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A task suspends, a dynamic table stops refreshing, a stream goes stale -- and nobody notices until a dashboard goes blank or a downstream consumer raises an alarm. Manual health checks don't scale, and static monitors only catch what you thought to look for.\u003C/p\u003E\n","\u003Cp\u003EIn this guide you will build a \u003Cstrong\u003EPipeline Auditor\u003C/strong\u003E -- a full-stack application that uses a Cortex AI coding agent to automatically discover every object in a Snowflake database (or a single schema), run health checks against each one, and produce a structured JSON report with severity-rated findings. When something looks wrong, the agent can suggest a fix and even execute it for you.\u003C/p\u003E\n","\u003Cp\u003EThe app runs on \u003Cstrong\u003ESnowflake App Runtime\u003C/strong\u003E (SAR) as a Next.js application and uses the \u003Cstrong\u003E@snowflake/cortex-agent-sdk\u003C/strong\u003E TypeScript SDK to communicate with the coding agent.\u003C/p\u003E\n","\u003Ch3\u003EWhat You'll Need\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA Snowflake account with Cortex AI enabled (Enterprise edition or higher)\u003C/li\u003E\u003Cli\u003ENode.js 22+ and npm\u003C/li\u003E\u003Cli\u003ESnowflake CLI (\u003Ccode\u003Esnow\u003C/code\u003E) installed and configured\u003C/li\u003E\u003Cli\u003EBasic TypeScript and React knowledge\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to scaffold and deploy a Next.js app on Snowflake App Runtime\u003C/li\u003E\u003Cli\u003EHow to use \u003Ccode\u003E@snowflake/cortex-agent-sdk\u003C/code\u003E to run coding agent sessions\u003C/li\u003E\u003Cli\u003EHow to build composable system prompts that produce structured JSON output\u003C/li\u003E\u003Cli\u003EHow to create a poll-based streaming architecture for real-time progress\u003C/li\u003E\u003Cli\u003EHow to implement AI-powered fix suggestions with multi-turn chat\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Build\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA full-stack pipeline auditor with:\n\u003Cul\u003E\u003Cli\u003EDatabase and schema discovery\u003C/li\u003E\u003Cli\u003EConfigurable audit scope (tables, dynamic tables, tasks, views, streams, pipes, procedures)\u003C/li\u003E\u003Cli\u003EReal-time audit progress tracking via a sidebar\u003C/li\u003E\u003Cli\u003EStructured health reports with severity ratings (critical / warning / info)\u003C/li\u003E\u003Cli\u003EAI-powered "Suggest Fix" with follow-up chat and SQL execution\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESetup\u003C/h2\u003E\n","\u003Cp\u003EDuration: 10\u003C/p\u003E\n","\u003Ch3\u003ECreate Snowflake Objects\u003C/h3\u003E\n","\u003Cp\u003ERun the following SQL in Snowsight to create the database the app will audit. You can also point the auditor at any existing database -- this one is just a convenient starting point.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE SYSADMIN;\n\nCREATE DATABASE IF NOT EXISTS PIPELINE_AUDITOR_DB;\nCREATE SCHEMA IF NOT EXISTS PIPELINE_AUDITOR_DB.AUDITOR;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EScaffold the App\u003C/h3\u003E\n","\u003Cp\u003EClone the companion repository and navigate to the project:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Egit clone https://github.com/sfc-gh-JCHAEARLEY/sfguide-pipeline-auditor-with-cortex-agent-sdk.git\ncd sfguide-pipeline-auditor-with-cortex-agent-sdk\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EInstall dependencies:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Enpm install\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EProject Structure\u003C/h3\u003E\n","\u003Cp\u003EThe project follows a standard Next.js App Router layout:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Epipeline-auditor/\n  app/\n    api/\n      audit/          # POST to start audit, GET to poll progress\n      suggest-fix/    # POST to request fix, chat/ for follow-up, dismiss/\n      databases/      # GET available databases\n      schemas/        # GET schemas in a database\n      chat/           # POST for follow-up chat on the audit thread\n    page.tsx          # Root page -- providers + AuditDashboard\n  components/\n    AuditDashboard.tsx   # Main layout orchestrator\n    AuditHeader.tsx      # Database/schema dropdowns + scope picker\n    AuditSidebar.tsx     # Real-time tool call progress\n    ChatThread.tsx       # Message thread with inline report accordion\n    EmptyState.tsx       # Landing page\n  hooks/\n    useAudit.ts          # Poll-based audit state machine\n    useSuggestFix.ts     # Fix request + multi-turn follow-up\n  lib/\n    agent-client.ts      # CortexAgentClient factory\n    audit-engine.ts      # Composable system prompt + JSON schema\n    audit-store.ts       # In-memory job store for poll-based streaming\n    types.ts             # Shared TypeScript types\n  app.yml                # SAR build/run manifest\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EEnvironment Variables (Local Dev Only)\u003C/h3\u003E\n","\u003Cp\u003EWhen running locally with \u003Ccode\u003Enpm run dev\u003C/code\u003E, create a \u003Ccode\u003E.env.local\u003C/code\u003E file:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESNOWFLAKE_ACCOUNT=your_account_identifier\nSNOWFLAKE_PAT=your_personal_access_token\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWhen deployed to SAR, the app reads an OAuth token from the SPCS runtime automatically -- no env vars needed.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EBuild the Audit Engine\u003C/h2\u003E\n","\u003Cp\u003EDuration: 15\u003C/p\u003E\n","\u003Cp\u003EThe audit engine is the core of the application. It creates a Cortex AI coding agent session, feeds it a composable system prompt, and streams back structured results. Four files work together to make this happen.\u003C/p\u003E\n","\u003Ch3\u003EAgent Client Factory\u003C/h3\u003E\n","\u003Cp\u003E\u003Ccode\u003Elib/agent-client.ts\u003C/code\u003E creates a \u003Ccode\u003ECortexAgentClient\u003C/code\u003E instance that works in both environments:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-typescript\"\u003Eimport { CortexAgentClient, OAuthAuth } from "@snowflake/cortex-agent-sdk";\nimport fs from "fs";\n\nconst TOKEN_PATH = "/snowflake/session/token";\n\nexport function createAgentClient(): CortexAgentClient {\n  const account = process.env.SNOWFLAKE_ACCOUNT || "";\n\n  // In SAR (SPCS), the runtime mounts an OAuth token at a known path\n  if (fs.existsSync(TOKEN_PATH)) {\n    const token = fs.readFileSync(TOKEN_PATH, "utf-8").trim();\n    return new CortexAgentClient({\n      account,\n      auth: new OAuthAuth(token),\n    });\n  }\n\n  // Fall back to PAT for local development\n  const pat = process.env.SNOWFLAKE_PAT || "";\n  return new CortexAgentClient({ account, auth: pat });\n}\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe dual-auth pattern is important: SAR containers get a short-lived OAuth token injected at \u003Ccode\u003E/snowflake/session/token\u003C/code\u003E by the SPCS runtime. For local development, you use a Personal Access Token (PAT) instead.