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Instead of clicking through Snowsight, you can build, run, and orchestrate agents from scripts, notebooks, CI/CD pipelines, and scheduled jobs.\u003C/p\u003E\n","\u003Cp\u003EThis guide walks you from a first "hello world" agent run through streaming events, multi-turn conversations, a coding agent sandbox, and finally an automated data engineering ops agent that monitors your pipelines and data quality.\u003C/p\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA Snowflake account with Cortex AI enabled\u003C/li\u003E\u003Cli\u003EBasic Python and SQL knowledge\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to authenticate with the SDK using a programmatic access token (PAT)\u003C/li\u003E\u003Cli\u003EHow to run a lite agent and stream its response\u003C/li\u003E\u003Cli\u003EHow to process streaming events and extract results\u003C/li\u003E\u003Cli\u003EHow to manage threads and build multi-turn conversations\u003C/li\u003E\u003Cli\u003EHow to run a coding agent in a sandboxed environment\u003C/li\u003E\u003Cli\u003EHow to build an automated data engineering ops agent\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Need\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA \u003Ca href=\"https://signup.snowflake.com/\"\u003ESnowflake account\u003C/a\u003E (Enterprise edition or higher)\u003C/li\u003E\u003Cli\u003EPython 3.10 or later\u003C/li\u003E\u003Cli\u003EA programmatic access token (PAT) -- we'll create one in the Setup step\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Build\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EFive progressively more advanced agent scripts\u003C/li\u003E\u003Cli\u003EA production-ready automated ops agent that checks pipeline health, data quality, and diagnoses incidents\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESetup\u003C/h2\u003E\n","\u003Ch3\u003EInstall the SDK\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Epip install snowflake-cortex-agent-sdk\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ECreate a Programmatic Access Token (PAT)\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003EIn Snowsight, click your name in the bottom-left corner\u003C/li\u003E\u003Cli\u003EGo to \u003Cstrong\u003ESettings\u003C/strong\u003E > \u003Cstrong\u003EAuthentication\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EUnder \u003Cstrong\u003EProgrammatic access tokens\u003C/strong\u003E, click \u003Cstrong\u003EGenerate Token\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EGive it a name (e.g., \u003Ccode\u003Eagent-sdk-quickstart\u003C/code\u003E) and copy the token\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003ESet Environment Variables\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Eexport SNOWFLAKE_ACCOUNT="your-account-identifier"\nexport SNOWFLAKE_PAT="your-pat-token"\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003ENOTE: Your account identifier is the \u003Ccode\u003E<orgname>-<accountname>\u003C/code\u003E portion of your Snowflake URL (e.g., \u003Ccode\u003Emyorg-myaccount\u003C/code\u003E).\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003ECreate Demo Objects\u003C/h3\u003E\n","\u003Cp\u003ERun the following SQL in Snowsight or via SnowSQL to create the demo schema and sample data:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE SYSADMIN;\n\nCREATE DATABASE IF NOT EXISTS AGENT_SDK_DEMO_DB;\nCREATE SCHEMA IF NOT EXISTS AGENT_SDK_DEMO_DB.QUICKSTART;\n\n-- Create a demo role with limited access\nUSE ROLE USERADMIN;\nCREATE ROLE IF NOT EXISTS AGENT_SDK_DEMO_ROLE;\nGRANT ROLE AGENT_SDK_DEMO_ROLE TO USER IDENTIFIER(CURRENT_USER());\n\n-- Grant access to the demo objects only\nUSE ROLE SYSADMIN;\n\nCREATE OR REPLACE TABLE AGENT_SDK_DEMO_DB.QUICKSTART.SALES (\n    sale_date DATE,\n    region VARCHAR,\n    product VARCHAR,\n    units_sold INT,\n    revenue FLOAT\n);\n\nINSERT INTO AGENT_SDK_DEMO_DB.QUICKSTART.SALES VALUES\n    ('2026-10-01', 'North America', 'Widget A', 150, 4500.00),\n    ('2026-10-01', 'Europe', 'Widget A', 120, 3600.00),\n    ('2026-10-01', 'Asia Pacific', 'Widget B', 200, 8000.00),\n    ('2026-10-02', 'North America', 'Widget B', 180, 7200.00),\n    ('2026-10-02', 'Europe', 'Widget A', 90, 2700.00),\n    ('2026-10-02', 'Asia Pacific', 'Widget A', 160, 4800.00),\n    ('2026-10-03', 'North America', 'Widget A', 130, 3900.00),\n    ('2026-10-03', 'Europe', 'Widget B', 110, 4400.00),\n    ('2026-10-03', 'Asia Pacific', 'Widget B', 220, 8800.00),\n    ('2026-10-04', 'North America', 'Widget B', 170, 6800.00),\n    ('2026-10-04', 'Europe', 'Widget A', 140, 4200.00),\n    ('2026-10-04', 'Asia Pacific', 'Widget A', 190, 5700.00);\n\n-- Grant access to the demo role (not PUBLIC)\nGRANT USAGE ON DATABASE AGENT_SDK_DEMO_DB TO ROLE AGENT_SDK_DEMO_ROLE;\nGRANT USAGE ON SCHEMA AGENT_SDK_DEMO_DB.QUICKSTART TO ROLE AGENT_SDK_DEMO_ROLE;\nGRANT SELECT ON ALL TABLES IN SCHEMA AGENT_SDK_DEMO_DB.QUICKSTART TO ROLE AGENT_SDK_DEMO_ROLE;\nGRANT INSERT ON ALL TABLES IN SCHEMA AGENT_SDK_DEMO_DB.QUICKSTART TO ROLE AGENT_SDK_DEMO_ROLE;\nGRANT CREATE TABLE ON SCHEMA AGENT_SDK_DEMO_DB.QUICKSTART TO ROLE AGENT_SDK_DEMO_ROLE;\nGRANT CREATE VIEW ON SCHEMA AGENT_SDK_DEMO_DB.QUICKSTART TO ROLE AGENT_SDK_DEMO_ROLE;\nGRANT USAGE ON WAREHOUSE COMPUTE_WH TO ROLE AGENT_SDK_DEMO_ROLE;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003ENOTE: Replace \u003Ccode\u003ECOMPUTE_WH\u003C/code\u003E with your own warehouse name if different. Generate your PAT while using the \u003Ccode\u003EAGENT_SDK_DEMO_ROLE\u003C/code\u003E role, or set the role in your connection config. This ensures the agent can only access the demo database.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EYou're ready to write your first agent script.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EYour First Agent Run\u003C/h2\u003E\n","\u003Cp\u003EIn this step you'll connect to Snowflake and run a lite agent that answers a question about your sales data using the \u003Ccode\u003Ecortex_analyst_text_to_sql\u003C/code\u003E tool.\u003C/p\u003E\n","\u003Cp\u003EThe agent takes an inline semantic model that describes your table's dimensions and measures, generates SQL to answer the question, and returns the results.\u003C/p\u003E\n","\u003Cp\u003ECreate a file called \u003Ccode\u003E01_hello_agent.py\u003C/code\u003E:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eimport os\nfrom cortex_agent_sdk import CortexAgentClient\nfrom cortex_agent_sdk.auth import PatAuth\n\nclient = CortexAgentClient(\n    account=os.environ["SNOWFLAKE_ACCOUNT"],\n    auth=PatAuth(os.environ["SNOWFLAKE_PAT"]),\n)\n\n# Inline semantic model describing the SALES table\nSEMANTIC_MODEL = """\nname: sales_model\ntables:\n  - name: SALES\n    base_table:\n      database: AGENT_SDK_DEMO_DB\n      schema: QUICKSTART\n      table: SALES\n    dimensions:\n      - name: sale_date\n        expr: sale_date\n        data_type: DATE\n      - name: region\n        expr: region\n        data_type: VARCHAR\n      - name: product\n        expr: product\n        data_type: VARCHAR\n    measures:\n      - name: units_sold\n        expr: units_sold\n        data_type: INT\n        default_aggregation: sum\n      - name: revenue\n        expr: revenue\n        data_type: FLOAT\n        default_aggregation: sum\n"""\n\n\ndef extract_analyst_response(events):\n    """Extract SQL, text explanation, and results from analyst tool events."""\n    for event in events:\n        delta = event.get("delta", {})\n        for item in delta.get("content", []):\n            if item.get("type") == "tool_results":\n                tr = item.get("tool_results", {})\n                for c in tr.get("content", []):\n                    if c.get("type") == "json":\n                        return c.get("json", {})\n    return {}\n\n\nbody = {\n    "model": "auto",\n    "messages": [\n        {\n            "role": "user",\n            "content": [\n                {"type": "text", "text": "What are the total sales by region?"