\nFROM SPECIFICATION $$\n{\n    "models": {\n        "orchestration": "claude-4-sonnet"\n    },\n    "orchestration": {\n        "budget": {\n            "seconds": 60,\n            "tokens": 32000\n        }\n    },\n    "instructions": {\n        "system": "Strategic research and market intelligence specialist focused on executive-level business analysis, competitive positioning, and market opportunity identification.",\n        "response": "Always synthesize data into executive insights. Provide board-ready intelligence."\n    },\n    "tools": [\n        {\n            "tool_spec": {\n                "type": "cortex_analyst_text_to_sql",\n                "name": "STRATEGIC_MARKET_ANALYST",\n                "description": "Executive-level market intelligence platform for strategic analysis."\n            }\n        }\n    ],\n    "tool_resources": {\n        "STRATEGIC_MARKET_ANALYST": {\n            "execution_environment": {\n                "type": "warehouse",\n                "warehouse": "COMPUTE_WH",\n                "query_timeout": 300\n            },\n            "semantic_view": "CUSTOMER_INTELLIGENCE_DB.PUBLIC.STRATEGIC_RESEARCH_ANALYST"\n        }\n    }\n}\n$$;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EVerify Agents\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESHOW AGENTS IN SCHEMA SNOWFLAKE_INTELLIGENCE.AGENTS;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003EBuild LangGraph Supervisor Workflow\u003C/h2\u003E\n","\u003Cp\u003ENow we'll build the LangGraph workflow that orchestrates these Snowflake Cortex Agents. This section uses the Jupyter notebook from the companion repository.\u003C/p\u003E\n","\u003Ch3\u003ELocal Environment Setup\u003C/h3\u003E\n","\u003Cp\u003EFirst, set up your local Python environment:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003E# Clone the repository\ngit clone https://github.com/Snowflake-Labs/sfguide-build-and-evaluate-agents-with-snowflake-and-langgraph.git\ncd sfguide-build-and-evaluate-agents-with-snowflake-and-langgraph\n\n# Create virtual environment\npython -m venv venv\nsource venv/bin/activate  # On Windows: venv\\Scripts\\activate\n\n# Install dependencies\npip install -r requirements.txt\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EConfigure Environment Variables\u003C/h3\u003E\n","\u003Cp\u003ECreate a \u003Ccode\u003E.env\u003C/code\u003E file with your Snowflake credentials:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-env\"\u003ESNOWFLAKE_ACCOUNT=your_account_identifier\nSNOWFLAKE_USER=your_username\nSNOWFLAKE_PASSWORD=your_password\nSNOWFLAKE_DATABASE=CUSTOMER_INTELLIGENCE_DB\nSNOWFLAKE_SCHEMA=PUBLIC\nSNOWFLAKE_WAREHOUSE=COMPUTE_WH\nSNOWFLAKE_ROLE=your_role\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EOpen the Notebook\u003C/h3\u003E\n","\u003Cp\u003EOpen \u003Ccode\u003Ebuild_and_evaluate_agents_with_langgraph_and_snowflake.ipynb\u003C/code\u003E:\u003C/p\u003E\n","\u003Ch3\u003ENotebook Walkthrough\u003C/h3\u003E\n","\u003Cp\u003EThe notebook guides you through building the multi-agent workflow step-by-step:\u003C/p\u003E\n","\u003Ch4\u003ESteps 1-2: Import Dependencies and Define State\u003C/h4\u003E\n","\u003Cp\u003ERun the first few cells to import all necessary libraries including LangGraph, LangChain, and the Snowflake integration. The workflow uses an extended \u003Ccode\u003EMessagesState\u003C/code\u003E that tracks:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EThe execution plan (immutable after creation)\u003C/li\u003E\u003Cli\u003ECurrent step in the plan\u003C/li\u003E\u003Cli\u003EAgent outputs (accumulated from all agents)\u003C/li\u003E\u003Cli\u003EExecution errors (aggregated, not cascaded)\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch4\u003ESteps 3-5: Connect to Snowflake and Initialize Components\u003C/h4\u003E\n","\u003Cp\u003EThese cells establish your Snowflake session and initialize:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ESupervisor Model\u003C/strong\u003E: A \u003Ccode\u003EChatSnowflake\u003C/code\u003E instance using \u003Ccode\u003Eclaude-4-sonnet\u003C/code\u003E for routing and synthesis\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESpecialized Agents\u003C/strong\u003E: Three \u003Ccode\u003ESnowflakeCortexAgent\u003C/code\u003E instances (CONTENT_AGENT, DATA_ANALYST_AGENT, RESEARCH_AGENT)\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch4\u003ESteps 6-7: Create Prompts and Helper Functions\u003C/h4\u003E\n","\u003Cp\u003EThe supervisor uses two prompts:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EPlanning Prompt\u003C/strong\u003E: Creates an efficient, immutable execution plan with consolidated queries\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESynthesis Prompt\u003C/strong\u003E: Combines agent results into executive summaries\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EHelper functions handle message parsing, plan tracking, and context passing between agents.\u003C/p\u003E\n","\u003Ch4\u003ESteps 8-9: Define Node Functions\u003C/h4\u003E\n","\u003Cp\u003EEach node in the graph has a dedicated function:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003Esupervisor_node\u003C/strong\u003E: Handles planning (once) and synthesis (once) - the hub of the architecture\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003Econtent_agent_node\u003C/strong\u003E: Invokes the Content Agent for feedback and sentiment analysis\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003Edata_analyst_agent_node\u003C/strong\u003E: Invokes the Data Analyst Agent for metrics and churn analysis\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003Eresearch_agent_node\u003C/strong\u003E: Invokes the Research Agent for strategic analysis\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EAll agent nodes route back to the supervisor for clean hub-and-spoke coordination.\u003C/p\u003E\n","\u003Ch4\u003ESteps 10-12: Build and Compile the Graph\u003C/h4\u003E\n","\u003Cp\u003EAssemble the workflow using LangGraph's \u003Ccode\u003EStateGraph\u003C/code\u003E:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESTART → supervisor (planning) → Agent → supervisor (routing) → Agent → supervisor (synthesis) → END\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe compiled graph features:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EImmutable plan\u003C/strong\u003E: Created once, executed linearly without re-planning\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ENo LLM routing calls\u003C/strong\u003E: Supervisor uses simple plan lookup\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EConsolidated queries\u003C/strong\u003E: Single SQL aggregations instead of multiple calls\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAggregated error handling\u003C/strong\u003E: Errors collected, not cascaded\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch4\u003EStep 13: Test the Workflow\u003C/h4\u003E\n","\u003Cp\u003ERun sample queries to test the system:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EQuery Type\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EExpected Agent\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECustomer feedback, sentiment, complaints\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECONTENT_AGENT\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EMetrics, behavior, churn, analytics\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EDATA_ANALYST_AGENT\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EMarket research, competition, strategy\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ERESEARCH_AGENT\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Ch2\u003ERun with LangGraph Studio\u003C/h2\u003E\n","\u003Cp\u003E\u003Ca href=\"https://smith.langchain.com/studio\"\u003ELangGraph Studio\u003C/a\u003E provides a visual interface for developing, debugging, and testing your multi-agent workflow. It's an excellent alternative to running the notebook for interactive exploration.