\u003C/p\u003E\n","\u003Ch3\u003EComposable System Prompt\u003C/h3\u003E\n","\u003Cp\u003E\u003Ccode\u003Elib/audit-engine.ts\u003C/code\u003E is where the real intelligence lives. Rather than one monolithic prompt, the audit instructions are split into composable sections keyed by scope:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-typescript\"\u003Eexport const PROMPT_SECTIONS: Record<string, string> = {\n  tables_freshness: `\nTABLES & FRESHNESS:\n   - Tables and row counts:\n     SELECT table_schema, table_name, table_type, row_count\n     FROM <DATABASE>.INFORMATION_SCHEMA.TABLES\n     WHERE table_schema NOT IN ('INFORMATION_SCHEMA')\n     ORDER BY table_schema, table_name;\n   - Check freshness -- identify stale tables:\n     SELECT table_schema, table_name, table_type, row_count, last_altered,\n            DATEDIFF('hour', last_altered, CURRENT_TIMESTAMP()) AS hours_since_update,\n            CASE\n              WHEN DATEDIFF('hour', last_altered, CURRENT_TIMESTAMP()) > 168 THEN 'critical'\n              WHEN DATEDIFF('hour', last_altered, CURRENT_TIMESTAMP()) > 24 THEN 'warning'\n              ELSE 'fresh'\n            END AS freshness_status\n     FROM <DATABASE>.INFORMATION_SCHEMA.TABLES\n     WHERE table_schema NOT IN ('INFORMATION_SCHEMA') AND table_type = 'BASE TABLE'\n     ORDER BY hours_since_update DESC;\n   - Check for row count anomalies (empty tables that shouldn't be)`,\n\n  dynamic_tables: `\nDYNAMIC TABLES:\n   - Discover: SHOW DYNAMIC TABLES IN DATABASE <DATABASE>;\n   - Check scheduling_state (should be ACTIVE)\n   - Check refresh history for failures ...`,\n\n  tasks: `\nTASKS:\n   - Discover: SHOW TASKS IN DATABASE <DATABASE>;\n   - Check execution history for failures via TASK_HISTORY table function\n   - Flag suspended tasks and tasks with recent failures`,\n\n  streams: `\nSTREAMS:\n   - Discover: SHOW STREAMS IN DATABASE <DATABASE>;\n   - Check each stream's stale status (stale = true is CRITICAL)\n   - Check stale_after timestamp -- if approaching, flag as warning`,\n\n  pipes: `\nPIPES:\n   - Discover: SHOW PIPES IN DATABASE <DATABASE>;\n   - Check pipe status (RUNNING, STOPPED_CLONING, PAUSED, STALLED)\n   - Check recent copy history for errors ...`,\n\n  procedures: `\nSTORED PROCEDURES:\n   - Discover: SHOW PROCEDURES IN DATABASE <DATABASE>;\n   - Identify ETL-related procedures\n   - Flag procedures that do data movement but have no associated task`,\n};\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe \u003Ccode\u003EbuildSystemPrompt\u003C/code\u003E function assembles only the sections the user selected:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-typescript\"\u003Eexport function buildSystemPrompt(\n  database: string,\n  scope: string[],\n  schema: string,\n): string {\n  const allScopes = Object.keys(PROMPT_SECTIONS);\n  const activeScopes = scope.length > 0 ? scope : allScopes;\n\n  // Use the single-schema preamble when a schema is specified\n  let prompt = schema\n    ? PROMPT_PREAMBLE_SINGLE_SCHEMA\n    : PROMPT_PREAMBLE_ALL_SCHEMAS;\n\n  for (const key of allScopes) {\n    if (activeScopes.includes(key)) {\n      prompt += PROMPT_SECTIONS[key];\n    }\n  }\n  prompt += PROMPT_FOOTER;\n\n  // Replace <DATABASE> and <SCHEMA> placeholders throughout\n  prompt = prompt.replace(/<DATABASE>/g, database);\n  if (schema) {\n    prompt = prompt.replace(/<SCHEMA>/g, schema);\n    // Rewrite SHOW ... IN DATABASE to SHOW ... IN SCHEMA\n    // Rewrite INFORMATION_SCHEMA filters to target the single schema\n  }\n\n  return prompt;\n}\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis composability matters: auditing only tasks and streams is much faster than auditing everything. The user picks the scope in the UI, and the prompt adapts.\u003C/p\u003E\n","\u003Cp\u003EThe engine also defines \u003Ccode\u003EAUDIT_REPORT_SCHEMA\u003C/code\u003E -- a JSON schema that tells the agent exactly what structure to return. The schema includes a \u003Ccode\u003Epipeline_inventory\u003C/code\u003E (what was discovered), a \u003Ccode\u003Efindings\u003C/code\u003E array (each with \u003Ccode\u003Ecategory\u003C/code\u003E, \u003Ccode\u003Eseverity\u003C/code\u003E, \u003Ccode\u003Eobject\u003C/code\u003E, and \u003Ccode\u003Emessage\u003C/code\u003E), and a \u003Ccode\u003Esummary\u003C/code\u003E with counts and an \u003Ccode\u003Eoverall_health\u003C/code\u003E rating.\u003C/p\u003E\n","\u003Ch3\u003EIn-Memory Job Store\u003C/h3\u003E\n","\u003Cp\u003EBecause the audit can take 30-60 seconds, the app uses a poll-based architecture instead of holding an HTTP connection open. \u003Ccode\u003Elib/audit-store.ts\u003C/code\u003E is the glue:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-typescript\"\u003Eexport interface AuditJob {\n  events: Record<string, unknown>[];\n  done: boolean;\n  error?: string;\n}\n\nexport const jobStore = new Map<string, AuditJob>();\n\nexport function pushJobEvent(\n  jobId: string,\n  event: Record<string, unknown>,\n): void {\n  const job = jobStore.get(jobId);\n  if (job) job.events.push(event);\n}\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EEvents accumulate in memory. The frontend polls for new events every 2 seconds. A cleanup interval removes completed jobs after 30 minutes.\u003C/p\u003E\n","\u003Ch3\u003EThe Audit API Route\u003C/h3\u003E\n","\u003Cp\u003E\u003Ccode\u003Eapp/api/audit/route.ts\u003C/code\u003E ties everything together. The POST handler:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EAccepts \u003Ccode\u003Edatabase\u003C/code\u003E, \u003Ccode\u003Escope\u003C/code\u003E, and \u003Ccode\u003Eschema\u003C/code\u003E from the request body\u003C/li\u003E\u003Cli\u003ECreates a job ID and registers it in the job store\u003C/li\u003E\u003Cli\u003EReturns the job ID immediately (non-blocking)\u003C/li\u003E\u003Cli\u003EFires \u003Ccode\u003ErunAuditJob\u003C/code\u003E in the background\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EThe background function:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-typescript\"\u003Easync function runAuditJob(jobId, database, scope, schema) {\n  const client = createAgentClient();\n  const systemPrompt = buildSystemPrompt(database, scope, schema);\n  const userPrompt = buildAuditUserPrompt(database, scope, schema);\n\n  const messages = [\n    {\n      role: "user",\n      content: [{ type: "text", text: systemPrompt + "\\n\\n" + userPrompt }],\n    },\n  ];\n\n  const stream = client.codingAgent.stream({\n    permissionPolicy: "always_allow",\n    messages,\n  });\n\n  let accumulatedText = "";\n\n  // Use the SDK's callback API to process events\n  stream.on("text", (delta, accumulated) => {\n    accumulatedText = accumulated;\n    // Try to parse as JSON report on each chunk\n    try {\n      const parsed = JSON.parse(accumulated);\n      if (parsed.findings) {\n        pushJobEvent(jobId, { type: "report", report: parsed });\n      }\n    } catch { /* not complete JSON yet */ }\n  });\n\n  stream.on("toolUse", (ev) => {\n    // Push progress events so the sidebar shows tool execution\n    pushJobEvent(jobId, { type: "tool_progress", toolName: ev.name });\n  });\n\n  stream.on("thinking", (delta) => {\n    pushJobEvent(jobId, { type: "thinking", text: delta });\n  });\n\n  await stream.done();\n  // Job is complete — mark done in the job store\n}\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe stream uses the SDK's callback API to process three event types:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003Etext\u003C/strong\u003E -- accumulated until it forms valid JSON matching the report schema\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EtoolUse\u003C/strong\u003E -- emitted as progress events so the sidebar can show what the agent is doing\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003Ethinking\u003C/strong\u003E -- the agent's chain-of-thought reasoning\u003C/li\u003E\u003C/ul\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EKey concepts:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ECoding agent sessions\u003C/strong\u003E give the AI access to \u003Ccode\u003Esql_execute\u003C/code\u003E so it can query your Snowflake account directly\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EComposable prompts\u003C/strong\u003E let you scope audits to specific object types for faster, targeted checks\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EPoll-based streaming\u003C/strong\u003E avoids long-lived HTTP connections while still giving the user real-time progress\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EStructured JSON output\u003C/strong\u003E means the report is machine-parseable, not just freeform text\u003C/li\u003E\u003C/ul\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EBuild the Dashboard UI\u003C/h2\u003E\n","\u003Cp\u003EDuration: 10\u003C/p\u003E\n","\u003Cp\u003EThe frontend is a single-page React application built with Material UI. The component hierarchy is straightforward.