}\n            ],\n        }\n    ],\n    "tools": [\n        {\n            "tool_spec": {\n                "type": "cortex_analyst_text_to_sql",\n                "name": "sales_analyst",\n            }\n        }\n    ],\n    "tool_resources": {\n        "sales_analyst": {"inline_semantic_model": SEMANTIC_MODEL}\n    },\n}\n\n# Stream and extract the response\nprint("=== Streaming agent run ===\\n")\n\nstream = client.agent.stream(body)\nevents = list(stream)\n\nresult = extract_analyst_response(events)\n\nprint(f"Interpretation: {result.get('text', '(none)')}\\n")\nprint(f"Generated SQL:\\n{result.get('sql', '(none)')}\\n")\n\nrows = result.get("results", [])\nif rows:\n    print(f"Results ({len(rows)} rows):")\n    for row in rows:\n        print(f"  {row}")\n\nclient.close()\nprint("\\nDone!")\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ERun it:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Epython 01_hello_agent.py\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou should see the agent interpret your question, generate SQL to query the \u003Ccode\u003ESALES\u003C/code\u003E table, and return the results grouped by region.\u003C/p\u003E\n","\u003Cp\u003EKey concepts:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003E\u003Ccode\u003ECortexAgentClient\u003C/code\u003E\u003C/strong\u003E is the main entry point — pass your account and a \u003Ccode\u003EPatAuth\u003C/code\u003E token\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003E\u003Ccode\u003Eclient.agent.stream(body)\u003C/code\u003E\u003C/strong\u003E sends a request and returns an event iterator\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ETool spec\u003C/strong\u003E uses \u003Ccode\u003Ecortex_analyst_text_to_sql\u003C/code\u003E with an inline semantic model that maps your table structure\u003C/li\u003E\u003Cli\u003EThe response includes both the generated SQL and a natural language interpretation\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EStreaming and Callbacks\u003C/h2\u003E\n","\u003Cp\u003EIn this step you'll process events as they arrive and use background runs for asynchronous execution.\u003C/p\u003E\n","\u003Cp\u003ECreate \u003Ccode\u003E02_streaming_callbacks.py\u003C/code\u003E:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eimport os\nfrom cortex_agent_sdk import CortexAgentClient\nfrom cortex_agent_sdk.auth import PatAuth\n\nclient = CortexAgentClient(\n    account=os.environ["SNOWFLAKE_ACCOUNT"],\n    auth=PatAuth(os.environ["SNOWFLAKE_PAT"]),\n)\n\nSEMANTIC_MODEL = """..."""\n# Copy the full SEMANTIC_MODEL from 01_hello_agent.py (the inline semantic model\n# describing AGENT_SDK_DEMO_DB.QUICKSTART.SALES)\n\n\ndef process_events(stream):\n    """Process streaming events, printing each type as it arrives."""\n    for event in stream:\n        delta = event.get("delta", {})\n        for item in delta.get("content", []):\n            item_type = item.get("type", "")\n\n            if item_type == "tool_use":\n                tool = item.get("tool_use", {})\n                print(f"  [TOOL_USE] {tool.get('name')} — query: {tool.get('input', {}).get('query', '')}")\n\n            elif item_type == "tool_results":\n                tr = item.get("tool_results", {})\n                print(f"  [TOOL_RESULT] status: {tr.get('status', 'unknown')}")\n                for c in tr.get("content", []):\n                    if c.get("type") == "json":\n                        j = c.get("json", {})\n                        print(f"  [SQL] {j.get('sql', '')[:100]}...")\n                        print(f"  [TEXT] {j.get('text', '')}")\n\n            elif item_type == "text":\n                text_val = item.get("text", {})\n                if isinstance(text_val, dict):\n                    print(f"  [TEXT] {text_val.get('value', '')}")\n                else:\n                    print(f"  [TEXT] {text_val}")\n\n\nbody = {\n    "model": "auto",\n    "messages": [\n        {\n            "role": "user",\n            "content": [\n                {"type": "text", "text": "Which product has the highest total revenue?"}\n            ],\n        }\n    ],\n    "tools": [\n        {"tool_spec": {"type": "cortex_analyst_text_to_sql", "name": "sales_analyst"}}\n    ],\n    "tool_resources": {\n        "sales_analyst": {"inline_semantic_model": SEMANTIC_MODEL}\n    },\n}\n\n# Process events as they arrive\nprint("=== Processing events ===\\n")\nstream = client.agent.stream(body)\nprocess_events(stream)\n\n# Background run\nprint("\\n\\n=== Background run ===\\n")\n\nbg_body = dict(body)\nbg_body["messages"] = [\n    {"role": "user", "content": [{"type": "text", "text": "What are the daily revenue trends?"}]}\n]\n\nbg_body["stream"] = False\nbg_body["background"] = True\nbg_run = client.agent.run(bg_body)\nprint(f"Run started: {bg_run.run_id}")\nprint("Waiting for completion...\\n")\n\nresponse = bg_run.wait()\nfor item in response.content:\n    if item.get("type") == "tool_results":\n        tr = item.get("tool_results", {})\n        for c in tr.get("content", []):\n            if c.get("type") == "json":\n                j = c.get("json", {})\n                print(f"SQL: {j.get('sql', '')[:150]}...")\n                print(f"Interpretation: {j.get('text', '')}")\n\nclient.close()\nprint("\\nDone!")\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EKey patterns:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EEvent processing\u003C/strong\u003E — iterate the stream and switch on \u003Ccode\u003Eitem["type"]\u003C/code\u003E to handle \u003Ccode\u003Etool_use\u003C/code\u003E, \u003Ccode\u003Etool_results\u003C/code\u003E, and \u003Ccode\u003Etext\u003C/code\u003E events differently\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EBackground runs\u003C/strong\u003E — set \u003Ccode\u003Estream=False\u003C/code\u003E and \u003Ccode\u003Ebackground=True\u003C/code\u003E in the body dict, then call \u003Ccode\u003Eclient.agent.run(body)\u003C/code\u003E to get a \u003Ccode\u003EBackgroundRun\u003C/code\u003E handle; call \u003Ccode\u003E.wait()\u003C/code\u003E to block until done, or \u003Ccode\u003E.cancel()\u003C/code\u003E to abort\u003C/li\u003E\u003Cli\u003EThis is useful for kicking off multiple agent runs in parallel\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EThreads and Conversations\u003C/h2\u003E\n","\u003Cp\u003EThreads persist message history across turns. You create a thread, then pass \u003Ccode\u003Ethread_id\u003C/code\u003E on subsequent runs so the agent has context from previous turns.\u003C/p\u003E\n","\u003Cp\u003ECreate \u003Ccode\u003E03_conversations.py\u003C/code\u003E:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eimport os\nfrom cortex_agent_sdk import CortexAgentClient\nfrom cortex_agent_sdk.auth import PatAuth\n\nclient = CortexAgentClient(\n    account=os.environ["SNOWFLAKE_ACCOUNT"],\n    auth=PatAuth(os.environ["SNOWFLAKE_PAT"]),\n)\n\nSEMANTIC_MODEL = """..."""\n# Copy the full SEMANTIC_MODEL from 01_hello_agent.py (the inline semantic model\n# describing AGENT_SDK_DEMO_DB.QUICKSTART.SALES)\n\n\ndef extract_text(events):\n    """Extract the analyst text explanation from events."""\n    for event in events:\n        delta = event.get("delta", {})\n        for item in delta.get("content", []):\n            if item.get("type") == "tool_results":\n                tr = item.get("tool_results", {})\n                for c in tr.get("content", []):\n                    if c.get("type") == "json":\n                        return c["json"].get("text", "")\n    return ""\n\n\ndef run_on_thread(client, thread_id, question):\n    """Run an agent turn on an existing thread."""