\u003C/p\u003E\n","\u003Ch3\u003EInstall LangGraph CLI\u003C/h3\u003E\n","\u003Cp\u003EAdd the LangGraph CLI to your environment:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Epip install langgraph-cli\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EConfigure Agent Location\u003C/h3\u003E\n","\u003Cp\u003EThe companion repository includes a \u003Ccode\u003Estudio_app.py\u003C/code\u003E file that connects to your Snowflake Cortex Agents. Open this file and verify the agent database and schema match your setup:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eagent_database = "SNOWFLAKE_INTELLIGENCE"  # Your agent database\nagent_schema = "AGENTS"                     # Your agent schema\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ELaunch LangGraph Studio\u003C/h3\u003E\n","\u003Cp\u003EFrom the repository directory, start the development server:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Elanggraph dev\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis opens LangGraph Studio in your browser at \u003Ca href=\"https://smith.langchain.com/studio/\"\u003Ehttps://smith.langchain.com/studio/\u003C/a\u003E.\u003C/p\u003E\n","\u003Ch3\u003EUsing LangGraph Studio\u003C/h3\u003E\n","\u003Cp\u003EIn the Studio interface:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\u003Cstrong\u003EEnter your query\u003C/strong\u003E in the "Messages" field\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EClick "Run"\u003C/strong\u003E to execute the workflow\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EWatch the execution\u003C/strong\u003E as it flows through the supervisor and agents\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EInspect state\u003C/strong\u003E at each node to debug or understand behavior\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003ESample Test Queries\u003C/h3\u003E\n","\u003Cp\u003ETry these business scenarios to test your multi-agent system:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EQuery Type\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EExample Query\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EContent Analysis\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E"Assess the churn risk for customers complaining about API issues."\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EData Analytics\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E"What's the average session duration for enterprise vs professional customers?"\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EStrategic Research\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E"What industries represent our best expansion opportunities?"\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EChurn Prediction\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E"Which customers are most likely to churn in the next 30 days?"\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESupport Analysis\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E"What are the most common support issues for enterprise customers?"\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Ch3\u003EOptional: Enable LangSmith Tracing\u003C/h3\u003E\n","\u003Cp\u003EFor enhanced observability, add LangSmith credentials to your \u003Ccode\u003E.env\u003C/code\u003E file:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-env\"\u003ELANGSMITH_API_KEY=your_langsmith_api_key\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis enables detailed tracing of all LLM calls and agent interactions within LangGraph Studio.\u003C/p\u003E\n","\u003Ch3\u003ETroubleshooting\u003C/h3\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EIssue\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ESolution\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E"401 Unauthorized"\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECheck your Snowflake credentials in \u003Ccode\u003E.env\u003C/code\u003E and verify your role has USAGE on the Cortex Agents\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E"No human message found"\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EEnter the query in the "Messages" field in LangGraph Studio\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAgent returns 0 customers\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EVerify Cortex Search service includes \u003Ccode\u003Ecustomer_id\u003C/code\u003E in ATTRIBUTES and demo data was loaded\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EConnection Issues\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EVerify \u003Ccode\u003ESNOWFLAKE_ACCOUNT\u003C/code\u003E format (e.g., \u003Ccode\u003Eorg-account\u003C/code\u003E or \u003Ccode\u003Eaccount.region\u003C/code\u003E)\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Ch2\u003EEvaluate with TruLens\u003C/h2\u003E\n","\u003Cp\u003E\u003Ca href=\"https://www.trulens.org/\"\u003ETruLens\u003C/a\u003E provides observability and evaluation for your multi-agent system. Continue following the notebook to set up metrics and run evaluations.\u003C/p\u003E\n","\u003Ch3\u003ETruLens Setup (Steps 14+ in Notebook)\u003C/h3\u003E\n","\u003Cp\u003EThe notebook walks you through:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003EImporting TruLens Dependencies\u003C/strong\u003E: Including \u003Ccode\u003ETruGraph\u003C/code\u003E, \u003Ccode\u003ESnowflakeConnector\u003C/code\u003E, and \u003Ccode\u003ECortex\u003C/code\u003E provider\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003EConnecting to Snowflake\u003C/strong\u003E: Creating a connector that stores evaluation data in Snowflake\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003EConfiguring Evaluation Metrics\u003C/strong\u003E:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EPlan Quality\u003C/strong\u003E: Evaluates how well the supervisor creates execution plans\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EPlan Adherence\u003C/strong\u003E: Checks if agents follow the plan\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EExecution Efficiency\u003C/strong\u003E: Measures workflow efficiency\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ELogical Consistency\u003C/strong\u003E: Verifies consistency across agent responses\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003EInstrumenting the App\u003C/strong\u003E: Wrapping the LangGraph graph with \u003Ccode\u003ETruGraph\u003C/code\u003E for observability\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003ERunning Evaluations\u003C/strong\u003E: Processing test queries and computing metrics\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003EEvaluation Metrics\u003C/h3\u003E\n","\u003Cp\u003EThe notebook configures these Goal-Plan-Action (GPA) alignment metrics:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EMetric\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EDescription\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EPlan Quality\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EHow well does the supervisor create actionable execution plans?\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EPlan Adherence\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EDo agents follow the specified plan?\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EExecution Efficiency\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EIs the workflow efficient (minimal redundant calls)?\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ELogical Consistency\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAre responses consistent across agents?