\u003C/p\u003E\n","\u003Ch3\u003EPage Root and Providers\u003C/h3\u003E\n","\u003Cp\u003E\u003Ccode\u003Eapp/page.tsx\u003C/code\u003E wraps the dashboard in the required providers:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-typescript\"\u003E"use client";\n\nimport { AppThemeProvider } from "@/components/ThemeContext";\nimport { AuditDashboard } from "@/components/AuditDashboard";\n\nexport default function Page() {\n  return (\n    <AppThemeProvider>\n      <AuditDashboard />\n    </AppThemeProvider>\n  );\n}\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Ccode\u003EAppThemeProvider\u003C/code\u003E wraps the app in the MUI dark/light theme with localStorage persistence.\u003C/p\u003E\n","\u003Ch3\u003EAuditDashboard -- The Orchestrator\u003C/h3\u003E\n","\u003Cp\u003E\u003Ccode\u003EAuditDashboard.tsx\u003C/code\u003E connects the hooks to the layout:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-typescript\"\u003Eexport function AuditDashboard() {\n  const {\n    isAuditing, isLoading, report, messages,\n    toolProgress, error, stats,\n    startAudit, cancelAudit, resetView,\n  } = useAudit();\n\n  const { fixState, requestFix, sendFollowUp, dismissFix } = useSuggestFix();\n\n  return (\n    <Box sx={{ height: "100vh", display: "flex", flexDirection: "column" }}>\n      <AuditHeader\n        onStartAudit={(db, scope, schema) => startAudit(db, scope, schema)}\n        onCancel={cancelAudit}\n        onReset={resetView}\n        isLoading={isLoading}\n        isAuditing={isAuditing}\n      />\n\n      <Box sx={{ flex: 1, overflow: "hidden", display: "flex" }}>\n        {!hasAuditData ? (\n          <EmptyState />\n        ) : (\n          <>\n            {/* Chat thread with inline report accordion */}\n            <Box sx={{ flex: 1, overflow: "auto" }}>\n              <ChatThread\n                messages={messages}\n                report={report}\n                toolProgress={toolProgress}\n                isLoading={isLoading}\n                onSuggestFix={handleSuggestFix}\n                fixState={fixState}\n              />\n            </Box>\n\n            {/* Real-time stats sidebar */}\n            <AuditSidebar\n              toolProgress={toolProgress}\n              report={report}\n              stats={stats}\n              isLoading={isLoading}\n            />\n          </>\n        )}\n      </Box>\n    </Box>\n  );\n}\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe layout has three zones:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EAuditHeader\u003C/strong\u003E -- database and schema selectors (fetched via \u003Ccode\u003E/api/databases\u003C/code\u003E and \u003Ccode\u003E/api/schemas\u003C/code\u003E), scope checkboxes, and the "Run Audit" button\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EChatThread\u003C/strong\u003E -- the main content area showing the conversation, tool call activity, and the audit report as an expandable accordion with severity badges\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAuditSidebar\u003C/strong\u003E -- a live feed of tool calls with SQL previews and elapsed time\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EThe useAudit Hook -- Poll-Based State Machine\u003C/h3\u003E\n","\u003Cp\u003EThe \u003Ccode\u003EuseAudit\u003C/code\u003E hook drives the audit lifecycle. The core polling function is reused by both audit and fix flows:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-typescript\"\u003Econst POLL_INTERVAL_MS = 2000;\n\nexport async function pollJob(\n  jobId: string,\n  onEvent: (event: StreamEvent) => void,\n  signal: AbortSignal,\n): Promise<void> {\n  let cursor = 0;\n  while (!signal.aborted) {\n    const resp = await fetch(\n      `/api/audit/progress?jobId=${encodeURIComponent(jobId)}&after=${cursor}`,\n      { credentials: "include", signal },\n    );\n    const data = await resp.json();\n    for (const event of data.events) {\n      onEvent(event);\n    }\n    cursor = data.cursor;\n    if (data.done) return;\n    await new Promise((resolve) => setTimeout(resolve, POLL_INTERVAL_MS));\n  }\n}\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe \u003Ccode\u003EstartAudit\u003C/code\u003E callback POSTs to \u003Ccode\u003E/api/audit\u003C/code\u003E, gets back a \u003Ccode\u003EjobId\u003C/code\u003E, then polls for events. Each event type maps to a state update:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EEvent type\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EState change\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Etool_progress\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAppends to sidebar + message tool call list\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Ethinking\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAppends to the assistant message's thinking steps\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Ereport\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESets the parsed audit report\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Eresult\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EClears loading state, sets duration/tool call stats\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Eerror\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESets error message, clears loading\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003EThe poll includes retry logic with exponential backoff for transient 5xx errors -- important since SAR proxies can occasionally hiccup.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EKey concepts:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ECursor-based polling\u003C/strong\u003E ensures no events are missed even if a poll is delayed\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAbortController\u003C/strong\u003E lets the user cancel a running audit cleanly\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EuseEffect + fetch\u003C/strong\u003E handles the database/schema dropdowns with loading states\u003C/li\u003E\u003C/ul\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EAdd Suggest Fix\u003C/h2\u003E\n","\u003Cp\u003EDuration: 10\u003C/p\u003E\n","\u003Cp\u003EAfter the audit produces findings, each one gets a "Suggest Fix" button. This opens a separate coding agent session focused on remediation.\u003C/p\u003E\n","\u003Ch3\u003EThe Suggest Fix API\u003C/h3\u003E\n","\u003Cp\u003E\u003Ccode\u003Eapp/api/suggest-fix/route.ts\u003C/code\u003E creates a new agent session with a remediation-focused prompt:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-typescript\"\u003Easync function runSuggestFixJob(jobId, finding, database) {\n  const systemPrompt = `You are a Snowflake pipeline remediation expert. \nWhen given a finding, respond with a clear, actionable fix. \nUse SQL code blocks for any commands. \nIf the user asks you to run SQL, you may use sql_execute.\nTarget database: ${database}.`;\n\n  const userPrompt = `Suggest a concrete fix for this pipeline audit finding:\n\nFinding:\n- Severity: ${finding.severity}\n- Category: ${finding.category}\n- Object: ${finding.object}\n- Message: ${finding.message}\n${finding.details ? `- Details: ${JSON.stringify(finding.details)}` : ""}\n\nRules:\n- Do NOT run any tools or queries. Respond with your answer directly.