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pre[class*=language-]{background-color:rgba(var(--ui-12-rgb),.5);color:var(--text-01);text-shadow:none;padding:var(--spacing-00);border-radius:var(--spacing-00);font-size:smaller}","isGSAPEnabled":false,":type":"snowflake-site/components/markup-editor"},"responsivegrid":{"columnClassNames":{"quickstart_hero":"aem-GridColumn aem-GridColumn--default--12","flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12","markup_editor":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnCount":12,":items":{"quickstart_hero":{"id":"quickstart-hero-ebb1016b73","isDeveloperGuidesPage":false,"quickstartHeroBreadcrumbs":[{"title":"Upgrade from Cortex Analyst to Cortex 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It builds on everything Cortex Analyst offers — text-to-SQL over semantic views — and adds agentic orchestration that delivers higher accuracy, self-correction, and a unified interface for structured data, unstructured data, and custom tools.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EYour existing semantic views work as-is.\u003C/strong\u003E No re-modeling is required. The upgrade is entirely at the API and application layer.\u003C/p\u003E\n","\u003Ch3\u003EWhy upgrade?\u003C/h3\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003ECapability\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003ECortex Analyst\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003ECortex Agents\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EOpen-ended and complex questions\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003ELimited to single-shot SQL\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EReasons through multi-step questions, breaks them into sub-tasks\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EAmbiguous questions\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EReturns suggestions, requires user to rephrase\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EAsks clarifying questions naturally in conversation\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003ESQL accuracy\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EHigh\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EHigher — agent can inspect results and self-correct errors\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003ERecovery from incorrect answers\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003ERequires user to re-ask\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EAgent detects issues and retries with a different approach\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003ECharts and visualizations\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003ENot supported\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EGenerates charts automatically when data benefits from it\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EUnstructured data (RAG)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003ENot supported\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003ECombines SQL answers with document search via Cortex Search\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Ch3\u003EWhat you'll learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to create a Cortex Agent backed by your semantic view\u003C/li\u003E\u003Cli\u003EHow to update your API calls from Analyst to Agents\u003C/li\u003E\u003Cli\u003EHow to parse the new response format\u003C/li\u003E\u003Cli\u003EHow to use server-managed threads for multi-turn conversations\u003C/li\u003E\u003Cli\u003EHow to handle edge cases (legacy models, routing, timeouts)\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat you'll need\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA Snowflake account with Cortex Agents access\u003C/li\u003E\u003Cli\u003EAn existing semantic view or semantic model YAML on a stage\u003C/li\u003E\u003Cli\u003EBasic familiarity with REST APIs and Python\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch2\u003EPrerequisites\u003C/h2\u003E\n","\u003Ch3\u003EPrivileges\u003C/h3\u003E\n","\u003Cp\u003ETo create and use a Cortex Agent, your role needs:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Grant privileges to create an agent\nGRANT USAGE ON DATABASE <database_name> TO ROLE <role_name>;\nGRANT USAGE ON SCHEMA <database_name>.<schema_name> TO ROLE <role_name>;\nGRANT CREATE AGENT ON SCHEMA <database_name>.<schema_name> TO ROLE <role_name>;\n\n-- Grant access to the semantic view and underlying tables\nGRANT USAGE ON SEMANTIC VIEW <database_name>.<schema_name>.<view_name> TO ROLE <role_name>;\nGRANT SELECT ON TABLE <database_name>.<schema_name>.<table_name> TO ROLE <role_name>;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EEnable cross-region inference (recommended)\u003C/h3\u003E\n","\u003Cp\u003ECross-region inference gives your agent access to the full set of LLMs. An ACCOUNTADMIN must run:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EALTER ACCOUNT SET CORTEX_ENABLED_CROSS_REGION = 'ANY_REGION';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EPython environment\u003C/h3\u003E\n","\u003Cp\u003EThe code examples in this guide use Python with the \u003Ccode\u003Erequests\u003C/code\u003E library:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Epip install requests\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EAuthentication\u003C/h3\u003E\n","\u003Cp\u003ECortex Agents supports the same authentication methods as Cortex Analyst:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EProgrammatic Access Tokens (PAT)\u003C/li\u003E\u003Cli\u003EKey pair authentication (JWT)\u003C/li\u003E\u003Cli\u003EOAuth\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EAll examples in this guide use a PAT stored in the \u003Ccode\u003EPAT\u003C/code\u003E environment variable.\u003C/p\u003E\n","\u003Ch2\u003EHow Cortex Agents Works\u003C/h2\u003E\n","\u003Cp\u003ECortex Analyst is a single-purpose text-to-SQL endpoint. You send a question and a semantic view; it returns SQL.\u003C/p\u003E\n","\u003Cp\u003ECortex Agents wraps that capability in an orchestrator that can:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\u003Cstrong\u003EReason\u003C/strong\u003E about your question and decide how to approach it\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EGenerate SQL\u003C/strong\u003E directly against your semantic view\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EExecute the SQL\u003C/strong\u003E and inspect results\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESelf-correct\u003C/strong\u003E if the SQL errors or returns unexpected results\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ECombine tools\u003C/strong\u003E — answer questions that need both structured data and document search\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EThis architecture is why agents handle open-ended and ambiguous questions better: instead of a single pass, the agent can iterate until it gets the right answer.