\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAnswer Relevance\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EDoes the final answer address the original question?\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Ch3\u003ERunning the Evaluation\u003C/h3\u003E\n","\u003Cp\u003EFollow the notebook cells to:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EDefine evaluation inputs as LangGraph state dicts\u003C/li\u003E\u003Cli\u003ECreate a DataFrame with test queries\u003C/li\u003E\u003Cli\u003EConfigure and start the evaluation run\u003C/li\u003E\u003Cli\u003EWait for invocations to complete\u003C/li\u003E\u003Cli\u003ECompute all metrics\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EResults are stored in Snowflake and can be viewed in Snowsight.\u003C/p\u003E\n","\u003Ch2\u003EConclusion and Resources\u003C/h2\u003E\n","\u003Cp\u003ECongratulations! You've successfully built an \u003Cstrong\u003Eefficient multi-agent supervisor architecture\u003C/strong\u003E using LangGraph and Snowflake Cortex. This system demonstrates:\u003C/p\u003E\n","\u003Ch3\u003EWhat You Built\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003ESnowflake Infrastructure\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EDatabase with customer intelligence data\u003C/li\u003E\u003Cli\u003ECortex Search services for semantic search\u003C/li\u003E\u003Cli\u003ESemantic Views for text-to-SQL\u003C/li\u003E\u003Cli\u003ECustom AI UDFs for specialized analysis\u003C/li\u003E\u003Cli\u003EThree specialized Cortex Agents\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003EEfficient LangGraph Workflow\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EHub-and-spoke architecture with supervisor as central coordinator\u003C/li\u003E\u003Cli\u003EImmutable execution planning (plan once, execute linearly)\u003C/li\u003E\u003Cli\u003ENo LLM calls for routing (simple plan lookup)\u003C/li\u003E\u003Cli\u003EAggregated error handling (errors collected, not cascaded)\u003C/li\u003E\u003Cli\u003EState-based context passing between agents\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003EInteractive Development Environment\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ELangGraph Studio for visual workflow debugging\u003C/li\u003E\u003Cli\u003EReal-time state inspection at each node\u003C/li\u003E\u003Cli\u003ELangSmith tracing for detailed observability\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003EEvaluation Pipeline\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ETruLens integration for observability\u003C/li\u003E\u003Cli\u003ECustom metrics for goal-plan-action (GPA) alignment\u003C/li\u003E\u003Cli\u003ESnowflake-native evaluation storage\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003ENext Steps\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EAdd more agents\u003C/strong\u003E: Extend with specialized agents for sales, finance, or operations\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ETune prompts\u003C/strong\u003E: Use the evaluations to guide improvements to supervisor and agent prompts\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EBuild custom tools\u003C/strong\u003E: Create additional UDFs for domain-specific analysis\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EIterate with LangGraph Studio\u003C/strong\u003E: Use the visual debugger to rapidly test and refine your workflow\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ERelated Resources\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-build-and-evaluate-agents-with-snowflake-and-langgraph\"\u003EDeveloper Guide GitHub Repository\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/langchain-ai/langchain-snowflake\"\u003ELangChain Snowflake Integration\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E"],"description":"Build and evaluate a multi-agent supervisor system using LangGraph and Snowflake Cortex Agents","title":"Build and Evaluate Multi-Agent Systems with Snowflake and LangGraph","isDeveloperGuidesPage":false,":items":{},":itemsOrder":[],"elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"## Overview\n\nModern organizations need to combine multiple AI capabilities to handle complex business questions. This quickstart demonstrates how to build a **multi-agent supervisor architecture** using LangGraph and Snowflake Cortex Agents for customer intelligence, churn prediction, and business analytics.\n\nYou can also watch this use case in action on Youtube:\n\n[LangGraph And Snowflake Cortex AI: Exploring Multi-Agentic Workflows](https://www.youtube.com/watch?v=LsanBaHbCvE)\n\n### What is a Multi-Agent Supervisor Architecture?\n\nA multi-agent supervisor architecture uses a central \"supervisor\" LLM as a hub that coordinates specialized agents:\n\n1. **Plans** an execution strategy with explicit steps (once, immutable)\n2. **Routes** queries to specialized agents based on the plan\n3. **Coordinates** data flow between agents when multi-step analysis is needed\n4. **Synthesizes** results from agents into coherent executive summaries\n\n```\nUser Query → Supervisor (Plan) → Agent → Supervisor (Route) → Agent → Supervisor (Synthesize) → Summary\n```\n\n### Why LangGraph\n\n[LangGraph](https://github.com/langchain-ai/langgraph) provides low-level supporting infrastructure for any long-running, stateful workflow or agent. LangGraph is great for building durable, stateful agents across multiple systems.\n\nIf you're not building with LangGraph, try this [guide](https://www.snowflake.com/en/developers/guides/multi-agent-orchestration-snowflake-intelligence/) to build a multi-agent system entirely native to Snowflake.\n\n### Architecture Overview\n\n![Architecture](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-and-evaluate-agents-with-langgraph-and-snowflake/architecture.png)\n\n### What You'll Learn\n\n- How to set up Snowflake Cortex Agents with specialized tools\n- How to create Cortex Search services for semantic search\n- How to build Semantic Views for Cortex Analyst text-to-SQL\n- How to build a multi-agent supervisor workflow using LangGraph\n- How to run and debug your workflow with LangGraph Studio\n- How to evaluate agent performance using TruLens with Snowflake\n\n### What You'll Build\n\nA complete multi-agent customer intelligence system that:\n\n- Routes queries to specialized agents (Content, Data Analyst, Research)\n- Uses Cortex Search for semantic search across support tickets\n- Uses Cortex Analyst for natural language to SQL conversion\n- Uses custom AI UDFs for sentiment analysis and churn prediction\n- Can be run interactively via LangGraph Studio or programmatically via notebook\n- Evaluates responses using Agent Goal-Plan-Action alignment metrics via TruLens\n\n### Prerequisites\n\n- [Snowflake Account](https://signup.snowflake.com/) with Cortex AI features enabled\n- Python 3.9+ installed locally\n- [Git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git) installed\n- [LangGraph CLI](https://langchain-ai.github.io/langgraph/how-tos/local-studio/) (`pip install langgraph-cli`) for LangGraph Studio\n- [LangSmith Account](https://smith.langchain.com/) (optional, for tracing)\n- Basic familiarity with LangGraph and LangChain concepts (optional)\n\n## Setup Git Integration\n\nThe first step is to clone the repository into Snowflake using Git Integration. This gives you access to all the SQL scripts and demo data files.