\n- Provide exact SQL commands in fenced code blocks.\n- Explain briefly why the fix works.\n- Keep the response under 500 words.`;\n\n  const messages = [{\n    role: "user",\n    content: [{ type: "text", text: systemPrompt + "\\n\\n" + userPrompt }],\n  }];\n\n  const client = createAgentClient();\n  const stream = client.codingAgent.stream({\n    permissionPolicy: "always_allow",\n    messages,\n  });\n\n  // Use callbacks to stream text to the job store\n  stream.on("text", (delta) => {\n    pushJobEvent(jobId, { type: "text", text: delta });\n  });\n\n  await stream.done();\n\n  // Save the conversation thread for follow-up chat\n  setFixSession([...messages, {\n    role: "assistant",\n    content: [{ type: "text", text: stream.currentText }],\n  }]);\n}\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe fix session is stored per-job in the job store so follow-up chat can continue the conversation without leaking state between requests.\u003C/p\u003E\n","\u003Ch3\u003EMulti-Turn Follow-Up Chat\u003C/h3\u003E\n","\u003Cp\u003EThe user can ask follow-up questions ("Can you also add a monitoring task?", "What about the downstream views?"). \u003Ccode\u003Eapp/api/suggest-fix/chat/route.ts\u003C/code\u003E appends the new message to the stored thread and streams back the response:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-typescript\"\u003Easync function runFixChatJob(jobId: string, message: string) {\n  const fixThread = getFixSession();\n  const client = createAgentClient();\n\n  // Append user message to the thread\n  fixThread.push({\n    role: "user",\n    content: [{ type: "text", text: message }],\n  });\n\n  // Stream with full conversation history for context\n  const stream = client.codingAgent.stream({\n    permissionPolicy: "always_allow",\n    messages: fixThread,\n  });\n\n  // ... accumulate text, push events, update thread\n}\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis is a true multi-turn conversation -- the agent sees every prior message, so it can refine its suggestions based on the user's feedback.\u003C/p\u003E\n","\u003Ch3\u003EThe useSuggestFix Hook\u003C/h3\u003E\n","\u003Cp\u003EOn the frontend, \u003Ccode\u003Ehooks/useSuggestFix.ts\u003C/code\u003E manages the fix lifecycle:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-typescript\"\u003Eexport function useSuggestFix() {\n  const [state, setState] = useState<SuggestFixState>(IDLE);\n\n  const requestFix = useCallback(async (index, finding, database) => {\n    // POST to /api/suggest-fix, poll for text events\n    // Update state with streaming assistant messages\n  }, []);\n\n  const sendFollowUp = useCallback(async (message) => {\n    // POST to /api/suggest-fix/chat, poll for response\n    // Append user message + streaming assistant response\n  }, []);\n\n  const dismissFix = useCallback(() => {\n    // Abort, reset state, POST to /api/suggest-fix/dismiss\n  }, []);\n\n  return { fixState: state, requestFix, sendFollowUp, dismissFix };\n}\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe fix panel renders inline in the ChatThread component. Each finding in the report has a "Suggest Fix" button. When clicked, it opens a chat-like panel below the finding with the AI's suggestion and a text input for follow-ups.\u003C/p\u003E\n","\u003Ch3\u003ECopy and Run\u003C/h3\u003E\n","\u003Cp\u003ESQL code blocks in fix suggestions include a \u003Cstrong\u003ECopy\u003C/strong\u003E button so users can paste them into a Snowflake worksheet for execution.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EKey concepts:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ESeparate agent sessions\u003C/strong\u003E for audit vs. fix keep concerns isolated and prompts focused\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EMulti-turn chat\u003C/strong\u003E with full message history gives the agent context for iterative refinement\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EPer-job thread state\u003C/strong\u003E (stored in the job store) keeps conversations isolated between requests\u003C/li\u003E\u003Cli\u003EThe coding agent uses \u003Ccode\u003EpermissionPolicy: "always_allow"\u003C/code\u003E because it needs to run SQL queries autonomously. Deploy the app with a \u003Cstrong\u003Eread-only role\u003C/strong\u003E to limit what the agent can do.\u003C/li\u003E\u003C/ul\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EDeploy and Run\u003C/h2\u003E\n","\u003Cp\u003EDuration: 10\u003C/p\u003E\n","\u003Ch3\u003EGenerate the Deployment Manifest\u003C/h3\u003E\n","\u003Cp\u003EThe project includes an \u003Ccode\u003Eapp.yml\u003C/code\u003E that defines the build and run phases:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-yaml\"\u003Einstall:\n  commands:\n    - ["npm", "ci", "--include=dev"]\n\nbuild:\n  commands:\n    - ["npm", "run", "build"]\n    - ["cp", "-r", ".next/static", ".next/standalone/.next/static"]\n    - ["cp", "-r", "public", ".next/standalone/public"]\n    - ["rm", "-rf", "node_modules"]\n\nrun:\n  command: ["node", ".next/standalone/server.js"]\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe build uses Next.js standalone output mode, which bundles only the required Node.js modules into \u003Ccode\u003E.next/standalone/\u003C/code\u003E for a smaller container image.\u003C/p\u003E\n","\u003Ch3\u003ESet Up and Deploy\u003C/h3\u003E\n","\u003Cp\u003EFrom the project directory, run:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Esnow app setup\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis generates a \u003Ccode\u003Esnowflake.yml\u003C/code\u003E with your deployment target (account, database, warehouse). Review it, then deploy:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Esnow app deploy\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe deploy process:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EBuilds the Next.js app inside an SPCS container\u003C/li\u003E\u003Cli\u003EPushes the container image to the Snowflake image registry\u003C/li\u003E\u003Cli\u003ECreates an APPLICATION SERVICE object\u003C/li\u003E\u003Cli\u003EMakes the app available at a URL\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003EOpen the App\u003C/h3\u003E\n","\u003Cp\u003EAfter deployment completes, \u003Ccode\u003Esnow app deploy\u003C/code\u003E prints the app URL. Open it in your browser.\u003C/p\u003E\n","\u003Cp\u003EYou can also find the URL with:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Esnow app status\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ERun Your First Audit\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003ESelect a database\u003C/strong\u003E from the dropdown in the header. The app queries \u003Ccode\u003ESHOW DATABASES\u003C/code\u003E to populate the list.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003E(Optional) Select a schema\u003C/strong\u003E to narrow the audit to a single schema instead of the entire database.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003EChoose your audit scope\u003C/strong\u003E using the checkboxes. You can audit any combination of:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ETables and freshness\u003C/li\u003E\u003Cli\u003EDynamic tables\u003C/li\u003E\u003Cli\u003ETasks\u003C/li\u003E\u003Cli\u003EViews\u003C/li\u003E\u003Cli\u003EStreams\u003C/li\u003E\u003Cli\u003EPipes\u003C/li\u003E\u003Cli\u003EStored procedures\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003EClick "Run Audit"\u003C/strong\u003E. The sidebar lights up with real-time progress as the agent executes discovery queries and health checks.