\n    body = {\n        "model": "auto",\n        "messages": [\n            {"role": "user", "content": [{"type": "text", "text": question}]},\n        ],\n        "tools": [\n            {"tool_spec": {"type": "cortex_analyst_text_to_sql", "name": "sales_analyst"}}\n        ],\n        "tool_resources": {\n            "sales_analyst": {"inline_semantic_model": SEMANTIC_MODEL}\n        },\n        "thread_id": thread_id,\n    }\n    stream = client.agent.stream(body)\n    events = list(stream)\n    return extract_text(events)\n\n\n# Create a thread\nprint("=== Multi-turn conversation ===\\n")\n\nthread = client.threads.create(origin_application="sdk-quickstart")\nthread_id = thread.get("thread_id") or thread.get("id")\nprint(f"Thread created: {thread_id}\\n")\n\n# Turn 1\nprint("User: What are the top selling products?\\n")\ntext = run_on_thread(client, thread_id, "What are the top selling products?")\nprint(f"Agent: {text}\\n")\nprint("-" * 60)\n\n# Turn 2 — the agent has context from turn 1\nprint("\\nUser: Break that down by region.\\n")\ntext = run_on_thread(client, thread_id, "Break that down by region.")\nprint(f"Agent: {text}\\n")\nprint("-" * 60)\n\n# Turn 3\nprint("\\nUser: Which region shows the strongest performance?\\n")\ntext = run_on_thread(client, thread_id, "Which region shows the strongest performance?")\nprint(f"Agent: {text}\\n")\n\n# Thread operations\nprint("\\n=== Thread operations ===\\n")\n\nclient.threads.update(thread_id, thread_name="Sales Analysis Q4")\nprint("Thread renamed to 'Sales Analysis Q4'")\n\ndesc = client.threads.describe(thread_id)\nmessages = desc.get("messages", [])\nprint(f"Messages in thread: {len(messages)}")\n\n# Search\nresults = client.threads.search(query="sales", limit=5)\nthreads = results.get("threads", [])\nprint(f"Found {len(threads)} threads matching 'sales'")\n\n# Clean up\nclient.threads.delete(thread_id)\nprint("\\nThread deleted. Done!")\nclient.close()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EKey patterns:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003E\u003Ccode\u003Eclient.threads.create()\u003C/code\u003E\u003C/strong\u003E creates a new thread — pass \u003Ccode\u003Eorigin_application\u003C/code\u003E to tag it\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003E\u003Ccode\u003Ethread_id\u003C/code\u003E\u003C/strong\u003E on subsequent runs links them to the same conversation\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003E\u003Ccode\u003Eclient.threads.search()\u003C/code\u003E\u003C/strong\u003E finds threads by keyword (fuzzy or regex)\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003E\u003Ccode\u003Eclient.threads.update()\u003C/code\u003E\u003C/strong\u003E renames threads; \u003Cstrong\u003E\u003Ccode\u003E.delete()\u003C/code\u003E\u003C/strong\u003E removes them\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECoding Agent Sandbox\u003C/h2\u003E\n","\u003Cp\u003EThe coding agent runs in a sandboxed environment with \u003Ccode\u003Ecode_toolset_all\u003C/code\u003E — it can execute SQL, run bash commands, read/write files, and use skills. This is powerful for automated data engineering tasks.\u003C/p\u003E\n","\u003Cp\u003ECreate \u003Ccode\u003E04_coding_agent.py\u003C/code\u003E:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eimport os\nfrom cortex_agent_sdk import CortexAgentClient\nfrom cortex_agent_sdk.auth import PatAuth\n\nclient = CortexAgentClient(\n    account=os.environ["SNOWFLAKE_ACCOUNT"],\n    auth=PatAuth(os.environ["SNOWFLAKE_PAT"]),\n)\n\n\ndef print_coding_events(stream):\n    """Process and display coding agent events."""\n    text_parts = []\n    for event in stream:\n        delta = event.get("delta", {})\n        for item in delta.get("content", []):\n            item_type = item.get("type", "")\n            if item_type == "text":\n                text = item.get("text", {})\n                val = text.get("value", text) if isinstance(text, dict) else text\n                text_parts.append(str(val))\n            elif item_type == "tool_use":\n                tool = item.get("tool_use", {})\n                print(f"  [TOOL] {tool.get('name', 'unknown')}")\n            elif item_type == "tool_results":\n                tr = item.get("tool_results", {})\n                print(f"  [RESULT] status: {tr.get('status', 'unknown')}")\n    return "".join(text_parts)\n\n\n# Single coding agent run\nprint("=== Coding agent: create a summary table ===\\n")\n\nbody = {\n    "model": "auto",\n    "messages": [\n        {\n            "role": "user",\n            "content": [\n                {\n                    "type": "text",\n                    "text": (\n                        "Query the AGENT_SDK_DEMO_DB.QUICKSTART.SALES table, "\n                        "calculate total revenue and units sold per region, "\n                        "and create a new table called AGENT_SDK_DEMO_DB.QUICKSTART.REGIONAL_SUMMARY "\n                        "with the results. Show me the SQL you ran and the final table contents."\n                    ),\n                }\n            ],\n        }\n    ],\n    "permission_policy": "always_allow",\n}\n\nstream = client.coding_agent.stream(body)\ntext = print_coding_events(stream)\nif text:\n    print(f"\\nAgent response:\\n{text[:1000]}")\n\n# Multi-turn coding session\nprint("\\n\\n=== Multi-turn coding session ===\\n")\n\nthread = client.threads.create(origin_application="sdk-quickstart")\nthread_id = thread.get("thread_id") or thread.get("id")\n\n# Turn 1: Analyze\nprint("User: Analyze the SALES table — what patterns do you see?\\n")\nbody2 = {\n    "model": "auto",\n    "messages": [\n        {\n            "role": "user",\n            "content": [\n                {\n                    "type": "text",\n                    "text": "Analyze the AGENT_SDK_DEMO_DB.QUICKSTART.SALES table. "\n                    "Run some queries to find interesting patterns.",\n                }\n            ],\n        }\n    ],\n    "permission_policy": "always_allow",\n    "thread_id": thread_id,\n}\nstream2 = client.coding_agent.stream(body2)\ntext2 = print_coding_events(stream2)\nif text2:\n    print(f"\\nAgent: {text2[:500]}...")\n\n# Turn 2: Build on findings\nprint("\\n\\nUser: Create a view based on what you found.\\n")\nbody3 = {\n    "model": "auto",\n    "messages": [\n        {\n            "role": "user",\n            "content": [\n                {\n                    "type": "text",\n                    "text": "Based on your analysis, create a useful view in AGENT_SDK_DEMO_DB.QUICKSTART "\n                    "that highlights the most interesting pattern you found.",\n                }\n            ],\n        }\n    ],\n    "permission_policy": "always_allow",\n    "thread_id": thread_id,\n}\nstream3 = client.coding_agent.stream(body3)\ntext3 = print_coding_events(stream3)\nif text3:\n    print(f"\\nAgent: {text3[:500]}...")\n\nclient.threads.delete(thread_id)\nclient.close()\nprint("\\nDone!")\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EKey patterns:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003E\u003Ccode\u003Eclient.coding_agent.stream(body)\u003C/code\u003E\u003C/strong\u003E runs an agent with full code execution capabilities\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003E\u003Ccode\u003Epermission_policy: "always_allow"\u003C/code\u003E\u003C/strong\u003E auto-approves all tool calls (use \u003Ccode\u003E"always_ask"\u003C/code\u003E for interactive approval). Since the agent can execute SQL and bash commands, always use a \u003Cstrong\u003Eleast-privilege role\u003C/strong\u003E like \u003Ccode\u003EAGENT_SDK_DEMO_ROLE\u003C/code\u003E rather than ACCOUNTADMIN.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003E\u003Ccode\u003Ethread_id\u003C/code\u003E\u003C/strong\u003E links multi-turn coding sessions so the agent builds on its own previous work\u003C/li\u003E\u003Cli\u003EThe agent can execute SQL, create objects, and analyze data autonomously\u003C/li\u003E\u003C/ul\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003ENOTE: The coding agent has real write access to your Snowflake account. Use appropriate roles and permissions in production.