\n\n### Create Database and Clone Repository\n\nOpen Snowsight and create a new SQL Worksheet. Run the following SQL:\n\n```sql\nUSE ROLE ACCOUNTADMIN;\n\n-- Create database and schema\nCREATE DATABASE IF NOT EXISTS CUSTOMER_INTELLIGENCE_DB;\nUSE DATABASE CUSTOMER_INTELLIGENCE_DB;\nCREATE SCHEMA IF NOT EXISTS PUBLIC;\nUSE SCHEMA PUBLIC;\n\n-- Create API integration for GitHub\nCREATE API INTEGRATION IF NOT EXISTS github_api_integration\n    API_PROVIDER = git_https_api\n    API_ALLOWED_PREFIXES = ('https://github.com/Snowflake-Labs/')\n    ENABLED = TRUE;\n\n-- Clone the GitHub repository\nCREATE OR REPLACE GIT REPOSITORY customer_intelligence_demo\n    API_INTEGRATION = github_api_integration\n    ORIGIN = 'https://github.com/Snowflake-Labs/sfguide-build-and-evaluate-agents-with-snowflake-and-langgraph.git';\n\n-- Fetch latest from GitHub\nALTER GIT REPOSITORY customer_intelligence_demo FETCH;\n\n-- Verify repository contents\nLS @customer_intelligence_demo/branches/main/;\n```\n\nYou should see the SQL scripts and CSV files in the repository listing.\n\n### Run Setup Scripts\n\nAfter the Git integration is set up, you can run each SQL script directly from the repository. Navigate to **Data » Databases » CUSTOMER_INTELLIGENCE_DB » PUBLIC » Git Repositories » customer_intelligence_demo » branches » main » sql** and execute each script in order:\n\n| Order | Script | Purpose |\n|-------|--------|---------|\n| 1 | `01_setup_database_and_load_data.sql` | Creates tables and loads CSV data |\n| 2 | `02_setup_cortex_search.sql` | Creates Cortex Search services |\n| 3 | `03_setup_semantic_views.sql` | Creates Semantic Views for Cortex Analyst |\n| 4 | `04_setup_udfs.sql` | Creates AI UDFs (tools for agents) |\n| 5 | `05_setup_cortex_agents.sql` | Creates the three Cortex Agents |\n\n\u003E **Note:** Run scripts in order as later scripts depend on earlier ones.\n\nThe following sections explain what each script does in detail.\n\n## Setup Database and Load Data\n\nThe `01_setup_database_and_load_data.sql` script creates the tables and loads demo data. Here's what it does:\n\n### Create Tables\n\nCreate the four tables that will store customer data:\n\n```sql\n-- CUSTOMERS TABLE\nCREATE OR REPLACE TABLE CUSTOMERS (\n    customer_id VARCHAR(50) PRIMARY KEY,\n    signup_date DATE NOT NULL,\n    plan_type VARCHAR(20) NOT NULL,\n    company_size VARCHAR(20) NOT NULL,\n    industry VARCHAR(30) NOT NULL,\n    status VARCHAR(20) NOT NULL DEFAULT 'active',\n    monthly_revenue DECIMAL(10,2),\n    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP()\n);\n\n-- USAGE_EVENTS TABLE\nCREATE OR REPLACE TABLE USAGE_EVENTS (\n    event_id VARCHAR(50) PRIMARY KEY,\n    customer_id VARCHAR(50) NOT NULL,\n    event_date DATE NOT NULL,\n    feature_used VARCHAR(50) NOT NULL,\n    session_duration_minutes INTEGER NOT NULL,\n    actions_count INTEGER NOT NULL,\n    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP()\n);\n\n-- SUPPORT_TICKETS TABLE\nCREATE OR REPLACE TABLE SUPPORT_TICKETS (\n    ticket_id VARCHAR(50) PRIMARY KEY,\n    customer_id VARCHAR(50) NOT NULL,\n    created_date DATE NOT NULL,\n    category VARCHAR(30) NOT NULL,\n    priority VARCHAR(10) NOT NULL,\n    status VARCHAR(20) NOT NULL,\n    resolution_time_hours INTEGER,\n    satisfaction_score INTEGER,\n    ticket_text TEXT NOT NULL,\n    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP()\n);\n\n-- CHURN_EVENTS TABLE\nCREATE OR REPLACE TABLE CHURN_EVENTS (\n    churn_id VARCHAR(50) PRIMARY KEY,\n    customer_id VARCHAR(50) NOT NULL,\n    churn_date DATE NOT NULL,\n    churn_reason VARCHAR(50) NOT NULL,\n    days_since_signup INTEGER NOT NULL,\n    final_plan_type VARCHAR(20),\n    final_monthly_revenue DECIMAL(10,2),\n    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP()\n);\n```\n\n### Step 3: Load Demo Data\n\nLoad the CSV files from the Git repository:\n\n```sql\n-- Create file format\nCREATE OR REPLACE FILE FORMAT csv_format\n    TYPE = CSV \n    SKIP_HEADER = 1 \n    FIELD_OPTIONALLY_ENCLOSED_BY = '\"'\n    NULL_IF = ('', 'NULL', 'null') \n    EMPTY_FIELD_AS_NULL = TRUE;\n\n-- Load CUSTOMERS\nINSERT INTO CUSTOMERS (customer_id, signup_date, plan_type, company_size, industry, status, monthly_revenue)\nSELECT $1,$2,$3,$4,$5,$6,$7 \nFROM @customer_intelligence_demo/branches/main/demo_customers.csv (FILE_FORMAT=\u003Ecsv_format);\n\n-- Load USAGE_EVENTS  \nINSERT INTO USAGE_EVENTS (event_id, customer_id, event_date, feature_used, session_duration_minutes, actions_count)\nSELECT $1,$2,$3,$4,$5,$6 \nFROM @customer_intelligence_demo/branches/main/demo_usage_events.csv (FILE_FORMAT=\u003Ecsv_format);\n\n-- Load SUPPORT_TICKETS\nINSERT INTO SUPPORT_TICKETS (ticket_id, customer_id, created_date, category, priority, status, resolution_time_hours, satisfaction_score, ticket_text)\nSELECT $1,$2,$3,$4,$5,$6,$7,$8,$9 \nFROM @customer_intelligence_demo/branches/main/demo_support_tickets.csv (FILE_FORMAT=\u003Ecsv_format);\n\n-- Load CHURN_EVENTS\nINSERT INTO CHURN_EVENTS (churn_id, customer_id, churn_date, churn_reason, days_since_signup, final_plan_type, final_monthly_revenue)\nSELECT $1,$2,$3,$4,$5,$6,$7 \nFROM @customer_intelligence_demo/branches/main/demo_churn_events.csv (FILE_FORMAT=\u003Ecsv_format);\n```\n\n### Step 4: Verify Data Loaded\n\n```sql\nSELECT 'CUSTOMERS' as table_name, COUNT(*) as row_count FROM CUSTOMERS\nUNION ALL SELECT 'USAGE_EVENTS', COUNT(*) FROM USAGE_EVENTS\nUNION ALL SELECT 'SUPPORT_TICKETS', COUNT(*) FROM SUPPORT_TICKETS\nUNION ALL SELECT 'CHURN_EVENTS', COUNT(*) FROM CHURN_EVENTS;\n```\n\nYou should see data in all four tables.\n\n## Setup Cortex Search Services\n\nCortex Search provides hybrid search (semantic + keyword) capabilities for unstructured text data. We'll create search services that the agents will use to find relevant customer feedback and support tickets.\n\n### Create Support Tickets Search Service\n\nThis service enables semantic search across all customer support tickets:\n\n```sql\nUSE DATABASE CUSTOMER_INTELLIGENCE_DB;\nUSE SCHEMA PUBLIC;\n\nCREATE OR REPLACE CORTEX SEARCH SERVICE SUPPORT_TICKETS_SEARCH\nON ticket_text\nATTRIBUTES customer_id, ticket_id, category, priority, status, created_date\nWAREHOUSE = COMPUTE_WH\nTARGET_LAG = '1 hour'\nAS (\n    SELECT \n        ticket_id,\n        customer_id,\n        category,\n        priority,\n        status,\n        created_date,\n        ticket_text\n    FROM SUPPORT_TICKETS\n);\n```\n\n### Create Customers Search Service (Optional)\n\n```sql\nCREATE OR REPLACE CORTEX SEARCH SERVICE CUSTOMERS_SEARCH\nON industry\nATTRIBUTES customer_id, plan_type, company_size, status, monthly_revenue\nWAREHOUSE = COMPUTE_WH\nTARGET_LAG = '1 hour'\nAS (\n    SELECT \n        customer_id,\n        plan_type,\n        company_size,\n        industry,\n        status,\n        monthly_revenue\n    FROM CUSTOMERS\n);\n```\n\n### Verify Search Services\n\n```sql\nSHOW CORTEX SEARCH SERVICES IN SCHEMA CUSTOMER_INTELLIGENCE_DB.PUBLIC;\n```\n\n## Setup Semantic Views\n\n[Semantic Views](https://docs.snowflake.com/en/user-guide/views-semantic/overview) power Cortex Analyst, enabling natural language to SQL conversion. They define the schema, relationships, and business metrics that the AI uses to generate accurate SQL queries.