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003EReview the report\u003C/strong\u003E. When the audit completes, the report appears as an expandable accordion in the chat thread. Findings are grouped by severity with color-coded badges:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ERed for critical (stale data, failing refreshes, stale streams)\u003C/li\u003E\u003Cli\u003EOrange for warning (approaching thresholds, suspended tasks)\u003C/li\u003E\u003Cli\u003EBlue for info (best practice suggestions, architecture notes)\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003ESuggest fixes\u003C/strong\u003E. Click "Suggest Fix" on any finding to open a remediation chat. The agent provides SQL commands you can review and copy into a Snowflake worksheet.\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003ELocal Development\u003C/h3\u003E\n","\u003Cp\u003EFor iterating on the app locally:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Enpm run dev\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis starts the Next.js dev server at \u003Ccode\u003Ehttp://localhost:3000\u003C/code\u003E. Make sure your \u003Ccode\u003E.env.local\u003C/code\u003E has \u003Ccode\u003ESNOWFLAKE_ACCOUNT\u003C/code\u003E and \u003Ccode\u003ESNOWFLAKE_PAT\u003C/code\u003E set.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion And Resources\u003C/h2\u003E\n","\u003Cp\u003EDuration: 2\u003C/p\u003E\n","\u003Cp\u003EYou have built a full-stack AI-powered pipeline auditor that runs on Snowflake App Runtime. The app uses a Cortex AI coding agent to autonomously discover pipeline objects, run health checks, and produce structured reports -- with an interactive fix suggestion workflow for remediation.\u003C/p\u003E\n","\u003Ch3\u003EWhat You Learned\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to scaffold and deploy a Next.js app on Snowflake App Runtime with \u003Ccode\u003Esnow app\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EHow to use \u003Ccode\u003E@snowflake/cortex-agent-sdk\u003C/code\u003E to run coding agent sessions that analyze your pipelines\u003C/li\u003E\u003Cli\u003EHow to build composable system prompts that produce structured JSON reports\u003C/li\u003E\u003Cli\u003EHow to create a poll-based architecture for real-time agent progress\u003C/li\u003E\u003Cli\u003EHow to implement AI-powered fix suggestions with multi-turn chat\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EExtending It\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ESchedule audits\u003C/strong\u003E with Snowflake Tasks for daily or weekly health reports\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESend notifications\u003C/strong\u003E via \u003Ccode\u003ESYSTEM$SEND_EMAIL\u003C/code\u003E when critical issues are found\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EStore audit history\u003C/strong\u003E in a Snowflake table for trend analysis over time\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAdd more scopes\u003C/strong\u003E like Snowpipe latency, warehouse queue depth, or credit anomalies\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ERelated Resources\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://www.npmjs.com/package/@snowflake/cortex-agent-sdk\"\u003E@snowflake/cortex-agent-sdk on npm\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowflake-app-runtime\"\u003ESnowflake App Runtime 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://github.com/sfc-gh-JCHAEARLEY/sfguide-pipeline-auditor-with-cortex-agent-sdk\"\u003EPipeline Auditor Companion Repository\u003C/a\u003E\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 PIPELINE_AUDITOR_DB;\n-- Also drop the Application Service if deployed:\nDROP APPLICATION SERVICE IF EXISTS pipeline_auditor;\n\u003C/code\u003E\u003C/pre\u003E"],"description":"","title":"Building a Pipeline Auditor with the Cortex Agent SDK","isDeveloperGuidesPage":false,":type":"snowflake-site/components/contentfragment",":items":{},":itemsOrder":[],"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],"elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\u003C!-- ------------------------ --\u003E\n## Overview\nDuration: 5\n\nData pipelines break silently. A task suspends, a dynamic table stops refreshing, a stream goes stale -- and nobody notices until a dashboard goes blank or a downstream consumer raises an alarm. Manual health checks don't scale, and static monitors only catch what you thought to look for.\n\nIn this guide you will build a **Pipeline Auditor** -- a full-stack application that uses a Cortex AI coding agent to automatically discover every object in a Snowflake database (or a single schema), run health checks against each one, and produce a structured JSON report with severity-rated findings. When something looks wrong, the agent can suggest a fix and even execute it for you.\n\nThe app runs on **Snowflake App Runtime** (SAR) as a Next.js application and uses the **@snowflake/cortex-agent-sdk** TypeScript SDK to communicate with the coding agent.\n\n### What You'll Need\n- A Snowflake account with Cortex AI enabled (Enterprise edition or higher)\n- Node.js 22+ and npm\n- Snowflake CLI (`snow`) installed and configured\n- Basic TypeScript and React knowledge\n\n### What You'll Learn\n- How to scaffold and deploy a Next.js app on Snowflake App Runtime\n- How to use `@snowflake/cortex-agent-sdk` to run coding agent sessions\n- How to build composable system prompts that produce structured JSON output\n- How to create a poll-based streaming architecture for real-time progress\n- How to implement AI-powered fix suggestions with multi-turn chat\n\n### What You'll Build\n- A full-stack pipeline auditor with:\n  - Database and schema discovery\n  - Configurable audit scope (tables, dynamic tables, tasks, views, streams, pipes, procedures)\n  - Real-time audit progress tracking via a sidebar\n  - Structured health reports with severity ratings (critical / warning / info)\n  - AI-powered \"Suggest Fix\" with follow-up chat and SQL execution\n\n\u003C!-- ------------------------ --\u003E\n## Setup\nDuration: 10\n\n### Create Snowflake Objects\n\nRun the following SQL in Snowsight to create the database the app will audit. You can also point the auditor at any existing database -- this one is just a convenient starting point.\n\n```sql\nUSE ROLE SYSADMIN;\n\nCREATE DATABASE IF NOT EXISTS PIPELINE_AUDITOR_DB;\nCREATE SCHEMA IF NOT EXISTS PIPELINE_AUDITOR_DB.AUDITOR;\n```\n\n### Scaffold the App\n\nClone the companion repository and navigate to the project:\n\n```bash\ngit clone https://github.com/sfc-gh-JCHAEARLEY/sfguide-pipeline-auditor-with-cortex-agent-sdk.git\ncd sfguide-pipeline-auditor-with-cortex-agent-sdk\n```\n\nInstall dependencies:\n\n```bash\nnpm install\n```\n\n### Project Structure\n\nThe project follows a standard Next.js App Router layout:\n\n```\npipeline-auditor/\n  app/\n    api/\n      audit/          # POST to start audit, GET to poll progress\n      suggest-fix/    # POST to request fix, chat/ for follow-up, dismiss/\n      databases/      # GET available databases\n      schemas/        # GET schemas in a database\n      chat/           # POST for follow-up chat on the audit thread\n    page.tsx          # Root page -- providers + AuditDashboard\n  components/\n    AuditDashboard.tsx   # Main layout orchestrator\n    AuditHeader.tsx      # Database/schema dropdowns + scope picker\n    AuditSidebar.tsx     # Real-time tool call progress\n    ChatThread.tsx       # Message thread with inline report accordion\n    EmptyState.tsx       # Landing page\n  hooks/\n    useAudit.ts          # Poll-based audit state machine\n    useSuggestFix.ts     # Fix request + multi-turn follow-up\n  lib/\n    agent-client.ts      # CortexAgentClient factory\n    audit-engine.ts      # Composable system prompt + JSON schema\n    audit-store.ts       # In-memory job store for poll-based streaming\n    types.ts             # Shared TypeScript types\n  app.yml                # SAR build/run manifest\n```\n\n### Environment Variables (Local Dev Only)\n\nWhen running locally with `npm run dev`, create a `.env.local` file:\n\n```\nSNOWFLAKE_ACCOUNT=your_account_identifier\nSNOWFLAKE_PAT=your_personal_access_token\n```\n\nWhen deployed to SAR, the app reads an OAuth token from the SPCS runtime automatically -- no env vars needed.