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EAutomated Data Engineering Ops Agent\u003C/h2\u003E\n","\u003Cp\u003EThis is the showcase: an all-in-one ops agent that monitors pipeline health, checks data quality, and diagnoses incidents — all programmatically via the SDK.\u003C/p\u003E\n","\u003Cp\u003ECreate \u003Ccode\u003E05_data_eng_ops_agent.py\u003C/code\u003E:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eimport os\nfrom datetime import datetime\nfrom cortex_agent_sdk import CortexAgentClient\nfrom cortex_agent_sdk.auth import PatAuth\n\nclient = CortexAgentClient(\n    account=os.environ["SNOWFLAKE_ACCOUNT"],\n    auth=PatAuth(os.environ["SNOWFLAKE_PAT"]),\n)\n\nCODING_OPTS = {\n    "model": "auto",\n    "permission_policy": "always_allow",\n}\n\n\ndef extract_text(stream):\n    """Extract text content from coding agent stream events."""\n    text_parts = []\n    for event in stream:\n        delta = event.get("delta", {})\n        for item in delta.get("content", []):\n            if item.get("type") == "text":\n                text = item.get("text", {})\n                val = text.get("value", text) if isinstance(text, dict) else text\n                text_parts.append(str(val))\n    return "".join(text_parts) if text_parts else "(No text response)"\n\n\ndef run_check(client, thread_id, prompt):\n    """Run a coding agent turn on a thread and return the text response."""\n    body = {\n        **CODING_OPTS,\n        "messages": [\n            {"role": "user", "content": [{"type": "text", "text": prompt}]}\n        ],\n        "thread_id": thread_id,\n    }\n    stream = client.coding_agent.stream(body)\n    return extract_text(stream)\n\n\ndef main():\n    print("=" * 70)\n    print("  DAILY DATA ENGINEERING OPS REPORT")\n    print(f"  Generated: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")\n    print("=" * 70)\n\n    thread = client.threads.create(origin_application="ops-agent")\n    thread_id = thread.get("thread_id") or thread.get("id")\n\n    # Pipeline Health\n    print("\\n[1/3] Running pipeline health check...\\n")\n    pipeline_report = run_check(\n        client,\n        thread_id,\n        """You are a data engineering ops agent. Perform a pipeline health check:\n\n1. Query SNOWFLAKE.ACCOUNT_USAGE.TASK_HISTORY for tasks that FAILED or were\n   CANCELLED in the last 24 hours. Show the task name, database, schema,\n   error message, and scheduled time.\n\n2. Check if any tasks are stuck by looking for tasks that have been running\n   for more than 30 minutes.\n\n3. Summarize your findings in this exact format:\n   - Total failures in last 24h: <count>\n   - Critical failures (list each with task name, error, and database.schema)\n   - Stuck tasks (if any)\n   - Overall pipeline health: HEALTHY / DEGRADED / CRITICAL\n\nIf there are no failures, report HEALTHY status.""",\n    )\n    print(pipeline_report)\n\n    # Data Quality\n    print("\\n" + "-" * 70)\n    print("\\n[2/3] Running data quality check...\\n")\n    quality_report = run_check(\n        client,\n        thread_id,\n        """You are a data engineering ops agent. Perform a data quality check:\n\n1. Query SNOWFLAKE.ACCOUNT_USAGE.DATA_QUALITY_MONITORING_RESULTS for any\n   DMF violations in the last 24 hours. If this view is not available or\n   returns no results, note that and move on.\n\n2. Check table freshness: query SNOWFLAKE.ACCOUNT_USAGE.TABLE_STORAGE_METRICS\n   or INFORMATION_SCHEMA.TABLES to find tables that haven't been updated\n   in the last 48 hours (look at last_altered or similar columns) in\n   databases that are NOT system databases (skip SNOWFLAKE, SNOWFLAKE_SAMPLE_DATA).\n   Limit to 10 stalest tables.\n\n3. Summarize your findings:\n   - DMF violations: <count> (list each with table, metric, value)\n   - Stale tables: <count> (list the top 5 stalest with last update time)\n   - Overall data quality: GOOD / WARNING / CRITICAL""",\n    )\n    print(quality_report)\n\n    # Incident Diagnosis\n    needs_diagnosis = any(\n        keyword in pipeline_report.lower() + quality_report.lower()\n        for keyword in ["critical", "degraded", "failed", "violation"]\n    )\n\n    if needs_diagnosis:\n        print("\\n" + "-" * 70)\n        print("\\n[3/3] Critical issues detected — running incident diagnosis...\\n")\n        incident_report = run_check(\n            client,\n            thread_id,\n            f"""You are a senior data engineering ops agent. Based on the pipeline\nhealth and data quality checks you just ran, diagnose root causes and\nsuggest concrete fixes.\n\nFor each critical or degraded finding:\n1. Investigate the root cause — run additional queries if needed\n   (check object dependencies, recent DDL changes, permission issues)\n2. Suggest a specific fix (SQL command, configuration change, or escalation)\n3. Rate the urgency: P1 (fix now), P2 (fix today), P3 (fix this week)\n\nFormat your response as an incident report with clear sections.""",\n        )\n        print(incident_report)\n    else:\n        print("\\n" + "-" * 70)\n        print("\\n[3/3] No critical issues detected. Skipping incident diagnosis.")\n\n    print("\\n" + "=" * 70)\n    print("  OPS REPORT COMPLETE")\n    print("=" * 70)\n\n    client.threads.delete(thread_id)\n    client.close()\n\n\nif __name__ == "__main__":\n    main()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EHow It Works\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003E\u003Cstrong\u003EPipeline Health Check\u003C/strong\u003E — the coding agent queries \u003Ccode\u003ETASK_HISTORY\u003C/code\u003E for failures, identifies stuck tasks, and rates overall health\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EData Quality Check\u003C/strong\u003E — the agent looks for DMF violations and stale tables, rates overall quality\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EIncident Diagnosis\u003C/strong\u003E — if anything is CRITICAL or DEGRADED, the agent investigates root causes and suggests specific fixes with urgency ratings\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EAll three steps run on the \u003Cstrong\u003Esame thread\u003C/strong\u003E, so the agent has full context from earlier checks when diagnosing incidents.\u003C/p\u003E\n","\u003Ch3\u003ERunning It\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Epython 05_data_eng_ops_agent.py\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EExtending It\u003C/h3\u003E\n","\u003Cp\u003EThis pattern is designed to be extended:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ESchedule it\u003C/strong\u003E with a Snowflake Task or cron job\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESend notifications\u003C/strong\u003E via webhook (Slack, PagerDuty, email) based on severity\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EStore reports\u003C/strong\u003E in a Snowflake table for historical trending\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAdd more checks\u003C/strong\u003E — Snowpipe latency, warehouse queue depth, credit consumption anomalies\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion And Resources\u003C/h2\u003E\n","\u003Cp\u003ECongratulations! You've built five progressively more powerful agent scripts using the Cortex Agent SDK.