\n\n### Create Customer Behavior Analyst View\n\nThis semantic view is used by the DATA_ANALYST_AGENT for customer behavior analytics:\n\n```sql\nCREATE OR REPLACE SEMANTIC VIEW CUSTOMER_BEHAVIOR_ANALYST\n  TABLES (\n    CUSTOMERS AS CUSTOMER_INTELLIGENCE_DB.PUBLIC.CUSTOMERS\n      PRIMARY KEY (CUSTOMER_ID)\n      COMMENT = 'Customer master data',\n    USAGE_EVENTS AS CUSTOMER_INTELLIGENCE_DB.PUBLIC.USAGE_EVENTS\n      COMMENT = 'Customer usage events',\n    SUPPORT_TICKETS AS CUSTOMER_INTELLIGENCE_DB.PUBLIC.SUPPORT_TICKETS\n      COMMENT = 'Support tickets',\n    CHURN_EVENTS AS CUSTOMER_INTELLIGENCE_DB.PUBLIC.CHURN_EVENTS\n      COMMENT = 'Churn events'\n  )\n  RELATIONSHIPS (\n    CUSTOMER_TO_USAGE AS USAGE_EVENTS (CUSTOMER_ID) REFERENCES CUSTOMERS (CUSTOMER_ID),\n    CUSTOMER_TO_SUPPORT AS SUPPORT_TICKETS (CUSTOMER_ID) REFERENCES CUSTOMERS (CUSTOMER_ID),\n    CUSTOMER_TO_CHURN AS CHURN_EVENTS (CUSTOMER_ID) REFERENCES CUSTOMERS (CUSTOMER_ID)\n  )\n  FACTS (\n    CUSTOMERS.MONTHLY_REVENUE AS CUSTOMERS.MONTHLY_REVENUE\n      WITH SYNONYMS = ('MRR', 'monthly recurring revenue')\n      COMMENT = 'Monthly revenue per customer',\n    USAGE_EVENTS.SESSION_DURATION_MINUTES AS USAGE_EVENTS.SESSION_DURATION_MINUTES\n      WITH SYNONYMS = ('session length')\n      COMMENT = 'Session duration in minutes',\n    USAGE_EVENTS.ACTIONS_COUNT AS USAGE_EVENTS.ACTIONS_COUNT\n      COMMENT = 'Number of actions in session',\n    SUPPORT_TICKETS.SATISFACTION_SCORE AS SUPPORT_TICKETS.SATISFACTION_SCORE\n      WITH SYNONYMS = ('CSAT')\n      COMMENT = 'Customer satisfaction score'\n  )\n  DIMENSIONS (\n    CUSTOMERS.CUSTOMER_ID AS CUSTOMERS.CUSTOMER_ID\n      WITH SYNONYMS = ('customer identifier', 'account id')\n      COMMENT = 'Unique customer identifier',\n    CUSTOMERS.INDUSTRY AS CUSTOMERS.INDUSTRY\n      WITH SYNONYMS = ('sector', 'vertical')\n      COMMENT = 'Customer industry',\n    CUSTOMERS.PLAN_TYPE AS CUSTOMERS.PLAN_TYPE\n      WITH SYNONYMS = ('subscription', 'tier')\n      COMMENT = 'Subscription plan level'\n  )\n  METRICS (\n    CUSTOMERS.TOTAL_CUSTOMERS AS COUNT(CUSTOMERS.CUSTOMER_ID)\n      WITH SYNONYMS = ('customer count')\n      COMMENT = 'Total number of customers',\n    CUSTOMERS.ARPU AS AVG(CUSTOMERS.MONTHLY_REVENUE)\n      WITH SYNONYMS = ('average revenue per user')\n      COMMENT = 'Average revenue per customer',\n    USAGE_EVENTS.AVG_SESSION_DURATION AS AVG(USAGE_EVENTS.SESSION_DURATION_MINUTES)\n      COMMENT = 'Average session duration'\n  )\n  COMMENT = 'Customer behavior analytics for usage patterns, churn analysis, and retention insights';\n```\n\n### Create Strategic Research Analyst View\n\nThis semantic view is used by the RESEARCH_AGENT for market intelligence:\n\n```sql\nCREATE OR REPLACE SEMANTIC VIEW STRATEGIC_RESEARCH_ANALYST\n  TABLES (\n    CUSTOMERS AS CUSTOMER_INTELLIGENCE_DB.PUBLIC.CUSTOMERS\n      PRIMARY KEY (CUSTOMER_ID)\n      COMMENT = 'Customer master data',\n    USAGE_EVENTS AS CUSTOMER_INTELLIGENCE_DB.PUBLIC.USAGE_EVENTS\n      COMMENT = 'Usage data',\n    SUPPORT_TICKETS AS CUSTOMER_INTELLIGENCE_DB.PUBLIC.SUPPORT_TICKETS\n      COMMENT = 'Support data',\n    CHURN_EVENTS AS CUSTOMER_INTELLIGENCE_DB.PUBLIC.CHURN_EVENTS\n      COMMENT = 'Churn data'\n  )\n  RELATIONSHIPS (\n    CUSTOMER_TO_USAGE AS USAGE_EVENTS (CUSTOMER_ID) REFERENCES CUSTOMERS (CUSTOMER_ID),\n    CUSTOMER_TO_SUPPORT AS SUPPORT_TICKETS (CUSTOMER_ID) REFERENCES CUSTOMERS (CUSTOMER_ID),\n    CUSTOMER_TO_CHURN AS CHURN_EVENTS (CUSTOMER_ID) REFERENCES CUSTOMERS (CUSTOMER_ID)\n  )\n  METRICS (\n    CUSTOMERS.TOTAL_MRR AS SUM(CUSTOMERS.MONTHLY_REVENUE)\n      WITH SYNONYMS = ('total recurring revenue')\n      COMMENT = 'Total monthly recurring revenue',\n    CHURN_EVENTS.LOST_REVENUE AS SUM(CHURN_EVENTS.FINAL_MONTHLY_REVENUE)\n      COMMENT = 'Total lost revenue'\n  )\n  COMMENT = 'Strategic research and market intelligence for executive-level business analysis';\n```\n\n### Verify Semantic Views\n\n```sql\nSHOW SEMANTIC VIEWS IN SCHEMA CUSTOMER_INTELLIGENCE_DB.PUBLIC;\n```\n\n## Setup Custom AI UDFs\n\nCustom UDFs (User-Defined Functions) extend agent capabilities with specialized AI analysis. These functions use Snowflake Cortex AI to perform sentiment analysis, behavior analysis, and strategic insights.\n\n### Create Customer Content Analyzer UDF\n\nUsed by the CONTENT_AGENT for sentiment and feedback analysis:\n\n```sql\nCREATE OR REPLACE FUNCTION AI_ANALYZE_CUSTOMER_CONTENT(\n    \"CUSTOMER_IDS_STRING\" VARCHAR, \n    \"ANALYSIS_TYPE\" VARCHAR DEFAULT 'recent_support_tickets'\n)\nRETURNS VARIANT\nLANGUAGE SQL\nAS '\nWITH parsed_customer_ids AS (\n  SELECT TRIM(VALUE) as customer_id\n  FROM TABLE(SPLIT_TO_TABLE(customer_ids_string, '',''))\n),\ntarget_tickets AS (\n  SELECT \n    st.customer_id,\n    st.ticket_text,\n    st.category,\n    st.priority,\n    st.satisfaction_score,\n    st.created_date\n  FROM support_tickets st\n  INNER JOIN parsed_customer_ids pci ON st.customer_id = pci.customer_id\n  ORDER BY st.created_date DESC\n  LIMIT 10\n),\ncontent_summary AS (\n  SELECT \n    COUNT(*) as total_tickets,\n    COUNT(DISTINCT customer_id) as customers_with_tickets,\n    ROUND(AVG(satisfaction_score), 2) as avg_satisfaction,\n    COUNT(CASE WHEN priority IN (''high'', ''critical'') THEN 1 END) as urgent_tickets,\n    LISTAGG(DISTINCT category, '', '') as ticket_categories,\n    SUBSTRING(LISTAGG(ticket_text, '' | ''), 1, 1000) as combined_ticket_text\n  FROM target_tickets\n),\nai_content_analysis AS (\n  SELECT \n    cs.*,\n    CASE \n      WHEN cs.total_tickets \u003E 0 THEN\n        AI_COMPLETE(\n          ''claude-3-5-sonnet'',\n          CONCAT(\n            ''Analyze customer feedback: '', analysis_type, ''\\\\n'',\n            ''Tickets: '', cs.total_tickets, '' ('', cs.urgent_tickets, '' urgent)\\\\n'',\n            ''Satisfaction: '', cs.avg_satisfaction, ''/5\\\\n'',\n            ''Categories: '', cs.ticket_categories, ''\\\\n'',\n            ''Content: '', cs.combined_ticket_text, ''\\\\n\\\\n'',\n            ''JSON: {\"sentiment\":\"positive/neutral/negative\",\"urgency\":\"low/medium/high\",\"key_issue\":\"main_problem\",\"action_needed\":\"recommendation\"}''\n          )\n        )\n      ELSE NULL\n    END as ai_content_insights\n  FROM content_summary cs\n)\nSELECT OBJECT_CONSTRUCT(\n    ''analysis_type'', analysis_type,\n    ''customer_ids_input'', customer_ids_string,\n    ''content_metrics'', OBJECT_CONSTRUCT(\n      ''total_tickets'', total_tickets,\n      ''customers_affected'', customers_with_tickets,\n      ''avg_satisfaction'', avg_satisfaction,\n      ''urgent_tickets'', urgent_tickets\n    ),\n    ''ai_content_insights'', TRY_PARSE_JSON(ai_content_insights),\n    ''analysis_timestamp'', CURRENT_TIMESTAMP()\n)::VARIANT as result\nFROM ai_content_analysis\n';\n```\n\n### Create Customer Segment Analyzer UDF\n\nUsed by the DATA_ANALYST_AGENT to analyze customer behavior, metrics and analytics.\n\n```sql\nCREATE OR REPLACE FUNCTION AI_ANALYZE_CUSTOMER_SEGMENT(\"CUSTOMER_IDS_STRING\" VARCHAR, \"SEGMENT_NAME\" VARCHAR DEFAULT 'high_value_customers')\nRETURNS VARIANT\nLANGUAGE SQL\nAS '\nWITH parsed_customer_ids AS (\n  -- Parse comma-separated string into individual customer IDs\n  SELECT \n    TRIM(VALUE) as customer_id\n  FROM TABLE(SPLIT_TO_TABLE(customer_ids_string, '',''))\n),\ntarget_customers AS (\n  SELECT \n    c.customer_id,\n    c.plan_type,\n    c.monthly_revenue,\n    c.company_size,\n    c.industry,\n    c.status,\n    DATEDIFF(''day'', c.signup_date, CURRENT_DATE()) as customer_age_days\n  FROM customers c\n  INNER JOIN parsed_customer_ids pci ON c.customer_id = pci.customer_id\n),\nsegment_usage AS (\n  SELECT \n    COUNT(DISTINCT u.customer_id) as active_users,\n    COUNT(*) as total_events,\n    ROUND(AVG(u.session_duration_minutes), 2) as avg_session_duration,\n    SUM(u.actions_count) as total_actions\n  FROM usage_events u\n  INNER JOIN parsed_customer_ids pci ON u.customer_id = pci.customer_id\n  WHERE u.event_date \u003E= CURRENT_DATE() - 365 - 90  -- Handle data offset\n),\nsegment_support AS (\n  SELECT \n    COUNT(*) as total_tickets,\n    ROUND(AVG(satisfaction_score), 2) as avg_satisfaction,\n    COUNT(CASE WHEN priority IN (''high'', ''critical'') THEN 1 END) as high_priority_tickets\n  FROM support_tickets s\n  INNER JOIN parsed_customer_ids pci ON s.customer_id = pci.customer_id\n  WHERE s.created_date \u003E= CURRENT_DATE() - 365 - 90  -- Handle data offset\n),\nsegment_summary AS (\n  SELECT \n    tc.customer_count,\n    tc.total_mrr,\n    tc.avg_revenue,\n    tc.plan_mix,\n    su.active_users,\n    su.total_events,\n    