\u003C/p\u003E\n","\u003Ch2\u003ECreate a Cortex Agent\u003C/h2\u003E\n","\u003Cp\u003EThe easiest way to create an agent is through the Snowsight UI. You can also use SQL or the REST API — all three methods produce the same agent object.\u003C/p\u003E\n","\u003Ch3\u003ECreate an agent in Snowsight\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003ESign in to Snowsight\u003C/li\u003E\u003Cli\u003EIn the navigation menu, select \u003Cstrong\u003EAI & ML → Agents\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003ESelect \u003Cstrong\u003ECreate agent\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EEnter a name and display name for your agent\u003C/li\u003E\u003Cli\u003ESelect \u003Cstrong\u003ECreate agent\u003C/strong\u003E\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EOnce the agent is created, add your semantic view as a tool:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003ESelect \u003Cstrong\u003EEdit\u003C/strong\u003E on your agent\u003C/li\u003E\u003Cli\u003ESelect \u003Cstrong\u003ETools\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EFind \u003Cstrong\u003ECortex Analyst\u003C/strong\u003E and select \u003Cstrong\u003E+ Add\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EEnter a name for the tool (e.g., "SalesAnalytics")\u003C/li\u003E\u003Cli\u003ESelect \u003Cstrong\u003ESemantic view\u003C/strong\u003E, then choose your semantic view from the picker\u003C/li\u003E\u003Cli\u003ESelect a warehouse for query execution\u003C/li\u003E\u003Cli\u003EFor \u003Cstrong\u003EDescription\u003C/strong\u003E, write a clear description of what data this semantic view covers and when to use it — this directly impacts routing accuracy\u003C/li\u003E\u003Cli\u003ESelect \u003Cstrong\u003EAdd\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003ESelect \u003Cstrong\u003ESave\u003C/strong\u003E\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EYou can test your agent immediately in the agent playground by entering a question on the agent details page.\u003C/p\u003E\n","\u003Cp\u003EFor full details, see \u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents-manage\"\u003EConfigure and interact with Agents\u003C/a\u003E.\u003C/p\u003E\n","\u003Ch3\u003EMultiple semantic views\u003C/h3\u003E\n","\u003Cp\u003EIf you currently use the \u003Ccode\u003Esemantic_models\u003C/code\u003E array in Cortex Analyst to route across multiple views, add each as a separate Cortex Analyst tool on the same agent. Repeat the "Add tool" steps above for each semantic view.\u003C/p\u003E\n","\u003Cp\u003EThe agent automatically routes questions to the correct tool based on the descriptions you provide. Write clear, distinct descriptions for each — for example:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E"Revenue, orders, and customer metrics. Use for sales performance questions."\u003C/li\u003E\u003Cli\u003E"Campaign performance, ad spend, and conversion data. Use for marketing questions."\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EGrant access\u003C/h3\u003E\n","\u003Cp\u003EAfter creating the agent, grant usage to other roles that need it:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EGRANT USAGE ON AGENT my_db.my_schema.my_analytics_agent TO ROLE analyst_role;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou can also do this in Snowsight under \u003Cstrong\u003EAccess\u003C/strong\u003E on the agent details page.\u003C/p\u003E\n","\u003Ch2\u003EUpdate Your API Calls\u003C/h2\u003E\n","\u003Ch3\u003ERequest and response: before and after\u003C/h3\u003E\n","\u003Cp\u003EThe key differences between the two APIs:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EEndpoint\u003C/strong\u003E: \u003Ccode\u003E/api/v2/cortex/analyst/message\u003C/code\u003E → \u003Ccode\u003E/api/v2/databases/{db}/schemas/{schema}/agents/{name}:run\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESemantic view\u003C/strong\u003E: Specified when creating the agent, not in every request\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EStream\u003C/strong\u003E: Agents stream by default; set \u003Ccode\u003Estream: false\u003C/code\u003E for a non-streaming response\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EResponse shape\u003C/strong\u003E: Analyst wraps in \u003Ccode\u003E{"message": {"role": "analyst", "content": [...]}}\u003C/code\u003E — Agents return \u003Ccode\u003E{"role": "assistant", "content": [...], "metadata": {...}}\u003C/code\u003E at the top level\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESQL delivery\u003C/strong\u003E: A \u003Ccode\u003Esql\u003C/code\u003E content block → \u003Ccode\u003Etool_use\u003C/code\u003E and \u003Ccode\u003Etool_results\u003C/code\u003E blocks\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EResults included\u003C/strong\u003E: Agents execute the SQL and return results directly\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EFinal answer\u003C/strong\u003E: Agents provide a natural language summary after seeing the data\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EPython example: complete before/after\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EBefore (Cortex Analyst):\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eimport requests\nimport os\n\nACCOUNT_URL = os.environ["SNOWFLAKE_ACCOUNT_BASE_URL"]\nPAT = os.environ["PAT"]\n\ndef ask_analyst(question: str, semantic_view: str) -> dict:\n    response = requests.post(\n        f"{ACCOUNT_URL}/api/v2/cortex/analyst/message",\n        headers={\n            "Authorization": f"Bearer {PAT}",\n            "Content-Type": "application/json",\n        },\n        json={\n            "messages": [\n                {\n                    "role": "user",\n                    "content": [{"type": "text", "text": question}],\n                }\n            ],\n            "semantic_view": semantic_view,\n        },\n    )\n    response.raise_for_status()\n    return response.json()\n\n\nresult = ask_analyst(\n    "What was total revenue last quarter?",\n    "MY_DB.MY_SCHEMA.SALES_SEMANTIC_VIEW",\n)\n\n# Extract SQL from Analyst response\nfor block in result["message"]["content"]:\n    if block["type"] == "sql":\n        print(f"SQL: {block['statement']}")\n    elif block["type"] == "text":\n        print(f"Text: {block['text']}")\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EAfter (Cortex Agents):\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eimport requests\nimport os\n\nACCOUNT_URL = os.environ["SNOWFLAKE_ACCOUNT_BASE_URL"]\nPAT = os.environ["PAT"]\n\ndef ask_agent(question: str, database: str, schema: str, agent_name: str) -> dict:\n    response = requests.post(\n        f"{ACCOUNT_URL}/api/v2/databases/{database}/schemas/{schema}/agents/{agent_name}:run",\n        headers={\n            "Authorization": f"Bearer {PAT}",\n            "Content-Type": "application/json",\n        },\n        json={\n            "messages": [\n                {\n                    "role": "user",\n                    "content": [{"type": "text", "text": question}],\n                }\n            ],\n            "stream": False,\n        },\n    )\n    response.raise_for_status()\n    return response.json()\n\n\nresult = ask_agent(\n    "What was total revenue last quarter?",\n    "my_db",\n    "my_schema",\n    "my_analytics_agent",\n)\n\n# Extract information from Agent response\n# Note: Agent response is {"role": "assistant", "content": [...], "metadata": {...