\u003C/p\u003E\n","\u003Ch3\u003EWhat You Learned\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to authenticate with the SDK using a programmatic access token\u003C/li\u003E\u003Cli\u003EHow to run lite agents with streaming and extract structured results\u003C/li\u003E\u003Cli\u003EHow to use background runs for asynchronous execution\u003C/li\u003E\u003Cli\u003EHow to build multi-turn conversations with persistent threads\u003C/li\u003E\u003Cli\u003EHow to use the coding agent sandbox for automated SQL execution\u003C/li\u003E\u003Cli\u003EHow to orchestrate multiple agent checks into an automated ops workflow\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ECleanup\u003C/h3\u003E\n","\u003Cp\u003ETo remove the demo objects created in this quickstart:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EDROP DATABASE IF EXISTS AGENT_SDK_DEMO_DB;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ERelated Resources\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://pypi.org/project/snowflake-cortex-agent-sdk/\"\u003ECortex Agent SDK on PyPI\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/overview\"\u003ESnowflake Cortex AI Documentation\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents\"\u003ECortex Agents Overview\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/programmatic-access-tokens\"\u003EProgrammatic Access Tokens\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E"],"description":"","title":"Getting Started with the Cortex Agent SDK",":type":"snowflake-site/components/contentfragment",":items":{},":itemsOrder":[],"elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\u003C!-- ------------------------ --\u003E\n## Overview\n\nThe [Snowflake Cortex Agent SDK](https://pypi.org/project/snowflake-cortex-agent-sdk/) gives you programmatic Python access to Snowflake Cortex Agents. Instead of clicking through Snowsight, you can build, run, and orchestrate agents from scripts, notebooks, CI/CD pipelines, and scheduled jobs.\n\nThis guide walks you from a first \"hello world\" agent run through streaming events, multi-turn conversations, a coding agent sandbox, and finally an automated data engineering ops agent that monitors your pipelines and data quality.\n\n### Prerequisites\n- A Snowflake account with Cortex AI enabled\n- Basic Python and SQL knowledge\n\n### What You'll Learn\n- How to authenticate with the SDK using a programmatic access token (PAT)\n- How to run a lite agent and stream its response\n- How to process streaming events and extract results\n- How to manage threads and build multi-turn conversations\n- How to run a coding agent in a sandboxed environment\n- How to build an automated data engineering ops agent\n\n### What You'll Need\n- A [Snowflake account](https://signup.snowflake.com/) (Enterprise edition or higher)\n- Python 3.10 or later\n- A programmatic access token (PAT) -- we'll create one in the Setup step\n\n### What You'll Build\n- Five progressively more advanced agent scripts\n- A production-ready automated ops agent that checks pipeline health, data quality, and diagnoses incidents\n\n\u003C!-- ------------------------ --\u003E\n## Setup\n\n### Install the SDK\n\n```bash\npip install snowflake-cortex-agent-sdk\n```\n\n### Create a Programmatic Access Token (PAT)\n\n1. In Snowsight, click your name in the bottom-left corner\n2. Go to **Settings** \u003E **Authentication**\n3. Under **Programmatic access tokens**, click **Generate Token**\n4. Give it a name (e.g., `agent-sdk-quickstart`) and copy the token\n\n### Set Environment Variables\n\n```bash\nexport SNOWFLAKE_ACCOUNT=\"your-account-identifier\"\nexport SNOWFLAKE_PAT=\"your-pat-token\"\n```\n\n\u003E NOTE: Your account identifier is the `\u003Corgname\u003E-\u003Caccountname\u003E` portion of your Snowflake URL (e.g., `myorg-myaccount`).\n\n### Create Demo Objects\n\nRun the following SQL in Snowsight or via SnowSQL to create the demo schema and sample data:\n\n```sql\nUSE ROLE SYSADMIN;\n\nCREATE DATABASE IF NOT EXISTS AGENT_SDK_DEMO_DB;\nCREATE SCHEMA IF NOT EXISTS AGENT_SDK_DEMO_DB.QUICKSTART;\n\n-- Create a demo role with limited access\nUSE ROLE USERADMIN;\nCREATE ROLE IF NOT EXISTS AGENT_SDK_DEMO_ROLE;\nGRANT ROLE AGENT_SDK_DEMO_ROLE TO USER IDENTIFIER(CURRENT_USER());\n\n-- Grant access to the demo objects only\nUSE ROLE SYSADMIN;\n\nCREATE OR REPLACE TABLE AGENT_SDK_DEMO_DB.QUICKSTART.SALES (\n    sale_date DATE,\n    region VARCHAR,\n    product VARCHAR,\n    units_sold INT,\n    revenue FLOAT\n);\n\nINSERT INTO AGENT_SDK_DEMO_DB.QUICKSTART.SALES VALUES\n    ('2026-10-01', 'North America', 'Widget A', 150, 4500.00),\n    ('2026-10-01', 'Europe', 'Widget A', 120, 3600.00),\n    ('2026-10-01', 'Asia Pacific', 'Widget B', 200, 8000.00),\n    ('2026-10-02', 'North America', 'Widget B', 180, 7200.00),\n    ('2026-10-02', 'Europe', 'Widget A', 90, 2700.00),\n    ('2026-10-02', 'Asia Pacific', 'Widget A', 160, 4800.00),\n    ('2026-10-03', 'North America', 'Widget A', 130, 3900.00),\n    ('2026-10-03', 'Europe', 'Widget B', 110, 4400.00),\n    ('2026-10-03', 'Asia Pacific', 'Widget B', 220, 8800.00),\n    ('2026-10-04', 'North America', 'Widget B', 170, 6800.00),\n    ('2026-10-04', 'Europe', 'Widget A', 140, 4200.00),\n    ('2026-10-04', 'Asia Pacific', 'Widget A', 190, 5700.00);\n\n-- Grant access to the demo role (not PUBLIC)\nGRANT USAGE ON DATABASE AGENT_SDK_DEMO_DB TO ROLE AGENT_SDK_DEMO_ROLE;\nGRANT USAGE ON SCHEMA AGENT_SDK_DEMO_DB.QUICKSTART TO ROLE AGENT_SDK_DEMO_ROLE;\nGRANT SELECT ON ALL TABLES IN SCHEMA AGENT_SDK_DEMO_DB.QUICKSTART TO ROLE AGENT_SDK_DEMO_ROLE;\nGRANT INSERT ON ALL TABLES IN SCHEMA AGENT_SDK_DEMO_DB.QUICKSTART TO ROLE AGENT_SDK_DEMO_ROLE;\nGRANT CREATE TABLE ON SCHEMA AGENT_SDK_DEMO_DB.QUICKSTART TO ROLE AGENT_SDK_DEMO_ROLE;\nGRANT CREATE VIEW ON SCHEMA AGENT_SDK_DEMO_DB.QUICKSTART TO ROLE AGENT_SDK_DEMO_ROLE;\nGRANT USAGE ON WAREHOUSE COMPUTE_WH TO ROLE AGENT_SDK_DEMO_ROLE;\n```\n\n\u003E NOTE: Replace `COMPUTE_WH` with your own warehouse name if different. Generate your PAT while using the `AGENT_SDK_DEMO_ROLE` role, or set the role in your connection config. This ensures the agent can only access the demo database.\n\nYou're ready to write your first agent script.\n\n\u003C!-- ------------------------ --\u003E\n## Your First Agent Run\n\nIn this step you'll connect to Snowflake and run a lite agent that answers a question about your sales data using the `cortex_analyst_text_to_sql` tool.\n\nThe agent takes an inline semantic model that describes your table's dimensions and measures, generates SQL to answer the question, and returns the results.\n\nCreate a file called `01_hello_agent.py`:\n\n```python\nimport os\nfrom cortex_agent_sdk import CortexAgentClient\nfrom cortex_agent_sdk.auth import PatAuth\n\nclient = CortexAgentClient(\n    account=os.environ[\"SNOWFLAKE_ACCOUNT\"],\n    auth=PatAuth(os.environ[\"SNOWFLAKE_PAT\"]),\n)\n\n# Inline semantic model describing the SALES table\nSEMANTIC_MODEL = \"\"\"\nname: sales_model\ntables:\n  - name: SALES\n    base_table:\n      database: AGENT_SDK_DEMO_DB\n      schema: QUICKSTART\n      table: SALES\n    dimensions:\n      - name: sale_date\n        expr: sale_date\n        data_type: DATE\n      - name: region\n        expr: region\n        data_type: VARCHAR\n      - name: product\n        expr: product\n        data_type: VARCHAR\n    measures:\n      - name: units_sold\n        expr: units_sold\n        data_type: INT\n        default_aggregation: sum\n      - name: revenue\n        expr: revenue\n        data_type: FLOAT\n        default_aggregation: sum\n\"\"\"\n\n\ndef extract_analyst_response(events):\n    \"\"\"Extract SQL, text explanation, and results from analyst tool events.\"\"\"\n    for event in events:\n        delta = event.get(\"delta\", {})\n        for item in delta.get(\"content\", []):\n            if item.get(\"type\") == \"tool_results\":\n                tr = item.get(\"tool_results\", {})\n                for c in tr.get(\"content\", []):\n                    if c.get(\"type\") == \"json\":\n                        return c.get(\"json\", {})\n    return {}\n\n\nbody = {\n    \"model\": \"auto\",\n    \"messages\": [\n        {\n            \"role\": \"user\",\n            \"content\": [\n                {\"type\": \"text\", \"text\": \"What are the total sales by region?