su.avg_session_duration,\n    ss.total_tickets,\n    ss.avg_satisfaction,\n    -- AI analysis with proper input validation\n    CASE \n      WHEN tc.customer_count \u003E 0 THEN\n        AI_COMPLETE(\n          ''claude-3-5-sonnet'',\n          CONCAT(\n            ''Analyze customer segment and respond with valid JSON only:\\\\n'',\n            ''Segment: '', segment_name, ''\\\\n'',\n            ''Customers: '', tc.customer_count, ''\\\\n'',\n            ''MRR: $'', tc.total_mrr, '' (avg $'', tc.avg_revenue, '')\\\\n'',\n            ''Usage: '', COALESCE(su.active_users, 0), '' active, '', COALESCE(su.total_events, 0), '' events\\\\n'',\n            ''Support: '', COALESCE(ss.total_tickets, 0), '' tickets, '', COALESCE(ss.avg_satisfaction, 0), ''/5 satisfaction\\\\n\\\\n'',\n            ''JSON: {\"risk_level\":\"low/medium/high/critical\",\"key_insight\":\"main_finding\",\"recommendation\":\"action\"}''\n          )\n        )\n      ELSE NULL\n    END as ai_segment_analysis\n  FROM (\n    SELECT \n      COUNT(*) as customer_count,\n      SUM(monthly_revenue) as total_mrr,\n      ROUND(AVG(monthly_revenue), 0) as avg_revenue,\n      LISTAGG(DISTINCT plan_type, '', '') as plan_mix\n    FROM target_customers\n  ) tc\n  CROSS JOIN segment_usage su\n  CROSS JOIN segment_support ss\n)\nSELECT \n  (OBJECT_CONSTRUCT(\n    ''segment_name'', segment_name,\n    ''customer_ids_input'', customer_ids_string,\n    ''segment_metrics'', OBJECT_CONSTRUCT(\n      ''customer_count'', customer_count,\n      ''total_mrr'', total_mrr,\n      ''avg_revenue_per_customer'', avg_revenue,\n      ''plan_mix'', plan_mix,\n      ''active_users'', COALESCE(active_users, 0),\n      ''avg_session_duration'', COALESCE(avg_session_duration, 0),\n      ''support_satisfaction'', COALESCE(avg_satisfaction, 0)\n    ),\n    ''ai_insights'', TRY_PARSE_JSON(ai_segment_analysis),\n    ''raw_ai_response'', ai_segment_analysis,\n    ''processing_mode'', ''agent_compatible_analysis'',\n    ''node_name'', ''ai_customer_segment_analyzer'',\n    ''analysis_timestamp'', CURRENT_TIMESTAMP()\n  ))::VARIANT as result\nFROM segment_summary\n';\n```\n\n### Create Churn Risk Predictor UDF\n\nUsed by the DATA_ANALYST_AGENT for engagement and churn risk prediction:\n\n```sql\nCREATE OR REPLACE FUNCTION AI_PREDICT_CHURN_RISK(\n    \"CUSTOMER_IDS_STRING\" VARCHAR, \n    \"ANALYSIS_DAYS\" NUMBER(38,0) DEFAULT 30\n)\nRETURNS VARIANT\nLANGUAGE SQL\nAS '\nWITH parsed_customer_ids AS (\n  SELECT TRIM(VALUE) as customer_id\n  FROM TABLE(SPLIT_TO_TABLE(customer_ids_string, '',''))\n),\ntarget_customers AS (\n  SELECT \n    c.customer_id,\n    c.plan_type,\n    c.monthly_revenue,\n    c.company_size,\n    c.industry,\n    c.status\n  FROM customers c\n  INNER JOIN parsed_customer_ids pci ON c.customer_id = pci.customer_id\n),\nbehavior_metrics AS (\n  SELECT \n    COUNT(DISTINCT u.customer_id) as active_customers,\n    COUNT(*) as total_events,\n    ROUND(AVG(u.session_duration_minutes), 2) as avg_session_duration,\n    SUM(u.actions_count) as total_actions\n  FROM usage_events u\n  INNER JOIN parsed_customer_ids pci ON u.customer_id = pci.customer_id\n),\nanalysis_result AS (\n  SELECT \n    tc.customer_count,\n    tc.total_mrr,\n    bm.active_customers,\n    bm.total_events,\n    bm.avg_session_duration,\n    CASE \n      WHEN tc.customer_count \u003E 0 THEN\n        AI_COMPLETE(\n          ''claude-3-5-sonnet'',\n          CONCAT(\n            ''Analyze customer behavior:\\\\n'',\n            ''Customers: '', tc.customer_count, '', MRR: $'', tc.total_mrr, ''\\\\n'',\n            ''Active: '', COALESCE(bm.active_customers, 0), ''\\\\n'',\n            ''Events: '', COALESCE(bm.total_events, 0), ''\\\\n'',\n            ''JSON: {\"engagement\":\"high/medium/low\",\"churn_risk\":\"low/medium/high\",\"recommendation\":\"action\"}''\n          )\n        )\n      ELSE NULL\n    END as ai_behavior_insights\n  FROM (\n    SELECT \n      COUNT(*) as customer_count,\n      SUM(monthly_revenue) as total_mrr\n    FROM target_customers\n  ) tc\n  CROSS JOIN behavior_metrics bm\n)\nSELECT OBJECT_CONSTRUCT(\n    ''customer_ids_input'', customer_ids_string,\n    ''behavior_metrics'', OBJECT_CONSTRUCT(\n      ''customer_count'', customer_count,\n      ''total_mrr'', total_mrr,\n      ''active_customers'', COALESCE(active_customers, 0),\n      ''avg_session_duration'', COALESCE(avg_session_duration, 0)\n    ),\n    ''ai_behavior_insights'', TRY_PARSE_JSON(ai_behavior_insights),\n    ''analysis_timestamp'', CURRENT_TIMESTAMP()\n)::VARIANT as result\nFROM analysis_result\n';\n```\n\n### Verify UDFs\n\n```sql\nSHOW USER FUNCTIONS IN SCHEMA CUSTOMER_INTELLIGENCE_DB.PUBLIC;\n```\n\n## Create Cortex Agents\n\nNow we create the three specialized [Cortex Agents](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents) that will be orchestrated by the LangGraph supervisor. Each agent has specific tools and instructions for their domain.\n\n### Create Agent Database\n\n```sql\nCREATE DATABASE IF NOT EXISTS SNOWFLAKE_INTELLIGENCE;\nUSE DATABASE SNOWFLAKE_INTELLIGENCE;\nCREATE SCHEMA IF NOT EXISTS AGENTS;\nUSE SCHEMA AGENTS;\n```\n\n### Create CONTENT_AGENT\n\nThe Content Agent specializes in customer feedback, sentiment analysis, and support intelligence:\n\n```sql\nCREATE OR REPLACE AGENT CONTENT_AGENT\n  COMMENT = 'Customer feedback, sentiment analysis, and communication intelligence specialist'\nFROM SPECIFICATION $$\n{\n    \"models\": {\n        \"orchestration\": \"claude-4-sonnet\"\n    },\n    \"orchestration\": {\n        \"budget\": {\n            \"seconds\": 60,\n            \"tokens\": 32000\n        }\n    },\n    \"instructions\": {\n        \"system\": \"The Content Agent specializes in analyzing customer feedback, support interactions, and satisfaction trends. It combines targeted customer analysis with broad pattern recognition to distinguish between isolated incidents and systemic issues.\",\n        \"response\": \"Always synthesize data into executive insights. Provide maximum 3-5 key findings with business impact.\"\n    },\n    \"tools\": [\n        {\n            \"tool_spec\": {\n                \"type\": \"cortex_search\",\n                \"name\": \"CUSTOMER_FEEDBACK_SEARCH\",\n                \"description\": \"Semantic search across all customer support tickets and feedback.\"\n            }\n        },\n        {\n            \"tool_spec\": {\n                \"type\": \"generic\",\n                \"name\": \"CUSTOMER_CONTENT_ANALYZER\",\n                \"description\": \"AI-powered analysis of specific customer feedback and support interactions.\",\n                \"input_schema\": {\n                    \"type\": \"object\",\n                    \"properties\": {\n                        \"customer_ids_string\": {\n                            \"type\": \"string\",\n                            \"description\": \"Comma-separated list of customer IDs to analyze\"\n                        }\n                    },\n                    \"required\": [\"customer_ids_string\"]\n                }\n            }\n        }\n    ],\n    \"tool_resources\": {\n        \"CUSTOMER_FEEDBACK_SEARCH\": {\n            \"execution_environment\": {\n                \"type\": \"warehouse\",\n                \"warehouse\": \"COMPUTE_WH\",\n                \"query_timeout\": 300\n            },\n            \"search_service\": \"CUSTOMER_INTELLIGENCE_DB.PUBLIC.SUPPORT_TICKETS_SEARCH\",\n            \"id_column\": \"ticket_id\",\n            \"title_column\": \"ticket_text\"\n        },\n        \"CUSTOMER_CONTENT_ANALYZER\": {\n            \"type\": \"procedure\",\n            \"identifier\": \"CUSTOMER_INTELLIGENCE_DB.PUBLIC.AI_ANALYZE_CUSTOMER_CONTENT\",\n            \"execution_environment\": {\n                \"type\": \"warehouse\",\n                \"warehouse\": \"COMPUTE_WH\",\n                \"query_timeout\": 300\n            }\n        }\n    }\n}\n$$;\n```\n\n### Create DATA_ANALYST_AGENT\n\nThe Data Analyst Agent specializes in customer behavior, metrics, and predictive analytics:\n\n```sql\nCREATE OR REPLACE AGENT DATA_ANALYST_AGENT\n  COMMENT = 'Customer behavior, business metrics, and predictive analytics specialist'\nFROM SPECIFICATION $$\n{\n    \"models\": {\n        \"orchestration\": \"claude-4-sonnet\"\n    },\n    \"orchestration\": {\n        \"budget\": {\n            \"seconds\": 60,\n            \"tokens\": 32000\n        }\n    },\n    \"instructions\": {\n        \"system\": \"Expert customer behavior analyst specializing in data-driven insights about customer engagement, usage patterns, churn prediction, and retention strategies.