\n\n\u003C!-- ------------------------ --\u003E\n## Build the Audit Engine\nDuration: 15\n\nThe audit engine is the core of the application. It creates a Cortex AI coding agent session, feeds it a composable system prompt, and streams back structured results. Four files work together to make this happen.\n\n### Agent Client Factory\n\n`lib/agent-client.ts` creates a `CortexAgentClient` instance that works in both environments:\n\n```typescript\nimport { CortexAgentClient, OAuthAuth } from \"@snowflake/cortex-agent-sdk\";\nimport fs from \"fs\";\n\nconst TOKEN_PATH = \"/snowflake/session/token\";\n\nexport function createAgentClient(): CortexAgentClient {\n  const account = process.env.SNOWFLAKE_ACCOUNT || \"\";\n\n  // In SAR (SPCS), the runtime mounts an OAuth token at a known path\n  if (fs.existsSync(TOKEN_PATH)) {\n    const token = fs.readFileSync(TOKEN_PATH, \"utf-8\").trim();\n    return new CortexAgentClient({\n      account,\n      auth: new OAuthAuth(token),\n    });\n  }\n\n  // Fall back to PAT for local development\n  const pat = process.env.SNOWFLAKE_PAT || \"\";\n  return new CortexAgentClient({ account, auth: pat });\n}\n```\n\nThe dual-auth pattern is important: SAR containers get a short-lived OAuth token injected at `/snowflake/session/token` by the SPCS runtime. For local development, you use a Personal Access Token (PAT) instead.\n\n### Composable System Prompt\n\n`lib/audit-engine.ts` is where the real intelligence lives. Rather than one monolithic prompt, the audit instructions are split into composable sections keyed by scope:\n\n```typescript\nexport const PROMPT_SECTIONS: Record\u003Cstring, string\u003E = {\n  tables_freshness: `\nTABLES & FRESHNESS:\n   - Tables and row counts:\n     SELECT table_schema, table_name, table_type, row_count\n     FROM \u003CDATABASE\u003E.INFORMATION_SCHEMA.TABLES\n     WHERE table_schema NOT IN ('INFORMATION_SCHEMA')\n     ORDER BY table_schema, table_name;\n   - Check freshness -- identify stale tables:\n     SELECT table_schema, table_name, table_type, row_count, last_altered,\n            DATEDIFF('hour', last_altered, CURRENT_TIMESTAMP()) AS hours_since_update,\n            CASE\n              WHEN DATEDIFF('hour', last_altered, CURRENT_TIMESTAMP()) \u003E 168 THEN 'critical'\n              WHEN DATEDIFF('hour', last_altered, CURRENT_TIMESTAMP()) \u003E 24 THEN 'warning'\n              ELSE 'fresh'\n            END AS freshness_status\n     FROM \u003CDATABASE\u003E.INFORMATION_SCHEMA.TABLES\n     WHERE table_schema NOT IN ('INFORMATION_SCHEMA') AND table_type = 'BASE TABLE'\n     ORDER BY hours_since_update DESC;\n   - Check for row count anomalies (empty tables that shouldn't be)`,\n\n  dynamic_tables: `\nDYNAMIC TABLES:\n   - Discover: SHOW DYNAMIC TABLES IN DATABASE \u003CDATABASE\u003E;\n   - Check scheduling_state (should be ACTIVE)\n   - Check refresh history for failures ...`,\n\n  tasks: `\nTASKS:\n   - Discover: SHOW TASKS IN DATABASE \u003CDATABASE\u003E;\n   - Check execution history for failures via TASK_HISTORY table function\n   - Flag suspended tasks and tasks with recent failures`,\n\n  streams: `\nSTREAMS:\n   - Discover: SHOW STREAMS IN DATABASE \u003CDATABASE\u003E;\n   - Check each stream's stale status (stale = true is CRITICAL)\n   - Check stale_after timestamp -- if approaching, flag as warning`,\n\n  pipes: `\nPIPES:\n   - Discover: SHOW PIPES IN DATABASE \u003CDATABASE\u003E;\n   - Check pipe status (RUNNING, STOPPED_CLONING, PAUSED, STALLED)\n   - Check recent copy history for errors ...`,\n\n  procedures: `\nSTORED PROCEDURES:\n   - Discover: SHOW PROCEDURES IN DATABASE \u003CDATABASE\u003E;\n   - Identify ETL-related procedures\n   - Flag procedures that do data movement but have no associated task`,\n};\n```\n\nThe `buildSystemPrompt` function assembles only the sections the user selected:\n\n```typescript\nexport function buildSystemPrompt(\n  database: string,\n  scope: string[],\n  schema: string,\n): string {\n  const allScopes = Object.keys(PROMPT_SECTIONS);\n  const activeScopes = scope.length \u003E 0 ? scope : allScopes;\n\n  // Use the single-schema preamble when a schema is specified\n  let prompt = schema\n    ? PROMPT_PREAMBLE_SINGLE_SCHEMA\n    : PROMPT_PREAMBLE_ALL_SCHEMAS;\n\n  for (const key of allScopes) {\n    if (activeScopes.includes(key)) {\n      prompt += PROMPT_SECTIONS[key];\n    }\n  }\n  prompt += PROMPT_FOOTER;\n\n  // Replace \u003CDATABASE\u003E and \u003CSCHEMA\u003E placeholders throughout\n  prompt = prompt.replace(/\u003CDATABASE\u003E/g, database);\n  if (schema) {\n    prompt = prompt.replace(/\u003CSCHEMA\u003E/g, schema);\n    // Rewrite SHOW ... IN DATABASE to SHOW ... IN SCHEMA\n    // Rewrite INFORMATION_SCHEMA filters to target the single schema\n  }\n\n  return prompt;\n}\n```\n\nThis composability matters: auditing only tasks and streams is much faster than auditing everything. The user picks the scope in the UI, and the prompt adapts.\n\nThe engine also defines `AUDIT_REPORT_SCHEMA` -- a JSON schema that tells the agent exactly what structure to return. The schema includes a `pipeline_inventory` (what was discovered), a `findings` array (each with `category`, `severity`, `object`, and `message`), and a `summary` with counts and an `overall_health` rating.\n\n### In-Memory Job Store\n\nBecause the audit can take 30-60 seconds, the app uses a poll-based architecture instead of holding an HTTP connection open. `lib/audit-store.ts` is the glue:\n\n```typescript\nexport interface AuditJob {\n  events: Record\u003Cstring, unknown\u003E[];\n  done: boolean;\n  error?: string;\n}\n\nexport const jobStore = new Map\u003Cstring, AuditJob\u003E();\n\nexport function pushJobEvent(\n  jobId: string,\n  event: Record\u003Cstring, unknown\u003E,\n): void {\n  const job = jobStore.get(jobId);\n  if (job) job.events.push(event);\n}\n```\n\nEvents accumulate in memory. The frontend polls for new events every 2 seconds. A cleanup interval removes completed jobs after 30 minutes.\n\n### The Audit API Route\n\n`app/api/audit/route.ts` ties everything together. The POST handler:\n\n1. Accepts `database`, `scope`, and `schema` from the request body\n2. Creates a job ID and registers it in the job store\n3. Returns the job ID immediately (non-blocking)\n4. Fires `runAuditJob` in the background\n\nThe background function:\n\n```typescript\nasync function runAuditJob(jobId, database, scope, schema) {\n  const client = createAgentClient();\n  const systemPrompt = buildSystemPrompt(database, scope, schema);\n  const userPrompt = buildAuditUserPrompt(database, scope, schema);\n\n  const messages = [\n    {\n      role: \"user\",\n      content: [{ type: \"text\", text: systemPrompt + \"\\n\\n\" + userPrompt }],\n    },\n  ];\n\n  const stream = client.codingAgent.stream({\n    permissionPolicy: \"always_allow\",\n    messages,\n  });\n\n  let accumulatedText = \"\";\n\n  // Use the SDK's callback API to process events\n  stream.on(\"text\", (delta, accumulated) =\u003E {\n    accumulatedText = accumulated;\n    // Try to parse as JSON report on each chunk\n    try {\n      const parsed = JSON.parse(accumulated);\n      if (parsed.findings) {\n        