}}\nfor block in result["content"]:\n    if block["type"] == "text":\n        print(f"Text: {block['text']}")\n    elif block["type"] == "tool_use":\n        print(f"SQL: {block['tool_use']['input']['sql']}")\n    elif block["type"] == "tool_results":\n        for item in block["tool_results"]["content"]:\n            if item["type"] == "json":\n                print(f"Query ID: {item['json'].get('query_id')}")\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003EParse the New Response Format\u003C/h2\u003E\n","\u003Ch3\u003EContent block types\u003C/h3\u003E\n","\u003Cp\u003ECortex Analyst returns three content types: \u003Ccode\u003Etext\u003C/code\u003E, \u003Ccode\u003Esql\u003C/code\u003E, and \u003Ccode\u003Esuggestions\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003ECortex Agents returns these content types:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EType\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EDescription\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003E\u003Ccode\u003Etext\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003ENatural language explanation or answer\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003E\u003Ccode\u003Ethinking\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EThe agent's internal reasoning (may be included)\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003E\u003Ccode\u003Etool_use\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EThe agent is invoking a tool (contains tool name and input)\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003E\u003Ccode\u003Etool_results\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EThe result of a tool invocation (contains output data)\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Ch3\u003EExtracting SQL and results\u003C/h3\u003E\n","\u003Cp\u003EIn Cortex Analyst, SQL lives in a \u003Ccode\u003Esql\u003C/code\u003E content block:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E# Analyst\nstatement = block["statement"]\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EIn Cortex Agents, SQL appears in \u003Ccode\u003Etool_use\u003C/code\u003E blocks (the generated query) and \u003Ccode\u003Etool_results\u003C/code\u003E blocks (the execution output):\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E# Agent - from tool_use (the SQL the agent generated)\nsql = block["tool_use"]["input"]["sql"]\n\n# Agent - from tool_results (execution output as JSON items)\nfor item in block["tool_results"]["content"]:\n    if item["type"] == "json":\n        query_id = item["json"].get("query_id")\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EComplete response parser\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Edef parse_agent_response(response: dict) -> dict:\n    """Parse a Cortex Agents response into structured components.\n\n    The response shape is: {"role": "assistant", "content": [...], "metadata": {...}}\n    """\n    parsed = {\n        "text_blocks": [],\n        "sql_statements": [],\n        "query_results": [],\n        "query_ids": [],\n    }\n\n    for block in response["content"]:\n        if block["type"] == "text":\n            parsed["text_blocks"].append(block["text"])\n\n        elif block["type"] == "tool_use":\n            # tool_use contains the SQL the agent generated\n            sql = block["tool_use"].get("input", {}).get("sql")\n            if sql:\n                parsed["sql_statements"].append(sql)\n\n        elif block["type"] == "tool_results":\n            # tool_results contains execution output as content items\n            for item in block["tool_results"].get("content", []):\n                if item.get("type") == "json":\n                    data = item["json"]\n                    if "query_id" in data:\n                        parsed["query_ids"].append(data["query_id"])\n                    if "result_set" in data:\n                        parsed["query_results"].append(data["result_set"])\n\n    return parsed\n\n\n# Usage\nresult = ask_agent("What was total revenue last quarter?", "my_db", "my_schema", "my_analytics_agent")\nparsed = parse_agent_response(result)\n\nprint(f"Answer: {parsed['text_blocks'][-1]}")  # Final text block is usually the answer\nprint(f"SQL: {parsed['sql_statements']}")\nprint(f"Data: {parsed['query_results']}")\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStreaming responses\u003C/h3\u003E\n","\u003Cp\u003ECortex Agents streams responses as server-sent events (SSE) by default. The streaming format uses \u003Ccode\u003Eevent:\u003C/code\u003E and \u003Ccode\u003Edata:\u003C/code\u003E lines with event types like \u003Ccode\u003Eresponse.text.delta\u003C/code\u003E, \u003Ccode\u003Eresponse.tool_use\u003C/code\u003E, \u003Ccode\u003Eresponse.tool_results\u003C/code\u003E, and others.\u003C/p\u003E\n","\u003Cp\u003EFor non-streaming use (simpler to implement), set \u003Ccode\u003E"stream": false\u003C/code\u003E in your request body — all examples in this guide use non-streaming mode.\u003C/p\u003E\n","\u003Cp\u003EFor streaming implementation details and event schemas, see the \u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents-run\"\u003ECortex Agents Run API documentation\u003C/a\u003E.\u003C/p\u003E\n","\u003Ch3\u003EMapping Analyst content types to Agent equivalents\u003C/h3\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EAnalyst\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EAgent equivalent\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EHow to detect\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003E\u003Ccode\u003Etype: "text"\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003E\u003Ccode\u003Etype: "text"\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003ESame — direct text content\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003E\u003Ccode\u003Etype: "sql"\u003C/code\u003E with \u003Ccode\u003Estatement\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003E\u003Ccode\u003Etype: "tool_use"\u003C/code\u003E + \u003Ccode\u003Etype: "tool_results"\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003ECheck for \u003Ccode\u003Etool_use\u003C/code\u003E blocks containing \u003Ccode\u003Esql\u003C/code\u003E in input\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003E\u003Ccode\u003Etype: "suggestions"\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003E\u003Ccode\u003Etype: "text"\u003C/code\u003E with clarification questions\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EAgent asks follow-up questions as natural text\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003E\u003Ccode\u003Econfidence.verified_query_used\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003ENot directly exposed\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EVerified queries still influence SQL generation but are not surfaced in the response\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Ch2\u003EUse Multi-Turn Conversations with Threads\u003C/h2\u003E\n","\u003Ch3\u003EHow Cortex Analyst handles multi-turn\u003C/h3\u003E\n","\u003Cp\u003EIn Cortex Analyst, you pass the full conversation history with every request:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E# Analyst: you maintain and send full history\nmessages = [\n    {"role": "user", "content": [{"type": "text", "text": "What was revenue last quarter?"}]},\n    {"role": "analyst", "content": [{"type": "text", "text": "Revenue was $4.2M..."}, {"type": "sql", "statement": "SELECT ..."}]},\n    {"role": "user", "content": [{"type": "text", "text": "Break that down by region"}]},\n]\n\nresponse = requests.post(\n    f"{ACCOUNT_URL}/api/v2/cortex/analyst/message",\n    json={"messages": messages, "semantic_view": "..."},\n    ...\n)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EHow Cortex Agents handles multi-turn\u003C/h3\u003E\n","\u003Cp\u003EWith Cortex Agents, the server maintains conversation context in a \u003Cstrong\u003Ethread\u003C/strong\u003E. You create a thread once, then reference it:\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EStep 1: Create a thread\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Edef create_thread() -> str:\n    """Create a conversation thread and return its ID."""\n    response = requests.post(\n        f"{ACCOUNT_URL}/api/v2/cortex/threads",\n        headers={\n            "Authorization": f"Bearer {PAT}",\n            "Content-Type": "application/json",\n        },\n        json={},\n    )\n    response.raise_for_status()\n    return response.json()["thread_id"]\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EStep 2: Send messages with thread context\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Edef ask_agent_with_thread(\n    question: str,\n    database: str,\n    schema: str,\n    agent_name: str,\n    thread_id: int,\n    parent_message_id: int = 0,\n) -> dict:\n    """Send a message in the context of an existing thread."""\n    response = requests.post(\n        f"{ACCOUNT_URL}/api/v2/databases/{database}/schemas/{schema}/agents/{agent_name}:run",\n        headers={\n            "Authorization": f"Bearer {PAT}",\n            "Content-Type": "application/json",\n        },\n        json={\n            "thread_id": thread_id,\n            "parent_message_id": parent_message_id,\n            "messages": [\n                {\n                    "role": "user",\n                    "content": [{"type": "text", "text": question}],\n                }\n            ],\n            "stream": False,\n        },\n    )\n    response.raise_for_status()\n    return response.json()\n\n\n# Multi-turn conversation\nthread_id = create_thread()\n\n# First question\nresult1 = ask_agent_with_thread(\n    "What was revenue last quarter?",\n    "my_db", "my_schema", "my_analytics_agent",\n    thread_id=thread_id,\n    parent_message_id=0,\n)\n\n# Follow-up — use assistant_message_id from the previous response metadata\nresult2 = ask_agent_with_thread(\n    "Break that down by region",\n    "my_db", "my_schema", "my_analytics_agent",\n    thread_id=thread_id,\n    parent_message_id=result1["metadata"]["assistant_message_id"],\n)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EBenefits of server-managed threads\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ESmaller payloads\u003C/strong\u003E: No need to send the full conversation history each time\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EBetter context\u003C/strong\u003E: The server maintains the full execution trace, including intermediate SQL and results\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESimpler client code\u003C/strong\u003E: No need to manage message arrays or truncate old context\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch2\u003EHandle Edge Cases\u003C/h2\u003E\n","\u003Ch3\u003ELegacy semantic models on stages\u003C/h3\u003E\n","\u003Cp\u003EIf you still use YAML files on a Snowflake stage (rather than a semantic view object), you can reference them in tool_resources:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE AGENT my_analytics_agent\n  FROM SPECIFICATION\n  $$\n  models:\n    orchestration: auto\n\n  tools:\n    - tool_spec:\n        type: "cortex_analyst_text_to_sql"\n        name: "SalesAnalytics"\n        description: "Analyzes sales data."\n\n  tool_resources:\n    SalesAnalytics:\n      semantic_model_file: "@my_db.my_schema.my_stage/sales_model.yaml"\n  $$;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ERecommendation\u003C/strong\u003E: Migrate to semantic views for better governance, RBAC, and sharing. Use \u003Ccode\u003ESYSTEM$CREATE_SEMANTIC_VIEW_FROM_YAML\u003C/code\u003E to convert your YAML file to a semantic view.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EForcing a specific tool\u003C/h3\u003E\n","\u003Cp\u003EIf you want to ensure the agent uses a particular tool (similar to how Analyst always used a specific semantic view), use \u003Ccode\u003Etool_choice\u003C/code\u003E:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-json\"\u003E{\n  "messages": [...],\n  "tool_choice": {\n    "type": "required",\n    "name": ["SalesAnalytics"]\n  }\n}\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EBudget and timeout configuration\u003C/h3\u003E\n","\u003Cp\u003EControl how long the agent can spend on a response:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-yaml\"\u003Eorchestration:\n  budget:\n    seconds: 30    # Max wall-clock time\n    tokens: 16000  # Max orchestration tokens\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EFor complex questions that need multiple tool calls, increase these values. For simple lookups, lower values reduce latency.\u003C/p\u003E\n","\u003Ch3\u003EUsing SQL instead of REST\u003C/h3\u003E\n","\u003Cp\u003EYou can also run an agent from SQL using \u003Ccode\u003ESNOWFLAKE.CORTEX.DATA_AGENT_RUN\u003C/code\u003E:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT TRY_PARSE_JSON(\n  SNOWFLAKE.CORTEX.DATA_AGENT_RUN(\n    'my_db.my_schema.my_analytics_agent',\n    $${\n      "parent_message_id": 0,\n      "messages": [\n        {\n          "role": "user",\n          "content": [{"type": "text", "text": "What was total revenue last quarter?"