\",\n        \"response\": \"Always synthesize data into executive insights. Never return raw data tables.\"\n    },\n    \"tools\": [\n        {\n            \"tool_spec\": {\n                \"type\": \"cortex_analyst_text_to_sql\",\n                \"name\": \"BUSINESS_INTELLIGENCE_ANALYST\",\n                \"description\": \"Natural language to SQL for comprehensive customer behavior analytics.\"\n            }\n        },\n        {\n            \"tool_spec\": {\n                \"type\": \"generic\",\n                \"name\": \"CHURN_RISK_PREDICTOR\",\n                \"description\": \"AI-powered churn risk prediction and engagement analysis.\",\n                \"input_schema\": {\n                    \"type\": \"object\",\n                    \"properties\": {\n                        \"customer_ids_string\": {\n                            \"type\": \"string\",\n                            \"description\": \"Comma-separated list of customer IDs to analyze\"\n                        }\n                    },\n                    \"required\": [\"customer_ids_string\"]\n                }\n            }\n        }\n    ],\n    \"tool_resources\": {\n        \"BUSINESS_INTELLIGENCE_ANALYST\": {\n            \"execution_environment\": {\n                \"type\": \"warehouse\",\n                \"warehouse\": \"COMPUTE_WH\",\n                \"query_timeout\": 300\n            },\n            \"semantic_view\": \"CUSTOMER_INTELLIGENCE_DB.PUBLIC.CUSTOMER_BEHAVIOR_ANALYST\"\n        },\n        \"CHURN_RISK_PREDICTOR\": {\n            \"type\": \"procedure\",\n            \"identifier\": \"CUSTOMER_INTELLIGENCE_DB.PUBLIC.AI_PREDICT_CHURN_RISK\",\n            \"execution_environment\": {\n                \"type\": \"warehouse\",\n                \"warehouse\": \"COMPUTE_WH\",\n                \"query_timeout\": 300\n            }\n        }\n    }\n}\n$$;\n```\n\n### Create RESEARCH_AGENT\n\nThe Research Agent specializes in market intelligence and strategic analysis:\n\n```sql\nCREATE OR REPLACE AGENT RESEARCH_AGENT\n  COMMENT = 'Market intelligence, strategic analysis, and competitive insights specialist'\nFROM SPECIFICATION $$\n{\n    \"models\": {\n        \"orchestration\": \"claude-4-sonnet\"\n    },\n    \"orchestration\": {\n        \"budget\": {\n            \"seconds\": 60,\n            \"tokens\": 32000\n        }\n    },\n    \"instructions\": {\n        \"system\": \"Strategic research and market intelligence specialist focused on executive-level business analysis, competitive positioning, and market opportunity identification.\",\n        \"response\": \"Always synthesize data into executive insights. Provide board-ready intelligence.\"\n    },\n    \"tools\": [\n        {\n            \"tool_spec\": {\n                \"type\": \"cortex_analyst_text_to_sql\",\n                \"name\": \"STRATEGIC_MARKET_ANALYST\",\n                \"description\": \"Executive-level market intelligence platform for strategic analysis.\"\n            }\n        }\n    ],\n    \"tool_resources\": {\n        \"STRATEGIC_MARKET_ANALYST\": {\n            \"execution_environment\": {\n                \"type\": \"warehouse\",\n                \"warehouse\": \"COMPUTE_WH\",\n                \"query_timeout\": 300\n            },\n            \"semantic_view\": \"CUSTOMER_INTELLIGENCE_DB.PUBLIC.STRATEGIC_RESEARCH_ANALYST\"\n        }\n    }\n}\n$$;\n```\n\n### Verify Agents\n\n```sql\nSHOW AGENTS IN SCHEMA SNOWFLAKE_INTELLIGENCE.AGENTS;\n```\n\n## Build LangGraph Supervisor Workflow\n\nNow we'll build the LangGraph workflow that orchestrates these Snowflake Cortex Agents. This section uses the Jupyter notebook from the companion repository.\n\n### Local Environment Setup\n\nFirst, set up your local Python environment:\n\n```bash\n# Clone the repository\ngit clone https://github.com/Snowflake-Labs/sfguide-build-and-evaluate-agents-with-snowflake-and-langgraph.git\ncd sfguide-build-and-evaluate-agents-with-snowflake-and-langgraph\n\n# Create virtual environment\npython -m venv venv\nsource venv/bin/activate  # On Windows: venv\\Scripts\\activate\n\n# Install dependencies\npip install -r requirements.txt\n```\n\n### Configure Environment Variables\n\nCreate a `.env` file with your Snowflake credentials:\n\n```env\nSNOWFLAKE_ACCOUNT=your_account_identifier\nSNOWFLAKE_USER=your_username\nSNOWFLAKE_PASSWORD=your_password\nSNOWFLAKE_DATABASE=CUSTOMER_INTELLIGENCE_DB\nSNOWFLAKE_SCHEMA=PUBLIC\nSNOWFLAKE_WAREHOUSE=COMPUTE_WH\nSNOWFLAKE_ROLE=your_role\n```\n\n### Open the Notebook\n\nOpen `build_and_evaluate_agents_with_langgraph_and_snowflake.ipynb`:\n\n\n### Notebook Walkthrough\n\nThe notebook guides you through building the multi-agent workflow step-by-step:\n\n#### Steps 1-2: Import Dependencies and Define State\n\nRun the first few cells to import all necessary libraries including LangGraph, LangChain, and the Snowflake integration. The workflow uses an extended `MessagesState` that tracks:\n\n- The execution plan (immutable after creation)\n- Current step in the plan\n- Agent outputs (accumulated from all agents)\n- Execution errors (aggregated, not cascaded)\n\n#### Steps 3-5: Connect to Snowflake and Initialize Components\n\nThese cells establish your Snowflake session and initialize:\n- **Supervisor Model**: A `ChatSnowflake` instance using `claude-4-sonnet` for routing and synthesis\n- **Specialized Agents**: Three `SnowflakeCortexAgent` instances (CONTENT_AGENT, DATA_ANALYST_AGENT, RESEARCH_AGENT)\n\n#### Steps 6-7: Create Prompts and Helper Functions\n\nThe supervisor uses two prompts:\n- **Planning Prompt**: Creates an efficient, immutable execution plan with consolidated queries\n- **Synthesis Prompt**: Combines agent results into executive summaries\n\nHelper functions handle message parsing, plan tracking, and context passing between agents.\n\n#### Steps 8-9: Define Node Functions\n\nEach node in the graph has a dedicated function:\n\n- **supervisor_node**: Handles planning (once) and synthesis (once) - the hub of the architecture\n- **content_agent_node**: Invokes the Content Agent for feedback and sentiment analysis\n- **data_analyst_agent_node**: Invokes the Data Analyst Agent for metrics and churn analysis\n- **research_agent_node**: Invokes the Research Agent for strategic analysis\n\nAll agent nodes route back to the supervisor for clean hub-and-spoke coordination.\n\n#### Steps 10-12: Build and Compile the Graph\n\nAssemble the workflow using LangGraph's `StateGraph`:\n\n```\nSTART → supervisor (planning) → Agent → supervisor (routing) → Agent → supervisor (synthesis) → END\n```\n\nThe compiled graph features:\n\n- **Immutable plan**: Created once, executed linearly without re-planning\n- **No LLM routing calls**: Supervisor uses simple plan lookup\n- **Consolidated queries**: Single SQL aggregations instead of multiple calls\n- **Aggregated error handling**: Errors collected, not cascaded\n\n#### Step 13: Test the Workflow\n\nRun sample queries to test the system:\n\n| Query Type | Expected Agent |\n|------------|----------------|\n| Customer feedback, sentiment, complaints | CONTENT_AGENT |\n| Metrics, behavior, churn, analytics | DATA_ANALYST_AGENT |\n| Market research, competition, strategy | RESEARCH_AGENT |\n\n## Run with LangGraph Studio\n\n[LangGraph Studio](https://smith.langchain.com/studio) provides a visual interface for developing, debugging, and testing your multi-agent workflow. It's an excellent alternative to running the notebook for interactive exploration.