pushJobEvent(jobId, { type: \"report\", report: parsed });\n      }\n    } catch { /* not complete JSON yet */ }\n  });\n\n  stream.on(\"toolUse\", (ev) =\u003E {\n    // Push progress events so the sidebar shows tool execution\n    pushJobEvent(jobId, { type: \"tool_progress\", toolName: ev.name });\n  });\n\n  stream.on(\"thinking\", (delta) =\u003E {\n    pushJobEvent(jobId, { type: \"thinking\", text: delta });\n  });\n\n  await stream.done();\n  // Job is complete — mark done in the job store\n}\n```\n\nThe stream uses the SDK's callback API to process three event types:\n\n- **text** -- accumulated until it forms valid JSON matching the report schema\n- **toolUse** -- emitted as progress events so the sidebar can show what the agent is doing\n- **thinking** -- the agent's chain-of-thought reasoning\n\n\u003E **Key concepts:**\n\u003E - **Coding agent sessions** give the AI access to `sql_execute` so it can query your Snowflake account directly\n\u003E - **Composable prompts** let you scope audits to specific object types for faster, targeted checks\n\u003E - **Poll-based streaming** avoids long-lived HTTP connections while still giving the user real-time progress\n\u003E - **Structured JSON output** means the report is machine-parseable, not just freeform text\n\n\u003C!-- ------------------------ --\u003E\n## Build the Dashboard UI\nDuration: 10\n\nThe frontend is a single-page React application built with Material UI. The component hierarchy is straightforward.\n\n### Page Root and Providers\n\n`app/page.tsx` wraps the dashboard in the required providers:\n\n```typescript\n\"use client\";\n\nimport { AppThemeProvider } from \"@/components/ThemeContext\";\nimport { AuditDashboard } from \"@/components/AuditDashboard\";\n\nexport default function Page() {\n  return (\n    \u003CAppThemeProvider\u003E\n      \u003CAuditDashboard /\u003E\n    \u003C/AppThemeProvider\u003E\n  );\n}\n```\n\n`AppThemeProvider` wraps the app in the MUI dark/light theme with localStorage persistence.\n\n### AuditDashboard -- The Orchestrator\n\n`AuditDashboard.tsx` connects the hooks to the layout:\n\n```typescript\nexport function AuditDashboard() {\n  const {\n    isAuditing, isLoading, report, messages,\n    toolProgress, error, stats,\n    startAudit, cancelAudit, resetView,\n  } = useAudit();\n\n  const { fixState, requestFix, sendFollowUp, dismissFix } = useSuggestFix();\n\n  return (\n    \u003CBox sx={{ height: \"100vh\", display: \"flex\", flexDirection: \"column\" }}\u003E\n      \u003CAuditHeader\n        onStartAudit={(db, scope, schema) =\u003E startAudit(db, scope, schema)}\n        onCancel={cancelAudit}\n        onReset={resetView}\n        isLoading={isLoading}\n        isAuditing={isAuditing}\n      /\u003E\n\n      \u003CBox sx={{ flex: 1, overflow: \"hidden\", display: \"flex\" }}\u003E\n        {!hasAuditData ? (\n          \u003CEmptyState /\u003E\n        ) : (\n          \u003C\u003E\n            {/* Chat thread with inline report accordion */}\n            \u003CBox sx={{ flex: 1, overflow: \"auto\" }}\u003E\n              \u003CChatThread\n                messages={messages}\n                report={report}\n                toolProgress={toolProgress}\n                isLoading={isLoading}\n                onSuggestFix={handleSuggestFix}\n                fixState={fixState}\n              /\u003E\n            \u003C/Box\u003E\n\n            {/* Real-time stats sidebar */}\n            \u003CAuditSidebar\n              toolProgress={toolProgress}\n              report={report}\n              stats={stats}\n              isLoading={isLoading}\n            /\u003E\n          \u003C/\u003E\n        )}\n      \u003C/Box\u003E\n    \u003C/Box\u003E\n  );\n}\n```\n\nThe layout has three zones:\n- **AuditHeader** -- database and schema selectors (fetched via `/api/databases` and `/api/schemas`), scope checkboxes, and the \"Run Audit\" button\n- **ChatThread** -- the main content area showing the conversation, tool call activity, and the audit report as an expandable accordion with severity badges\n- **AuditSidebar** -- a live feed of tool calls with SQL previews and elapsed time\n\n### The useAudit Hook -- Poll-Based State Machine\n\nThe `useAudit` hook drives the audit lifecycle. The core polling function is reused by both audit and fix flows:\n\n```typescript\nconst POLL_INTERVAL_MS = 2000;\n\nexport async function pollJob(\n  jobId: string,\n  onEvent: (event: StreamEvent) =\u003E void,\n  signal: AbortSignal,\n): Promise\u003Cvoid\u003E {\n  let cursor = 0;\n  while (!signal.aborted) {\n    const resp = await fetch(\n      `/api/audit/progress?jobId=${encodeURIComponent(jobId)}&after=${cursor}`,\n      { credentials: \"include\", signal },\n    );\n    const data = await resp.json();\n    for (const event of data.events) {\n      onEvent(event);\n    }\n    cursor = data.cursor;\n    if (data.done) return;\n    await new Promise((resolve) =\u003E setTimeout(resolve, POLL_INTERVAL_MS));\n  }\n}\n```\n\nThe `startAudit` callback POSTs to `/api/audit`, gets back a `jobId`, then polls for events. Each event type maps to a state update:\n\n| Event type | State change |\n|---|---|\n| `tool_progress` | Appends to sidebar + message tool call list |\n| `thinking` | Appends to the assistant message's thinking steps |\n| `report` | Sets the parsed audit report |\n| `result` | Clears loading state, sets duration/tool call stats |\n| `error` | Sets error message, clears loading |\n\nThe poll includes retry logic with exponential backoff for transient 5xx errors -- important since SAR proxies can occasionally hiccup.\n\n\u003E **Key concepts:**\n\u003E - **Cursor-based polling** ensures no events are missed even if a poll is delayed\n\u003E - **AbortController** lets the user cancel a running audit cleanly\n\u003E - **useEffect + fetch** handles the database/schema dropdowns with loading states\n\n\u003C!-- ------------------------ --\u003E\n## Add Suggest Fix\nDuration: 10\n\nAfter the audit produces findings, each one gets a \"Suggest Fix\" button. This opens a separate coding agent session focused on remediation.\n\n### The Suggest Fix API\n\n`app/api/suggest-fix/route.ts` creates a new agent session with a remediation-focused prompt:\n\n```typescript\nasync function runSuggestFixJob(jobId, finding, database) {\n  const systemPrompt = `You are a Snowflake pipeline remediation expert. \nWhen given a finding, respond with a clear, actionable fix. \nUse SQL code blocks for any commands. \nIf the user asks you to run SQL, you may use sql_execute.\nTarget database: ${database}.`;\n\n  const userPrompt = `Suggest a concrete fix for this pipeline audit finding:\n\nFinding:\n- Severity: ${finding.severity}\n- Category: ${finding.category}\n- Object: ${finding.object}\n- Message: ${finding.message}\n${finding.details ? `- Details: ${JSON.stringify(finding.details)}` : \"\"}\n\nRules:\n- Do NOT run any tools or queries. Respond with your answer directly.\n- Provide exact SQL commands in fenced code blocks.\n- Explain briefly why the fix works.\n- Keep the response under 500 words.`;\n\n  const messages = [{\n    role: \"user\",\n    content: [{ type: \"text\", text: systemPrompt + \"\\n\\n\" + userPrompt }],\n  }];\n\n  const client = createAgentClient();\n  const stream = client.codingAgent.stream({\n    permissionPolicy: \"always_allow\",\n    messages,\n  });\n\n  // Use callbacks to stream text to the job store\n  stream.on(\"text\", (delta) =\u003E {\n    pushJobEvent(jobId, { type: \"text\", text: delta });\n  });\n\n  await stream.done();\n\n  // Save the conversation thread for follow-up chat\n  setFixSession([...messages, {\n    role: \"assistant\",\n    content: [{ type: \"text\", text: stream.currentText }],\n  }]);\n}\n```\n\nThe fix session is stored per-job in the job store so follow-up chat can continue the conversation without leaking state between requests.