}]\n        }\n      ]\n    }$$\n  )\n) AS resp;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis returns a non-streaming JSON response and is useful for testing, notebooks, or stored procedure integrations.\u003C/p\u003E\n","\u003Ch2\u003EUpdate Monitoring\u003C/h2\u003E\n","\u003Ch3\u003EObservability changes\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EUsage tracking view\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ECortex Analyst: \u003Ccode\u003ESNOWFLAKE.LOCAL.CORTEX_ANALYST_REQUESTS_V\u003C/code\u003E\u003C/li\u003E\u003Cli\u003ECortex Agents: \u003Ccode\u003ESNOWFLAKE.LOCAL.GET_AI_OBSERVABILITY_EVENTS\u003C/code\u003E (see \u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents-monitor\"\u003EMonitor Cortex Agent requests\u003C/a\u003E)\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003ECredit service type\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ECortex Analyst: \u003Ccode\u003Ecortex_analyst\u003C/code\u003E\u003C/li\u003E\u003Cli\u003ECortex Agents: \u003Ccode\u003Ecortex_agents\u003C/code\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EFeedback endpoint\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ECortex Analyst: \u003Ccode\u003EPOST /api/v2/cortex/analyst/feedback\u003C/code\u003E\u003C/li\u003E\u003Cli\u003ECortex Agents: \u003Ccode\u003EPOST /api/v2/databases/{db}/schemas/{schema}/agents/{name}:feedback\u003C/code\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EMonitoring queries\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EFind recent agent requests:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT * FROM TABLE(\n  SNOWFLAKE.LOCAL.GET_AI_OBSERVABILITY_EVENTS(\n    'CORTEX_AGENT',\n    'my_db.my_schema.my_analytics_agent'\n  )\n)\nORDER BY timestamp DESC\nLIMIT 100;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ESending feedback\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Ecurl -X POST "$SNOWFLAKE_ACCOUNT_BASE_URL/api/v2/databases/my_db/schemas/my_schema/agents/my_analytics_agent:feedback" \\\n  --header "Authorization: Bearer $PAT" \\\n  --header 'Content-Type: application/json' \\\n  --data '{"orig_request_id": "<request_id>", "positive": true, "feedback_message": "Correct answer"}'\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003EValidate Your Migration\u003C/h2\u003E\n","\u003Cp\u003EBefore switching production traffic, confirm:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ERun 10-20 representative questions through both APIs and compare SQL correctness, accuracy, and latency\u003C/li\u003E\u003Cli\u003EVerify your app handles all response types: text-only, SQL + results, multi-step, and errors\u003C/li\u003E\u003Cli\u003ETest multi-turn conversations with threads (ask a question, then follow up)\u003C/li\u003E\u003Cli\u003ECheck that ambiguous or unanswerable questions are handled gracefully\u003C/li\u003E\u003Cli\u003EMonitor \u003Ccode\u003ECORTEX_AGENT_USAGE_HISTORY\u003C/code\u003E after switching and collect user feedback\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch2\u003EConclusion and Next Steps\u003C/h2\u003E\n","\u003Cp\u003EYou've upgraded from Cortex Analyst to Cortex Agents. Your application now benefits from:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EHigher accuracy\u003C/strong\u003E through agentic SQL generation and self-correction\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EServer-managed context\u003C/strong\u003E via threads\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EA unified API\u003C/strong\u003E ready for additional capabilities\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ENext steps\u003C/h3\u003E\n","\u003Cp\u003ENow that your agent is running, consider adding:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ECortex Search\u003C/strong\u003E for unstructured data (documents, policies, FAQs)\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ECustom tools\u003C/strong\u003E for business-specific logic (stored procedures, UDFs)\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003Edata_to_chart\u003C/strong\u003E for automatic visualizations\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EWeb search\u003C/strong\u003E for real-time external data\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EResources\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/snowflake-intelligence/build-agents\"\u003EBuild agents\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents-run\"\u003ECortex Agents Run API\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://quickstarts.snowflake.com/guide/best-practices-to-building-cortex-agents\"\u003EBest Practices for Building Cortex Agents\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://quickstarts.snowflake.com/guide/best-practices-semantic-views-cortex-analyst\"\u003EBest Practices for Semantic Views\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/views-semantic/overview\"\u003EOverview of Semantic Views\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E"],"description":"","title":"Upgrade from Cortex Analyst to Cortex Agents","isDeveloperGuidesPage":false,":type":"snowflake-site/components/contentfragment",":items":{},":itemsOrder":[],"elements":{"quickstartArticleBody":{"title":"Quickstart Article Body","dataType":"string","value":"## Overview\n\nCortex Agents is Snowflake's next-generation API for conversational analytics. It builds on everything Cortex Analyst offers — text-to-SQL over semantic views — and adds agentic orchestration that delivers higher accuracy, self-correction, and a unified interface for structured data, unstructured data, and custom tools.\n\n**Your existing semantic views work as-is.** No re-modeling is required. The upgrade is entirely at the API and application layer.\n\n### Why upgrade?\n\n| Capability | Cortex Analyst | Cortex Agents |\n| :--- | :--- | :--- |\n| Open-ended and complex questions | Limited to single-shot SQL | Reasons through multi-step questions, breaks them into sub-tasks |\n| Ambiguous questions | Returns suggestions, requires user to rephrase | Asks clarifying questions naturally in conversation |\n| SQL accuracy | High | Higher — agent can inspect results and self-correct errors |\n| Recovery from incorrect answers | Requires user to re-ask | Agent detects issues and retries with a different approach |\n| Charts and visualizations | Not supported | Generates charts automatically when data benefits from it |\n| Unstructured data (RAG) | Not supported | Combines SQL answers with document search via Cortex Search |\n\n### What you'll learn\n\n- How to create a Cortex Agent backed by your semantic view\n- How to update your API calls from Analyst to Agents\n- How to parse the new response format\n- How to use server-managed threads for multi-turn conversations\n- How to handle edge cases (legacy models, routing, timeouts)\n\n### What you'll need\n\n- A Snowflake account with Cortex Agents access\n- An existing semantic view or semantic model YAML on a stage\n- Basic familiarity with REST APIs and Python\n\n\n## Prerequisites\n\n### Privileges\n\nTo create and use a Cortex Agent, your role needs:\n\n```sql\n-- Grant privileges to create an agent\nGRANT USAGE ON DATABASE \u003Cdatabase_name\u003E TO ROLE \u003Crole_name\u003E;\nGRANT USAGE ON SCHEMA \u003Cdatabase_name\u003E.