\n\n### Install LangGraph CLI\n\nAdd the LangGraph CLI to your environment:\n\n```bash\npip install langgraph-cli\n```\n\n### Configure Agent Location\n\nThe companion repository includes a `studio_app.py` file that connects to your Snowflake Cortex Agents. Open this file and verify the agent database and schema match your setup:\n\n```python\nagent_database = \"SNOWFLAKE_INTELLIGENCE\"  # Your agent database\nagent_schema = \"AGENTS\"                     # Your agent schema\n```\n\n### Launch LangGraph Studio\n\nFrom the repository directory, start the development server:\n\n```bash\nlanggraph dev\n```\n\nThis opens LangGraph Studio in your browser at [https://smith.langchain.com/studio/](https://smith.langchain.com/studio/).\n\n### Using LangGraph Studio\n\nIn the Studio interface:\n\n1. **Enter your query** in the \"Messages\" field\n2. **Click \"Run\"** to execute the workflow\n3. **Watch the execution** as it flows through the supervisor and agents\n4. **Inspect state** at each node to debug or understand behavior\n\n### Sample Test Queries\n\nTry these business scenarios to test your multi-agent system:\n\n| Query Type | Example Query |\n|------------|---------------|\n| Content Analysis | \"Assess the churn risk for customers complaining about API issues.\" |\n| Data Analytics | \"What's the average session duration for enterprise vs professional customers?\" |\n| Strategic Research | \"What industries represent our best expansion opportunities?\" |\n| Churn Prediction | \"Which customers are most likely to churn in the next 30 days?\" |\n| Support Analysis | \"What are the most common support issues for enterprise customers?\" |\n\n### Optional: Enable LangSmith Tracing\n\nFor enhanced observability, add LangSmith credentials to your `.env` file:\n\n```env\nLANGSMITH_API_KEY=your_langsmith_api_key\n```\n\nThis enables detailed tracing of all LLM calls and agent interactions within LangGraph Studio.\n\n### Troubleshooting\n\n| Issue | Solution |\n|-------|----------|\n| \"401 Unauthorized\" | Check your Snowflake credentials in `.env` and verify your role has USAGE on the Cortex Agents |\n| \"No human message found\" | Enter the query in the \"Messages\" field in LangGraph Studio |\n| Agent returns 0 customers | Verify Cortex Search service includes `customer_id` in ATTRIBUTES and demo data was loaded |\n| Connection Issues | Verify `SNOWFLAKE_ACCOUNT` format (e.g., `org-account` or `account.region`) |\n\n## Evaluate with TruLens\n\n[TruLens](https://www.trulens.org/) provides observability and evaluation for your multi-agent system. Continue following the notebook to set up metrics and run evaluations.\n\n### TruLens Setup (Steps 14+ in Notebook)\n\nThe notebook walks you through:\n\n1. **Importing TruLens Dependencies**: Including `TruGraph`, `SnowflakeConnector`, and `Cortex` provider\n\n2. **Connecting to Snowflake**: Creating a connector that stores evaluation data in Snowflake\n\n3. **Configuring Evaluation Metrics**:\n   - **Plan Quality**: Evaluates how well the supervisor creates execution plans\n   - **Plan Adherence**: Checks if agents follow the plan\n   - **Execution Efficiency**: Measures workflow efficiency\n   - **Logical Consistency**: Verifies consistency across agent responses\n\n4. **Instrumenting the App**: Wrapping the LangGraph graph with `TruGraph` for observability\n\n5. **Running Evaluations**: Processing test queries and computing metrics\n\n### Evaluation Metrics\n\nThe notebook configures these Goal-Plan-Action (GPA) alignment metrics:\n\n| Metric | Description |\n|--------|-------------|\n| Plan Quality | How well does the supervisor create actionable execution plans? |\n| Plan Adherence | Do agents follow the specified plan? |\n| Execution Efficiency | Is the workflow efficient (minimal redundant calls)? |\n| Logical Consistency | Are responses consistent across agents? |\n| Answer Relevance | Does the final answer address the original question? |\n\n### Running the Evaluation\n\nFollow the notebook cells to:\n\n1. Define evaluation inputs as LangGraph state dicts\n2. Create a DataFrame with test queries\n3. Configure and start the evaluation run\n4. Wait for invocations to complete\n5. Compute all metrics\n\nResults are stored in Snowflake and can be viewed in Snowsight.\n\n## Conclusion and Resources\n\nCongratulations! You've successfully built an **efficient multi-agent supervisor architecture** using LangGraph and Snowflake Cortex. This system demonstrates:\n\n### What You Built\n\n1. **Snowflake Infrastructure**\n   - Database with customer intelligence data\n   - Cortex Search services for semantic search\n   - Semantic Views for text-to-SQL\n   - Custom AI UDFs for specialized analysis\n   - Three specialized Cortex Agents\n\n2. **Efficient LangGraph Workflow**\n   - Hub-and-spoke architecture with supervisor as central coordinator\n   - Immutable execution planning (plan once, execute linearly)\n   - No LLM calls for routing (simple plan lookup)\n   - Aggregated error handling (errors collected, not cascaded)\n   - State-based context passing between agents\n\n3. **Interactive Development Environment**\n   - LangGraph Studio for visual workflow debugging\n   - Real-time state inspection at each node\n   - LangSmith tracing for detailed observability\n\n4. **Evaluation Pipeline**\n   - TruLens integration for observability\n   - Custom metrics for goal-plan-action (GPA) alignment\n   - Snowflake-native evaluation storage\n\n### Next Steps\n\n- **Add more agents**: Extend with specialized agents for sales, finance, or operations\n- **Tune prompts**: Use the evaluations to guide improvements to supervisor and agent prompts\n- **Build custom tools**: Create additional UDFs for domain-specific analysis\n- **Iterate with LangGraph Studio**: Use the visual debugger to rapidly test and refine your workflow\n\n### Related Resources\n\n- [Developer Guide GitHub Repository](https://github.com/Snowflake-Labs/sfguide-build-and-evaluate-agents-with-snowflake-and-langgraph)\n- [LangChain Snowflake Integration](https://github.com/langchain-ai/langchain-snowflake)\n","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],":type":"snowflake-site/components/contentfragment","model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-132014fff2","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-946aa30850",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-8f1a43ad43","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2026-01-22",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-7363acc1ad","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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Git Integration\u003C/h2\u003E","\u003Ch2\u003ESetup Database and Load Data\u003C/h2\u003E","\u003Ch2\u003ESetup Cortex Search Services\u003C/h2\u003E","\u003Ch2\u003ESetup Semantic Views\u003C/h2\u003E","\u003Ch2\u003ESetup Custom AI UDFs\u003C/h2\u003E","\u003Ch2\u003ECreate Cortex Agents\u003C/h2\u003E","\u003Ch2\u003EBuild LangGraph Supervisor Workflow\u003C/h2\u003E","\u003Ch2\u003ERun with LangGraph Studio\u003C/h2\u003E","\u003Ch2\u003EEvaluate with TruLens\u003C/h2\u003E","\u003Ch2\u003EConclusion and 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