\"}\n            ],\n        }\n    ],\n    \"tools\": [\n        {\n            \"tool_spec\": {\n                \"type\": \"cortex_analyst_text_to_sql\",\n                \"name\": \"sales_analyst\",\n            }\n        }\n    ],\n    \"tool_resources\": {\n        \"sales_analyst\": {\"inline_semantic_model\": SEMANTIC_MODEL}\n    },\n}\n\n# Stream and extract the response\nprint(\"=== Streaming agent run ===\\n\")\n\nstream = client.agent.stream(body)\nevents = list(stream)\n\nresult = extract_analyst_response(events)\n\nprint(f\"Interpretation: {result.get('text', '(none)')}\\n\")\nprint(f\"Generated SQL:\\n{result.get('sql', '(none)')}\\n\")\n\nrows = result.get(\"results\", [])\nif rows:\n    print(f\"Results ({len(rows)} rows):\")\n    for row in rows:\n        print(f\"  {row}\")\n\nclient.close()\nprint(\"\\nDone!\")\n```\n\nRun it:\n\n```bash\npython 01_hello_agent.py\n```\n\nYou should see the agent interpret your question, generate SQL to query the `SALES` table, and return the results grouped by region.\n\nKey concepts:\n- **`CortexAgentClient`** is the main entry point — pass your account and a `PatAuth` token\n- **`client.agent.stream(body)`** sends a request and returns an event iterator\n- **Tool spec** uses `cortex_analyst_text_to_sql` with an inline semantic model that maps your table structure\n- The response includes both the generated SQL and a natural language interpretation\n\n\u003C!-- ------------------------ --\u003E\n## Streaming and Callbacks\n\nIn this step you'll process events as they arrive and use background runs for asynchronous execution.\n\nCreate `02_streaming_callbacks.py`:\n\n```python\nimport os\nfrom cortex_agent_sdk import CortexAgentClient\nfrom cortex_agent_sdk.auth import PatAuth\n\nclient = CortexAgentClient(\n    account=os.environ[\"SNOWFLAKE_ACCOUNT\"],\n    auth=PatAuth(os.environ[\"SNOWFLAKE_PAT\"]),\n)\n\nSEMANTIC_MODEL = \"\"\"...\"\"\"\n# Copy the full SEMANTIC_MODEL from 01_hello_agent.py (the inline semantic model\n# describing AGENT_SDK_DEMO_DB.QUICKSTART.SALES)\n\n\ndef process_events(stream):\n    \"\"\"Process streaming events, printing each type as it arrives.\"\"\"\n    for event in stream:\n        delta = event.get(\"delta\", {})\n        for item in delta.get(\"content\", []):\n            item_type = item.get(\"type\", \"\")\n\n            if item_type == \"tool_use\":\n                tool = item.get(\"tool_use\", {})\n                print(f\"  [TOOL_USE] {tool.get('name')} — query: {tool.get('input', {}).get('query', '')}\")\n\n            elif item_type == \"tool_results\":\n                tr = item.get(\"tool_results\", {})\n                print(f\"  [TOOL_RESULT] status: {tr.get('status', 'unknown')}\")\n                for c in tr.get(\"content\", []):\n                    if c.get(\"type\") == \"json\":\n                        j = c.get(\"json\", {})\n                        print(f\"  [SQL] {j.get('sql', '')[:100]}...\")\n                        print(f\"  [TEXT] {j.get('text', '')}\")\n\n            elif item_type == \"text\":\n                text_val = item.get(\"text\", {})\n                if isinstance(text_val, dict):\n                    print(f\"  [TEXT] {text_val.get('value', '')}\")\n                else:\n                    print(f\"  [TEXT] {text_val}\")\n\n\nbody = {\n    \"model\": \"auto\",\n    \"messages\": [\n        {\n            \"role\": \"user\",\n            \"content\": [\n                {\"type\": \"text\", \"text\": \"Which product has the highest total revenue?\"}\n            ],\n        }\n    ],\n    \"tools\": [\n        {\"tool_spec\": {\"type\": \"cortex_analyst_text_to_sql\", \"name\": \"sales_analyst\"}}\n    ],\n    \"tool_resources\": {\n        \"sales_analyst\": {\"inline_semantic_model\": SEMANTIC_MODEL}\n    },\n}\n\n# Process events as they arrive\nprint(\"=== Processing events ===\\n\")\nstream = client.agent.stream(body)\nprocess_events(stream)\n\n# Background run\nprint(\"\\n\\n=== Background run ===\\n\")\n\nbg_body = dict(body)\nbg_body[\"messages\"] = [\n    {\"role\": \"user\", \"content\": [{\"type\": \"text\", \"text\": \"What are the daily revenue trends?\"}]}\n]\n\nbg_body[\"stream\"] = False\nbg_body[\"background\"] = True\nbg_run = client.agent.run(bg_body)\nprint(f\"Run started: {bg_run.run_id}\")\nprint(\"Waiting for completion...\\n\")\n\nresponse = bg_run.wait()\nfor item in response.content:\n    if item.get(\"type\") == \"tool_results\":\n        tr = item.get(\"tool_results\", {})\n        for c in tr.get(\"content\", []):\n            if c.get(\"type\") == \"json\":\n                j = c.get(\"json\", {})\n                print(f\"SQL: {j.get('sql', '')[:150]}...\")\n                print(f\"Interpretation: {j.get('text', '')}\")\n\nclient.close()\nprint(\"\\nDone!\")\n```\n\nKey patterns:\n\n- **Event processing** — iterate the stream and switch on `item[\"type\"]` to handle `tool_use`, `tool_results`, and `text` events differently\n- **Background runs** — set `stream=False` and `background=True` in the body dict, then call `client.agent.run(body)` to get a `BackgroundRun` handle; call `.wait()` to block until done, or `.cancel()` to abort\n- This is useful for kicking off multiple agent runs in parallel\n\n\u003C!-- ------------------------ --\u003E\n## Threads and Conversations\n\nThreads persist message history across turns. You create a thread, then pass `thread_id` on subsequent runs so the agent has context from previous turns.\n\nCreate `03_conversations.py`:\n\n```python\nimport os\nfrom cortex_agent_sdk import CortexAgentClient\nfrom cortex_agent_sdk.auth import PatAuth\n\nclient = CortexAgentClient(\n    account=os.environ[\"SNOWFLAKE_ACCOUNT\"],\n    auth=PatAuth(os.environ[\"SNOWFLAKE_PAT\"]),\n)\n\nSEMANTIC_MODEL = \"\"\"...\"\"\"\n# Copy the full SEMANTIC_MODEL from 01_hello_agent.py (the inline semantic model\n# describing AGENT_SDK_DEMO_DB.QUICKSTART.SALES)\n\n\ndef extract_text(events):\n    \"\"\"Extract the analyst text explanation from events.\"\"\"\n    for event in events:\n        delta = event.get(\"delta\", {})\n        for item in delta.get(\"content\", []):\n            if item.get(\"type\") == \"tool_results\":\n                tr = item.get(\"tool_results\", {})\n                for c in tr.get(\"content\", []):\n                    if c.get(\"type\") == \"json\":\n                        return c[\"json\"].get(\"text\", \"\")\n    return \"\"\n\n\ndef run_on_thread(client, thread_id, question):\n    \"\"\"Run an agent turn on an existing thread.\"\"\"\n    body = {\n        \"model\": \"auto\",\n        \"messages\": [\n            {\"role\": \"user\", \"content\": [{\"type\": \"text\", \"text\": question}]},\n        ],\n        \"tools\": [\n            {\"tool_spec\": {\"type\": \"cortex_analyst_text_to_sql\", \"name\": \"sales_analyst\"}}\n        ],\n        \"tool_resources\": {\n            \"sales_analyst\": {\"inline_semantic_model\": SEMANTIC_MODEL}\n        },\n        \"thread_id\": thread_id,\n    }\n    stream = client.agent.stream(body)\n    events = list(stream)\n    return extract_text(events)\n\n\n# Create a thread\nprint(\"=== Multi-turn conversation ===\\n\")\n\nthread = client.threads.create(origin_application=\"sdk-quickstart\")\nthread_id = thread.get(\"thread_id\") or thread.get(\"id\")\nprint(f\"Thread created: {thread_id}\\n\")\n\n# Turn 1\nprint(\"User: What are the top selling products?\\n\")\ntext = run_on_thread(client, thread_id, \"What are the top selling products?\")\nprint(f\"Agent: {text}\\n\")\nprint(\"-\" * 60)\n\n# Turn 2 — the agent has context from turn 1\nprint(\"\\nUser: Break that down by region.\\n\")\ntext = run_on_thread(client, thread_id, \"Break that down by region.\")\nprint(f\"Agent: {text}\\n\")\nprint(\"-\" * 60)\n\n# Turn 3\nprint(\"\\nUser: Which region shows the strongest performance?