\u003Cschema_name\u003E TO ROLE \u003Crole_name\u003E;\nGRANT CREATE AGENT ON SCHEMA \u003Cdatabase_name\u003E.\u003Cschema_name\u003E TO ROLE \u003Crole_name\u003E;\n\n-- Grant access to the semantic view and underlying tables\nGRANT USAGE ON SEMANTIC VIEW \u003Cdatabase_name\u003E.\u003Cschema_name\u003E.\u003Cview_name\u003E TO ROLE \u003Crole_name\u003E;\nGRANT SELECT ON TABLE \u003Cdatabase_name\u003E.\u003Cschema_name\u003E.\u003Ctable_name\u003E TO ROLE \u003Crole_name\u003E;\n```\n\n### Enable cross-region inference (recommended)\n\nCross-region inference gives your agent access to the full set of LLMs. An ACCOUNTADMIN must run:\n\n```sql\nALTER ACCOUNT SET CORTEX_ENABLED_CROSS_REGION = 'ANY_REGION';\n```\n\n### Python environment\n\nThe code examples in this guide use Python with the `requests` library:\n\n```bash\npip install requests\n```\n\n### Authentication\n\nCortex Agents supports the same authentication methods as Cortex Analyst:\n- Programmatic Access Tokens (PAT)\n- Key pair authentication (JWT)\n- OAuth\n\nAll examples in this guide use a PAT stored in the `PAT` environment variable.\n\n\n## How Cortex Agents Works\n\nCortex Analyst is a single-purpose text-to-SQL endpoint. You send a question and a semantic view; it returns SQL.\n\nCortex Agents wraps that capability in an orchestrator that can:\n\n1. **Reason** about your question and decide how to approach it\n2. **Generate SQL** directly against your semantic view\n3. **Execute the SQL** and inspect results\n4. **Self-correct** if the SQL errors or returns unexpected results\n5. **Combine tools** — answer questions that need both structured data and document search\n\nThis architecture is why agents handle open-ended and ambiguous questions better: instead of a single pass, the agent can iterate until it gets the right answer.\n\n\n## Create a Cortex Agent\n\nThe easiest way to create an agent is through the Snowsight UI. You can also use SQL or the REST API — all three methods produce the same agent object.\n\n### Create an agent in Snowsight\n\n1. Sign in to Snowsight\n2. In the navigation menu, select **AI & ML → Agents**\n3. Select **Create agent**\n4. Enter a name and display name for your agent\n5. Select **Create agent**\n\nOnce the agent is created, add your semantic view as a tool:\n\n1. Select **Edit** on your agent\n2. Select **Tools**\n3. Find **Cortex Analyst** and select **+ Add**\n4. Enter a name for the tool (e.g., \"SalesAnalytics\")\n5. Select **Semantic view**, then choose your semantic view from the picker\n6. Select a warehouse for query execution\n7. For **Description**, write a clear description of what data this semantic view covers and when to use it — this directly impacts routing accuracy\n8. Select **Add**\n9. Select **Save**\n\nYou can test your agent immediately in the agent playground by entering a question on the agent details page.\n\nFor full details, see [Configure and interact with Agents](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents-manage).\n\n### Multiple semantic views\n\nIf you currently use the `semantic_models` array in Cortex Analyst to route across multiple views, add each as a separate Cortex Analyst tool on the same agent. Repeat the \"Add tool\" steps above for each semantic view.\n\nThe agent automatically routes questions to the correct tool based on the descriptions you provide. Write clear, distinct descriptions for each — for example:\n\n- \"Revenue, orders, and customer metrics. Use for sales performance questions.\"\n- \"Campaign performance, ad spend, and conversion data. Use for marketing questions.\"\n\n### Grant access\n\nAfter creating the agent, grant usage to other roles that need it:\n\n```sql\nGRANT USAGE ON AGENT my_db.my_schema.my_analytics_agent TO ROLE analyst_role;\n```\n\nYou can also do this in Snowsight under **Access** on the agent details page.\n\n\n## Update Your API Calls\n\n### Request and response: before and after\n\nThe key differences between the two APIs:\n- **Endpoint**: `/api/v2/cortex/analyst/message` → `/api/v2/databases/{db}/schemas/{schema}/agents/{name}:run`\n- **Semantic view**: Specified when creating the agent, not in every request\n- **Stream**: Agents stream by default; set `stream: false` for a non-streaming response\n- **Response shape**: Analyst wraps in `{\"message\": {\"role\": \"analyst\", \"content\": [...]}}` — Agents return `{\"role\": \"assistant\", \"content\": [...], \"metadata\": {...}}` at the top level\n- **SQL delivery**: A `sql` content block → `tool_use` and `tool_results` blocks\n- **Results included**: Agents execute the SQL and return results directly\n- **Final answer**: Agents provide a natural language summary after seeing the data\n\n### Python example: complete before/after\n\n**Before (Cortex Analyst):**\n\n```python\nimport requests\nimport os\n\nACCOUNT_URL = os.environ[\"SNOWFLAKE_ACCOUNT_BASE_URL\"]\nPAT = os.environ[\"PAT\"]\n\ndef ask_analyst(question: str, semantic_view: str) -\u003E dict:\n    response = requests.post(\n        f\"{ACCOUNT_URL}/api/v2/cortex/analyst/message\",\n        headers={\n            \"Authorization\": f\"Bearer {PAT}\",\n            \"Content-Type\": \"application/json\",\n        },\n        json={\n            \"messages\": [\n                {\n                    \"role\": \"user\",\n                    \"content\": [{\"type\": \"text\", \"text\": question}],\n                }\n            ],\n            \"semantic_view\": semantic_view,\n        },\n    )\n    response.raise_for_status()\n    return response.json()\n\n\nresult = ask_analyst(\n    \"What was total revenue last quarter?\",\n    \"MY_DB.MY_SCHEMA.SALES_SEMANTIC_VIEW\",\n)\n\n# Extract SQL from Analyst response\nfor block in result[\"message\"][\"content\"]:\n    if block[\"type\"] == \"sql\":\n        print(f\"SQL: {block['statement']}\")\n    elif block[\"type\"] == \"text\":\n        print(f\"Text: {block['text']}\")\n```\n\n**After (Cortex Agents):**\n\n```python\nimport requests\nimport os\n\nACCOUNT_URL = os.environ[\"SNOWFLAKE_ACCOUNT_BASE_URL\"]\nPAT = os.environ[\"PAT\"]\n\ndef ask_agent(question: str, database: str, schema: str, agent_name: str) -\u003E dict:\n    response = requests.post(\n        f\"{ACCOUNT_URL}/api/v2/databases/{database}/schemas/{schema}/agents/{agent_name}:run\",\n        headers={\n            \"Authorization\": f\"Bearer {PAT}\",\n            \"Content-Type\": \"application/json\",\n        },\n        json={\n            \"messages\": [\n                {\n                    \"role\": \"user\",\n                    \"content\": [{\"type\": \"text\", \"text\": question}],\n                }\n            ],\n            \"stream\": False,\n        },\n    )\n    response.raise_for_status()\n    return response.json()\n\n\nresult = ask_agent(\n    \"What was total revenue last quarter?