\n\n### Multi-Turn Follow-Up Chat\n\nThe user can ask follow-up questions (\"Can you also add a monitoring task?\", \"What about the downstream views?\"). `app/api/suggest-fix/chat/route.ts` appends the new message to the stored thread and streams back the response:\n\n```typescript\nasync function runFixChatJob(jobId: string, message: string) {\n  const fixThread = getFixSession();\n  const client = createAgentClient();\n\n  // Append user message to the thread\n  fixThread.push({\n    role: \"user\",\n    content: [{ type: \"text\", text: message }],\n  });\n\n  // Stream with full conversation history for context\n  const stream = client.codingAgent.stream({\n    permissionPolicy: \"always_allow\",\n    messages: fixThread,\n  });\n\n  // ... accumulate text, push events, update thread\n}\n```\n\nThis is a true multi-turn conversation -- the agent sees every prior message, so it can refine its suggestions based on the user's feedback.\n\n### The useSuggestFix Hook\n\nOn the frontend, `hooks/useSuggestFix.ts` manages the fix lifecycle:\n\n```typescript\nexport function useSuggestFix() {\n  const [state, setState] = useState\u003CSuggestFixState\u003E(IDLE);\n\n  const requestFix = useCallback(async (index, finding, database) =\u003E {\n    // POST to /api/suggest-fix, poll for text events\n    // Update state with streaming assistant messages\n  }, []);\n\n  const sendFollowUp = useCallback(async (message) =\u003E {\n    // POST to /api/suggest-fix/chat, poll for response\n    // Append user message + streaming assistant response\n  }, []);\n\n  const dismissFix = useCallback(() =\u003E {\n    // Abort, reset state, POST to /api/suggest-fix/dismiss\n  }, []);\n\n  return { fixState: state, requestFix, sendFollowUp, dismissFix };\n}\n```\n\nThe fix panel renders inline in the ChatThread component. Each finding in the report has a \"Suggest Fix\" button. When clicked, it opens a chat-like panel below the finding with the AI's suggestion and a text input for follow-ups.\n\n### Copy and Run\n\nSQL code blocks in fix suggestions include a **Copy** button so users can paste them into a Snowflake worksheet for execution.\n\n\u003E **Key concepts:**\n\u003E - **Separate agent sessions** for audit vs. fix keep concerns isolated and prompts focused\n\u003E - **Multi-turn chat** with full message history gives the agent context for iterative refinement\n\u003E - **Per-job thread state** (stored in the job store) keeps conversations isolated between requests\n\u003E - The coding agent uses `permissionPolicy: \"always_allow\"` because it needs to run SQL queries autonomously. Deploy the app with a **read-only role** to limit what the agent can do.\n\n\u003C!-- ------------------------ --\u003E\n## Deploy and Run\nDuration: 10\n\n### Generate the Deployment Manifest\n\nThe project includes an `app.yml` that defines the build and run phases:\n\n```yaml\ninstall:\n  commands:\n    - [\"npm\", \"ci\", \"--include=dev\"]\n\nbuild:\n  commands:\n    - [\"npm\", \"run\", \"build\"]\n    - [\"cp\", \"-r\", \".next/static\", \".next/standalone/.next/static\"]\n    - [\"cp\", \"-r\", \"public\", \".next/standalone/public\"]\n    - [\"rm\", \"-rf\", \"node_modules\"]\n\nrun:\n  command: [\"node\", \".next/standalone/server.js\"]\n```\n\nThe build uses Next.js standalone output mode, which bundles only the required Node.js modules into `.next/standalone/` for a smaller container image.\n\n### Set Up and Deploy\n\nFrom the project directory, run:\n\n```bash\nsnow app setup\n```\n\nThis generates a `snowflake.yml` with your deployment target (account, database, warehouse). Review it, then deploy:\n\n```bash\nsnow app deploy\n```\n\nThe deploy process:\n1. Builds the Next.js app inside an SPCS container\n2. Pushes the container image to the Snowflake image registry\n3. Creates an APPLICATION SERVICE object\n4. Makes the app available at a URL\n\n### Open the App\n\nAfter deployment completes, `snow app deploy` prints the app URL. Open it in your browser.\n\nYou can also find the URL with:\n\n```bash\nsnow app status\n```\n\n### Run Your First Audit\n\n1. **Select a database** from the dropdown in the header. The app queries `SHOW DATABASES` to populate the list.\n\n2. **(Optional) Select a schema** to narrow the audit to a single schema instead of the entire database.\n\n3. **Choose your audit scope** using the checkboxes. You can audit any combination of:\n   - Tables and freshness\n   - Dynamic tables\n   - Tasks\n   - Views\n   - Streams\n   - Pipes\n   - Stored procedures\n\n4. **Click \"Run Audit\"**. The sidebar lights up with real-time progress as the agent executes discovery queries and health checks.\n\n5. **Review the report**. When the audit completes, the report appears as an expandable accordion in the chat thread. Findings are grouped by severity with color-coded badges:\n   - Red for critical (stale data, failing refreshes, stale streams)\n   - Orange for warning (approaching thresholds, suspended tasks)\n   - Blue for info (best practice suggestions, architecture notes)\n\n6. **Suggest fixes**. Click \"Suggest Fix\" on any finding to open a remediation chat. The agent provides SQL commands you can review and copy into a Snowflake worksheet.\n\n### Local Development\n\nFor iterating on the app locally:\n\n```bash\nnpm run dev\n```\n\nThis starts the Next.js dev server at `http://localhost:3000`. Make sure your `.env.local` has `SNOWFLAKE_ACCOUNT` and `SNOWFLAKE_PAT` set.\n\n\u003C!-- ------------------------ --\u003E\n## Conclusion And Resources\nDuration: 2\n\nYou have built a full-stack AI-powered pipeline auditor that runs on Snowflake App Runtime. The app uses a Cortex AI coding agent to autonomously discover pipeline objects, run health checks, and produce structured reports -- with an interactive fix suggestion workflow for remediation.\n\n### What You Learned\n- How to scaffold and deploy a Next.js app on Snowflake App Runtime with `snow app`\n- How to use `@snowflake/cortex-agent-sdk` to run coding agent sessions that analyze your pipelines\n- How to build composable system prompts that produce structured JSON reports\n- How to create a poll-based architecture for real-time agent progress\n- How to implement AI-powered fix suggestions with multi-turn chat\n\n### Extending It\n- **Schedule audits** with Snowflake Tasks for daily or weekly health reports\n- **Send notifications** via `SYSTEM$SEND_EMAIL` when critical issues are found\n- **Store audit history** in a Snowflake table for trend analysis over time\n- **Add more scopes** like Snowpipe latency, warehouse queue depth, or credit anomalies\n\n### Related Resources\n- [@snowflake/cortex-agent-sdk on npm](https://www.npmjs.com/package/@snowflake/cortex-agent-sdk)\n- [Snowflake App Runtime Documentation](https://docs.snowflake.com/en/developer-guide/snowflake-app-runtime)\n- [Cortex Agents Overview](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents)\n- [Pipeline Auditor Companion Repository](https://github.com/sfc-gh-JCHAEARLEY/sfguide-pipeline-auditor-with-cortex-agent-sdk)\n\n### Cleanup\n\nTo remove the demo objects created in this quickstart:\n\n```sql\nDROP DATABASE IF EXISTS PIPELINE_AUDITOR_DB;\n-- Also drop the Application Service if deployed:\nDROP APPLICATION SERVICE IF EXISTS pipeline_auditor;\n```\n","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-a252f08cfa","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-9552895391",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-ec4dc879eb","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-114d93f058","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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