\\n\")\ntext = run_on_thread(client, thread_id, \"Which region shows the strongest performance?\")\nprint(f\"Agent: {text}\\n\")\n\n# Thread operations\nprint(\"\\n=== Thread operations ===\\n\")\n\nclient.threads.update(thread_id, thread_name=\"Sales Analysis Q4\")\nprint(\"Thread renamed to 'Sales Analysis Q4'\")\n\ndesc = client.threads.describe(thread_id)\nmessages = desc.get(\"messages\", [])\nprint(f\"Messages in thread: {len(messages)}\")\n\n# Search\nresults = client.threads.search(query=\"sales\", limit=5)\nthreads = results.get(\"threads\", [])\nprint(f\"Found {len(threads)} threads matching 'sales'\")\n\n# Clean up\nclient.threads.delete(thread_id)\nprint(\"\\nThread deleted. Done!\")\nclient.close()\n```\n\nKey patterns:\n\n- **`client.threads.create()`** creates a new thread — pass `origin_application` to tag it\n- **`thread_id`** on subsequent runs links them to the same conversation\n- **`client.threads.search()`** finds threads by keyword (fuzzy or regex)\n- **`client.threads.update()`** renames threads; **`.delete()`** removes them\n\n\u003C!-- ------------------------ --\u003E\n## Coding Agent Sandbox\n\nThe coding agent runs in a sandboxed environment with `code_toolset_all` — it can execute SQL, run bash commands, read/write files, and use skills. This is powerful for automated data engineering tasks.\n\nCreate `04_coding_agent.py`:\n\n```python\nimport os\nfrom cortex_agent_sdk import CortexAgentClient\nfrom cortex_agent_sdk.auth import PatAuth\n\nclient = CortexAgentClient(\n    account=os.environ[\"SNOWFLAKE_ACCOUNT\"],\n    auth=PatAuth(os.environ[\"SNOWFLAKE_PAT\"]),\n)\n\n\ndef print_coding_events(stream):\n    \"\"\"Process and display coding agent events.\"\"\"\n    text_parts = []\n    for event in stream:\n        delta = event.get(\"delta\", {})\n        for item in delta.get(\"content\", []):\n            item_type = item.get(\"type\", \"\")\n            if item_type == \"text\":\n                text = item.get(\"text\", {})\n                val = text.get(\"value\", text) if isinstance(text, dict) else text\n                text_parts.append(str(val))\n            elif item_type == \"tool_use\":\n                tool = item.get(\"tool_use\", {})\n                print(f\"  [TOOL] {tool.get('name', 'unknown')}\")\n            elif item_type == \"tool_results\":\n                tr = item.get(\"tool_results\", {})\n                print(f\"  [RESULT] status: {tr.get('status', 'unknown')}\")\n    return \"\".join(text_parts)\n\n\n# Single coding agent run\nprint(\"=== Coding agent: create a summary table ===\\n\")\n\nbody = {\n    \"model\": \"auto\",\n    \"messages\": [\n        {\n            \"role\": \"user\",\n            \"content\": [\n                {\n                    \"type\": \"text\",\n                    \"text\": (\n                        \"Query the AGENT_SDK_DEMO_DB.QUICKSTART.SALES table, \"\n                        \"calculate total revenue and units sold per region, \"\n                        \"and create a new table called AGENT_SDK_DEMO_DB.QUICKSTART.REGIONAL_SUMMARY \"\n                        \"with the results. Show me the SQL you ran and the final table contents.\"\n                    ),\n                }\n            ],\n        }\n    ],\n    \"permission_policy\": \"always_allow\",\n}\n\nstream = client.coding_agent.stream(body)\ntext = print_coding_events(stream)\nif text:\n    print(f\"\\nAgent response:\\n{text[:1000]}\")\n\n# Multi-turn coding session\nprint(\"\\n\\n=== Multi-turn coding session ===\\n\")\n\nthread = client.threads.create(origin_application=\"sdk-quickstart\")\nthread_id = thread.get(\"thread_id\") or thread.get(\"id\")\n\n# Turn 1: Analyze\nprint(\"User: Analyze the SALES table — what patterns do you see?\\n\")\nbody2 = {\n    \"model\": \"auto\",\n    \"messages\": [\n        {\n            \"role\": \"user\",\n            \"content\": [\n                {\n                    \"type\": \"text\",\n                    \"text\": \"Analyze the AGENT_SDK_DEMO_DB.QUICKSTART.SALES table. \"\n                    \"Run some queries to find interesting patterns.\",\n                }\n            ],\n        }\n    ],\n    \"permission_policy\": \"always_allow\",\n    \"thread_id\": thread_id,\n}\nstream2 = client.coding_agent.stream(body2)\ntext2 = print_coding_events(stream2)\nif text2:\n    print(f\"\\nAgent: {text2[:500]}...\")\n\n# Turn 2: Build on findings\nprint(\"\\n\\nUser: Create a view based on what you found.\\n\")\nbody3 = {\n    \"model\": \"auto\",\n    \"messages\": [\n        {\n            \"role\": \"user\",\n            \"content\": [\n                {\n                    \"type\": \"text\",\n                    \"text\": \"Based on your analysis, create a useful view in AGENT_SDK_DEMO_DB.QUICKSTART \"\n                    \"that highlights the most interesting pattern you found.\",\n                }\n            ],\n        }\n    ],\n    \"permission_policy\": \"always_allow\",\n    \"thread_id\": thread_id,\n}\nstream3 = client.coding_agent.stream(body3)\ntext3 = print_coding_events(stream3)\nif text3:\n    print(f\"\\nAgent: {text3[:500]}...\")\n\nclient.threads.delete(thread_id)\nclient.close()\nprint(\"\\nDone!\")\n```\n\nKey patterns:\n\n- **`client.coding_agent.stream(body)`** runs an agent with full code execution capabilities\n- **`permission_policy: \"always_allow\"`** auto-approves all tool calls (use `\"always_ask\"` for interactive approval). Since the agent can execute SQL and bash commands, always use a **least-privilege role** like `AGENT_SDK_DEMO_ROLE` rather than ACCOUNTADMIN.\n- **`thread_id`** links multi-turn coding sessions so the agent builds on its own previous work\n- The agent can execute SQL, create objects, and analyze data autonomously\n\n\u003E NOTE: The coding agent has real write access to your Snowflake account. Use appropriate roles and permissions in production.\n\n\u003C!-- ------------------------ --\u003E\n## Automated Data Engineering Ops Agent\n\nThis is the showcase: an all-in-one ops agent that monitors pipeline health, checks data quality, and diagnoses incidents — all programmatically via the SDK.\n\nCreate `05_data_eng_ops_agent.py`:\n\n```python\nimport os\nfrom datetime import datetime\nfrom cortex_agent_sdk import CortexAgentClient\nfrom cortex_agent_sdk.auth import PatAuth\n\nclient = CortexAgentClient(\n    account=os.environ[\"SNOWFLAKE_ACCOUNT\"],\n    auth=PatAuth(os.environ[\"SNOWFLAKE_PAT\"]),\n)\n\nCODING_OPTS = {\n    \"model\": \"auto\",\n    \"permission_policy\": \"always_allow\",\n}\n\n\ndef extract_text(stream):\n    \"\"\"Extract text content from coding agent stream events.\"\"\"\n    text_parts = []\n    for event in stream:\n        delta = event.get(\"delta\", {})\n        for item in delta.get(\"content\", []):\n            if item.get(\"type\") == \"text\":\n                text = item.get(\"text\", {})\n                val = text.get(\"value\", text) if isinstance(text, dict) else text\n                text_parts.append(str(val))\n    return \"\".join(text_parts) if text_parts else \"(No text response)\"\n\n\ndef run_check(client, thread_id, prompt):\n    \"\"\"Run a coding agent turn on a thread and return the text response.\"\"\"\n    body = {\n        **CODING_OPTS,\n        \"messages\": [\n            {\"role\": \"user\", \"content\": [{\"type\": \"text\", \"text\": prompt}]}\n        ],\n        \"thread_id\": thread_id,\n    }\n    stream = client.coding_agent.stream(body)\n    return extract_text(stream)\n\n\ndef main():\n    print(\"=\" * 70)\n    print(\"  DAILY DATA ENGINEERING OPS REPORT\")\n    print(f\"  Generated: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\")\n    print(\"=\" * 70)\n\n    thread = client.threads.create(origin_application=\"ops-agent\")\n    thread_id = thread.get(\"thread_id\") or thread.get(\"id\")\n\n    # Pipeline Health\n    print(\"\\n[1/3] Running pipeline health check...