\",\n    \"my_db\",\n    \"my_schema\",\n    \"my_analytics_agent\",\n)\n\n# Extract information from Agent response\n# Note: Agent response is {\"role\": \"assistant\", \"content\": [...], \"metadata\": {...}}\nfor block in result[\"content\"]:\n    if block[\"type\"] == \"text\":\n        print(f\"Text: {block['text']}\")\n    elif block[\"type\"] == \"tool_use\":\n        print(f\"SQL: {block['tool_use']['input']['sql']}\")\n    elif block[\"type\"] == \"tool_results\":\n        for item in block[\"tool_results\"][\"content\"]:\n            if item[\"type\"] == \"json\":\n                print(f\"Query ID: {item['json'].get('query_id')}\")\n```\n\n\n## Parse the New Response Format\n\n### Content block types\n\nCortex Analyst returns three content types: `text`, `sql`, and `suggestions`.\n\nCortex Agents returns these content types:\n\n| Type | Description |\n| :--- | :--- |\n| `text` | Natural language explanation or answer |\n| `thinking` | The agent's internal reasoning (may be included) |\n| `tool_use` | The agent is invoking a tool (contains tool name and input) |\n| `tool_results` | The result of a tool invocation (contains output data) |\n\n### Extracting SQL and results\n\nIn Cortex Analyst, SQL lives in a `sql` content block:\n```python\n# Analyst\nstatement = block[\"statement\"]\n```\n\nIn Cortex Agents, SQL appears in `tool_use` blocks (the generated query) and `tool_results` blocks (the execution output):\n```python\n# Agent - from tool_use (the SQL the agent generated)\nsql = block[\"tool_use\"][\"input\"][\"sql\"]\n\n# Agent - from tool_results (execution output as JSON items)\nfor item in block[\"tool_results\"][\"content\"]:\n    if item[\"type\"] == \"json\":\n        query_id = item[\"json\"].get(\"query_id\")\n```\n\n### Complete response parser\n\n```python\ndef parse_agent_response(response: dict) -\u003E dict:\n    \"\"\"Parse a Cortex Agents response into structured components.\n\n    The response shape is: {\"role\": \"assistant\", \"content\": [...], \"metadata\": {...}}\n    \"\"\"\n    parsed = {\n        \"text_blocks\": [],\n        \"sql_statements\": [],\n        \"query_results\": [],\n        \"query_ids\": [],\n    }\n\n    for block in response[\"content\"]:\n        if block[\"type\"] == \"text\":\n            parsed[\"text_blocks\"].append(block[\"text\"])\n\n        elif block[\"type\"] == \"tool_use\":\n            # tool_use contains the SQL the agent generated\n            sql = block[\"tool_use\"].get(\"input\", {}).get(\"sql\")\n            if sql:\n                parsed[\"sql_statements\"].append(sql)\n\n        elif block[\"type\"] == \"tool_results\":\n            # tool_results contains execution output as content items\n            for item in block[\"tool_results\"].get(\"content\", []):\n                if item.get(\"type\") == \"json\":\n                    data = item[\"json\"]\n                    if \"query_id\" in data:\n                        parsed[\"query_ids\"].append(data[\"query_id\"])\n                    if \"result_set\" in data:\n                        parsed[\"query_results\"].append(data[\"result_set\"])\n\n    return parsed\n\n\n# Usage\nresult = ask_agent(\"What was total revenue last quarter?\", \"my_db\", \"my_schema\", \"my_analytics_agent\")\nparsed = parse_agent_response(result)\n\nprint(f\"Answer: {parsed['text_blocks'][-1]}\")  # Final text block is usually the answer\nprint(f\"SQL: {parsed['sql_statements']}\")\nprint(f\"Data: {parsed['query_results']}\")\n```\n\n### Streaming responses\n\nCortex Agents streams responses as server-sent events (SSE) by default. The streaming format uses `event:` and `data:` lines with event types like `response.text.delta`, `response.tool_use`, `response.tool_results`, and others.\n\nFor non-streaming use (simpler to implement), set `\"stream\": false` in your request body — all examples in this guide use non-streaming mode.\n\nFor streaming implementation details and event schemas, see the [Cortex Agents Run API documentation](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents-run).\n\n### Mapping Analyst content types to Agent equivalents\n\n| Analyst | Agent equivalent | How to detect |\n| :--- | :--- | :--- |\n| `type: \"text\"` | `type: \"text\"` | Same — direct text content |\n| `type: \"sql\"` with `statement` | `type: \"tool_use\"` + `type: \"tool_results\"` | Check for `tool_use` blocks containing `sql` in input |\n| `type: \"suggestions\"` | `type: \"text\"` with clarification questions | Agent asks follow-up questions as natural text |\n| `confidence.verified_query_used` | Not directly exposed | Verified queries still influence SQL generation but are not surfaced in the response |\n\n\n## Use Multi-Turn Conversations with Threads\n\n### How Cortex Analyst handles multi-turn\n\nIn Cortex Analyst, you pass the full conversation history with every request:\n\n```python\n# Analyst: you maintain and send full history\nmessages = [\n    {\"role\": \"user\", \"content\": [{\"type\": \"text\", \"text\": \"What was revenue last quarter?\"}]},\n    {\"role\": \"analyst\", \"content\": [{\"type\": \"text\", \"text\": \"Revenue was $4.2M...\"}, {\"type\": \"sql\", \"statement\": \"SELECT ...\"}]},\n    {\"role\": \"user\", \"content\": [{\"type\": \"text\", \"text\": \"Break that down by region\"}]},\n]\n\nresponse = requests.post(\n    f\"{ACCOUNT_URL}/api/v2/cortex/analyst/message\",\n    json={\"messages\": messages, \"semantic_view\": \"...\"},\n    ...\n)\n```\n\n### How Cortex Agents handles multi-turn\n\nWith Cortex Agents, the server maintains conversation context in a **thread**. You create a thread once, then reference it:\n\n**Step 1: Create a thread**\n\n```python\ndef create_thread() -\u003E str:\n    \"\"\"Create a conversation thread and return its ID.\"\"\"\n    response = requests.post(\n        f\"{ACCOUNT_URL}/api/v2/cortex/threads\",\n        headers={\n            \"Authorization\": f\"Bearer {PAT}\",\n            \"Content-Type\": \"application/json\",\n        },\n        json={},\n    )\n    response.raise_for_status()\n    return response.json()[\"thread_id\"]\n```\n\n**Step 2: Send messages with thread context**\n\n```python\ndef ask_agent_with_thread(\n    question: str,\n    database: str,\n    schema: str,\n    agent_name: str,\n    thread_id: int,\n    parent_message_id: int = 0,\n) -\u003E dict:\n    \"\"\"Send a message in the context of an existing thread.\"\"\"\n    response = requests.post(\n        f\"{ACCOUNT_URL}/api/v2/databases/{database}/schemas/{schema}/agents/{agent_name}:run\",\n        headers={\n            \"Authorization\": f\"Bearer {PAT}\",\n            \"Content-Type\": \"application/json\",\n        },\n        json={\n            \"thread_id\": thread_id,\n            \"parent_message_id\": parent_message_id,\n            \"messages\": [\n                {\n                    \"role\": \"user\",\n                    \"content\": [{\"type\": \"text\", \"text\": question}],\n                }\n            ],\n            \"stream\": False,\n        },\n    )\n    response.raise_for_status()\n    return response.json()\n\n\n# Multi-turn conversation\nthread_id = create_thread()\n\n# First question\nresult1 = ask_agent_with_thread(\n    \"What was revenue last quarter?\",\n    \"my_db\", \"my_schema\", \"my_analytics_agent\",\n    thread_id=thread_id,\n    parent_message_id=0,\n)\n\n# Follow-up — use assistant_message_id from the previous response metadata\nresult2 = ask_agent_with_thread(\n    \"Break that down by region\",\n    \"my_db\", \"my_schema\", \"my_analytics_agent\",\n    thread_id=thread_id,\n    parent_message_id=result1[\"metadata\"][\"assistant_message_id\"],\n)\n```\n\n### Benefits of server-managed threads\n\n- **Smaller payloads**: No need to send the full conversation history each time\n- **Better context**: The server maintains the full execution trace, including intermediate SQL and results\n- **Simpler client code**: No need to manage message arrays or truncate old context\n\n\n## Handle Edge Cases\n\n### Legacy semantic models on stages\n\nIf you still use YAML files on a Snowflake stage (rather than a semantic view object), you can reference them in tool_resources:\n\n```sql\nCREATE OR REPLACE AGENT my_analytics_agent\n  FROM SPECIFICATION\n  $$\n  models:\n    orchestration: auto\n\n  tools:\n    - tool_spec:\n        type: \"cortex_analyst_text_to_sql\"\n        name: \"SalesAnalytics\"\n        description: \"Analyzes sales data.\"\n\n  tool_resources:\n    SalesAnalytics:\n      semantic_model_file: \"@my_db.my_schema.my_stage/sales_model.yaml\"\n  $$;\n```\n\n\u003E **Recommendation**: Migrate to semantic views for better governance, RBAC, and sharing. Use `SYSTEM$CREATE_SEMANTIC_VIEW_FROM_YAML` to convert your YAML file to a semantic view.\n\n### Forcing a specific tool\n\nIf you want to ensure the agent uses a particular tool (similar to how Analyst always used a specific semantic view), use `tool_choice`:\n\n```json\n{\n  \"messages\": [...],\n  \"tool_choice\": {\n    \"type\": \"required\",\n    \"name\": [\"SalesAnalytics\"]\n  }\n}\n```\n\n### Budget and timeout configuration\n\nControl how long the agent can spend on a response:\n\n```yaml\norchestration:\n  budget:\n    seconds: 30    # Max wall-clock time\n    tokens: 16000  # Max orchestration tokens\n```\n\nFor complex questions that need multiple tool calls, increase these values. For simple lookups, lower values reduce latency.\n\n### Using SQL instead of REST\n\nYou can also run an agent from SQL using `SNOWFLAKE.CORTEX.DATA_AGENT_RUN`:\n\n```sql\nSELECT TRY_PARSE_JSON(\n  SNOWFLAKE.CORTEX.DATA_AGENT_RUN(\n    'my_db.my_schema.my_analytics_agent',\n    $${\n      \"parent_message_id\": 0,\n      \"messages\": [\n        {\n          \"role\": \"user\",\n          \"content\": [{\"type\": \"text\", \"text\": \"What was total revenue last quarter?\"}]\n        }\n      ]\n    }$$\n  )\n) AS resp;\n```\n\nThis returns a non-streaming JSON response and is useful for testing, notebooks, or stored procedure integrations.\n\n\n## Update Monitoring\n\n### Observability changes\n\n**Usage tracking view**\n- Cortex Analyst: `SNOWFLAKE.LOCAL.CORTEX_ANALYST_REQUESTS_V`\n- Cortex Agents: `SNOWFLAKE.LOCAL.GET_AI_OBSERVABILITY_EVENTS` (see [Monitor Cortex Agent requests](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents-monitor))\n\n**Credit service type**\n- Cortex Analyst: `cortex_analyst`\n- Cortex Agents: `cortex_agents`\n\n**Feedback endpoint**\n- Cortex Analyst: `POST /api/v2/cortex/analyst/feedback`\n- Cortex Agents: `POST /api/v2/databases/{db}/schemas/{schema}/agents/{name}:feedback`\n\n### Monitoring queries\n\n**Find recent agent requests:**\n\n```sql\nSELECT * FROM TABLE(\n  SNOWFLAKE.LOCAL.GET_AI_OBSERVABILITY_EVENTS(\n    'CORTEX_AGENT',\n    'my_db.my_schema.my_analytics_agent'\n  )\n)\nORDER BY timestamp DESC\nLIMIT 100;\n```\n\n### Sending feedback\n\n```bash\ncurl -X POST \"$SNOWFLAKE_ACCOUNT_BASE_URL/api/v2/databases/my_db/schemas/my_schema/agents/my_analytics_agent:feedback\" \\\n  --header \"Authorization: Bearer $PAT\" \\\n  --header 'Content-Type: application/json' \\\n  --data '{\"orig_request_id\": \"\u003Crequest_id\u003E\", \"positive\": true, \"feedback_message\": \"Correct answer\"}'\n```\n\n\n## Validate Your Migration\n\nBefore switching production traffic, confirm:\n\n- Run 10-20 representative questions through both APIs and compare SQL correctness, accuracy, and latency\n- Verify your app handles all response types: text-only, SQL + results, multi-step, and errors\n- Test multi-turn conversations with threads (ask a question, then follow up)\n- Check that ambiguous or unanswerable questions are handled gracefully\n- Monitor `CORTEX_AGENT_USAGE_HISTORY` after switching and collect user feedback\n\n\n## Conclusion and Next Steps\n\nYou've upgraded from Cortex Analyst to Cortex Agents. Your application now benefits from:\n\n- **Higher accuracy** through agentic SQL generation and self-correction\n- **Server-managed context** via threads\n- **A unified API** ready for additional capabilities\n\n### Next steps\n\nNow that your agent is running, consider adding:\n\n- **Cortex Search** for unstructured data (documents, policies, FAQs)\n- **Custom tools** for business-specific logic (stored procedures, UDFs)\n- **data_to_chart** for automatic visualizations\n- **Web search** for real-time external data\n\n### Resources\n\n- [Build agents](https://docs.snowflake.com/en/user-guide/snowflake-cortex/snowflake-intelligence/build-agents)\n- [Cortex Agents Run API](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents-run)\n- [Best Practices for Building Cortex Agents](https://quickstarts.snowflake.com/guide/best-practices-to-building-cortex-agents)\n- [Best Practices for Semantic Views](https://quickstarts.snowflake.com/guide/best-practices-semantic-views-cortex-analyst)\n- [Overview of Semantic Views](https://docs.snowflake.com/en/user-guide/views-semantic/overview)\n",":type":"text/x-markdown","multiValue":false},"quickstartArticleLogoImage":{"title":"Quickstart Article Logo Image","dataType":"string",":type":"text/plain","multiValue":false}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-871ae8be07","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-b7b155aeb6",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-3ca63a7916","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2026-05-05",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-730a8e8f59","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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