\\n\")\n    pipeline_report = run_check(\n        client,\n        thread_id,\n        \"\"\"You are a data engineering ops agent. Perform a pipeline health check:\n\n1. Query SNOWFLAKE.ACCOUNT_USAGE.TASK_HISTORY for tasks that FAILED or were\n   CANCELLED in the last 24 hours. Show the task name, database, schema,\n   error message, and scheduled time.\n\n2. Check if any tasks are stuck by looking for tasks that have been running\n   for more than 30 minutes.\n\n3. Summarize your findings in this exact format:\n   - Total failures in last 24h: \u003Ccount\u003E\n   - Critical failures (list each with task name, error, and database.schema)\n   - Stuck tasks (if any)\n   - Overall pipeline health: HEALTHY / DEGRADED / CRITICAL\n\nIf there are no failures, report HEALTHY status.\"\"\",\n    )\n    print(pipeline_report)\n\n    # Data Quality\n    print(\"\\n\" + \"-\" * 70)\n    print(\"\\n[2/3] Running data quality check...\\n\")\n    quality_report = run_check(\n        client,\n        thread_id,\n        \"\"\"You are a data engineering ops agent. Perform a data quality check:\n\n1. Query SNOWFLAKE.ACCOUNT_USAGE.DATA_QUALITY_MONITORING_RESULTS for any\n   DMF violations in the last 24 hours. If this view is not available or\n   returns no results, note that and move on.\n\n2. Check table freshness: query SNOWFLAKE.ACCOUNT_USAGE.TABLE_STORAGE_METRICS\n   or INFORMATION_SCHEMA.TABLES to find tables that haven't been updated\n   in the last 48 hours (look at last_altered or similar columns) in\n   databases that are NOT system databases (skip SNOWFLAKE, SNOWFLAKE_SAMPLE_DATA).\n   Limit to 10 stalest tables.\n\n3. Summarize your findings:\n   - DMF violations: \u003Ccount\u003E (list each with table, metric, value)\n   - Stale tables: \u003Ccount\u003E (list the top 5 stalest with last update time)\n   - Overall data quality: GOOD / WARNING / CRITICAL\"\"\",\n    )\n    print(quality_report)\n\n    # Incident Diagnosis\n    needs_diagnosis = any(\n        keyword in pipeline_report.lower() + quality_report.lower()\n        for keyword in [\"critical\", \"degraded\", \"failed\", \"violation\"]\n    )\n\n    if needs_diagnosis:\n        print(\"\\n\" + \"-\" * 70)\n        print(\"\\n[3/3] Critical issues detected — running incident diagnosis...\\n\")\n        incident_report = run_check(\n            client,\n            thread_id,\n            f\"\"\"You are a senior data engineering ops agent. Based on the pipeline\nhealth and data quality checks you just ran, diagnose root causes and\nsuggest concrete fixes.\n\nFor each critical or degraded finding:\n1. Investigate the root cause — run additional queries if needed\n   (check object dependencies, recent DDL changes, permission issues)\n2. Suggest a specific fix (SQL command, configuration change, or escalation)\n3. Rate the urgency: P1 (fix now), P2 (fix today), P3 (fix this week)\n\nFormat your response as an incident report with clear sections.\"\"\",\n        )\n        print(incident_report)\n    else:\n        print(\"\\n\" + \"-\" * 70)\n        print(\"\\n[3/3] No critical issues detected. Skipping incident diagnosis.\")\n\n    print(\"\\n\" + \"=\" * 70)\n    print(\"  OPS REPORT COMPLETE\")\n    print(\"=\" * 70)\n\n    client.threads.delete(thread_id)\n    client.close()\n\n\nif __name__ == \"__main__\":\n    main()\n```\n\n### How It Works\n\n1. **Pipeline Health Check** — the coding agent queries `TASK_HISTORY` for failures, identifies stuck tasks, and rates overall health\n2. **Data Quality Check** — the agent looks for DMF violations and stale tables, rates overall quality\n3. **Incident Diagnosis** — if anything is CRITICAL or DEGRADED, the agent investigates root causes and suggests specific fixes with urgency ratings\n\nAll three steps run on the **same thread**, so the agent has full context from earlier checks when diagnosing incidents.\n\n### Running It\n\n```bash\npython 05_data_eng_ops_agent.py\n```\n\n### Extending It\n\nThis pattern is designed to be extended:\n\n- **Schedule it** with a Snowflake Task or cron job\n- **Send notifications** via webhook (Slack, PagerDuty, email) based on severity\n- **Store reports** in a Snowflake table for historical trending\n- **Add more checks** — Snowpipe latency, warehouse queue depth, credit consumption anomalies\n\n\u003C!-- ------------------------ --\u003E\n## Conclusion And Resources\n\nCongratulations! You've built five progressively more powerful agent scripts using the Cortex Agent SDK.\n\n### What You Learned\n- How to authenticate with the SDK using a programmatic access token\n- How to run lite agents with streaming and extract structured results\n- How to use background runs for asynchronous execution\n- How to build multi-turn conversations with persistent threads\n- How to use the coding agent sandbox for automated SQL execution\n- How to orchestrate multiple agent checks into an automated ops workflow\n\n### Cleanup\n\nTo remove the demo objects created in this quickstart:\n\n```sql\nDROP DATABASE IF EXISTS AGENT_SDK_DEMO_DB;\n```\n\n### Related Resources\n- [Cortex Agent SDK on PyPI](https://pypi.org/project/snowflake-cortex-agent-sdk/)\n- [Snowflake Cortex AI Documentation](https://docs.snowflake.com/en/user-guide/snowflake-cortex/overview)\n- [Cortex Agents Overview](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents)\n- [Programmatic Access Tokens](https://docs.snowflake.com/en/user-guide/programmatic-access-tokens)\n","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],"isDeveloperGuidesPage":false,"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-fcd749dc24","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-41a3fbd888",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-b1fac4724c","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2026-10-09",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-edf55a03a7","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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First Agent Run\u003C/h2\u003E","\u003Ch2\u003EStreaming and Callbacks\u003C/h2\u003E","\u003Ch2\u003EThreads and Conversations\u003C/h2\u003E","\u003Ch2\u003ECoding Agent Sandbox\u003C/h2\u003E","\u003Ch2\u003EAutomated Data Engineering Ops Agent\u003C/h2\u003E","\u003Ch2\u003EConclusion And 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text-color-text-04"},"marketo_v2":{"id":"marketo-v2-81c3c51284","marketoForm":{"hidden":null,"edit":false,"formId":"45871","successUrl":null,"script":null,"values":null},"munchkinId":"252-RFO-227","serverInstance":"252-RFO-227.mktoweb.com","formConfigured":true,"partnerCampaignPage":false,"marketoConfigured":true,":type":"snowflake-site/components/form/marketo-v2"}},":itemsOrder":["text","marketo_v2"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small"}},":itemsOrder":["container"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small"},"container":{"layout":"RESPONSIVE_GRID","columnCount":12,"columnClassNames":{"text_copy":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 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href=\"https://www.snowflake.com/en/product/applications-and-collaboration/\"\u003EApplications & Collaboration\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/pricing-options/\"\u003EPricing\u003C/a\u003E\u003C/li\u003E\r\n\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-small text-color-text-04"},"text_copy":{"id":"text-f645c6b847","additionalClasses":"sf-footer__link-group","text":"\u003Cp class=\"sf-footer__column-title\"\u003ESupport\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/support/\"\u003ESupport\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/legal/addenda/priority-support-services-description/\"\u003EPriority Support\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://status.snowflake.com/\" target=\"_blank\" rel=\"noopener 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