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pre[class*=language-]{background-color:rgba(var(--ui-12-rgb),.5);color:var(--text-01);text-shadow:none;padding:var(--spacing-00);border-radius:var(--spacing-00);font-size:smaller}",":type":"snowflake-site/components/markup-editor","isGSAPEnabled":false},"responsivegrid":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"quickstart_hero":"aem-GridColumn aem-GridColumn--default--12","flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12","markup_editor":"aem-GridColumn aem-GridColumn--default--12"},"columnCount":12,":items":{"quickstart_hero":{"id":"quickstart-hero-a3f1e90bfa","quickstartHeroFirstCertifiedTag":{"tagText":"Partner 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A customer submits a loan application — it needs to be scored by an AI model, checked against compliance rules, routed to an underwriter if the amount is high enough, and the decision needs to land back in the CRM and notify the operations team. A supplier sends an invoice — it needs to be matched against purchase orders in the ERP, flagged for anomalies, approved by finance if the amount exceeds a threshold, and posted to the ledger. A patient's lab results come in — they need to be compared against clinical baselines, escalated to the care team if values are critical, and the chart needs to be updated across three systems before the next appointment.\u003C/p\u003E\n","\u003Cp\u003EThese workflows are not static. The next step depends on what the AI model returns, what the data says, or what a human decides. They cross system boundaries — databases, SaaS platforms, notification channels, approval systems. They need to recover when something fails midway. And they need observability — not just "did it run?" but "what did it decide, why, and who approved it?"\u003C/p\u003E\n","\u003Cp\u003EThis is the kind of workflow \u003Ca href=\"https://www.prefect.io/\"\u003EPrefect\u003C/a\u003E is purpose-built for. Prefect is a Python-native orchestration platform that brings \u003Cstrong\u003Edurable execution\u003C/strong\u003E, \u003Cstrong\u003Eevent-driven automation\u003C/strong\u003E, \u003Cstrong\u003Ehuman-in-the-loop approvals\u003C/strong\u003E, and \u003Cstrong\u003Eruntime decision-making\u003C/strong\u003E to any Python workflow. When paired with Snowflake and \u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/llm-functions\"\u003ECortex AI\u003C/a\u003E, Prefect becomes the nervous system that coordinates intelligent, multi-system workflows — while Snowflake serves as the data platform and AI engine at the center.\u003C/p\u003E\n","\u003Ch3\u003EWhat We'll Build\u003C/h3\u003E\n","\u003Cp\u003EIn this guide, we'll build an end-to-end \u003Cstrong\u003Eagentic PR review pipeline\u003C/strong\u003E in a matter of minutes — a practical example that demonstrates how Prefect and Snowflake work together for workflows that cross system boundaries and make decisions at runtime. The pipeline will:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\u003Cstrong\u003EReact\u003C/strong\u003E to a pull request in GitHub\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EIngest\u003C/strong\u003E the PR data into Snowflake\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EClassify risk and summarize changes\u003C/strong\u003E using Snowflake Cortex AI — the intelligence runs inside Snowflake, not an external LLM\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EQuery historical context\u003C/strong\u003E from Snowflake to inform the decision\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EBranch at runtime\u003C/strong\u003E based on the AI assessment — low risk auto-approves, medium/high risk escalates\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ENotify\u003C/strong\u003E the team in Slack and \u003Cstrong\u003Epause for human approval\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EWrite\u003C/strong\u003E the final assessment back to Snowflake\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EMerge or close\u003C/strong\u003E the PR on GitHub with a Cortex AI review comment\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESend\u003C/strong\u003E a completion notice to Slack\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EIf the pipeline fails at step 7, Prefect \u003Cstrong\u003Eresumes from step 7\u003C/strong\u003E — it doesn't re-run the Cortex AI calls or re-fetch from GitHub. That's durable execution.\u003C/p\u003E\n","\u003Ch3\u003EThe Architecture\u003C/h3\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-prefect-and-snowflake/architecture.png?v=07692dea\" alt=\"Architecture\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003ESnowflake account with Cortex AI access\u003C/li\u003E\u003Cli\u003EPrefect Cloud account (free tier works)\u003C/li\u003E\u003Cli\u003EGitHub account with a test repository\u003C/li\u003E\u003Cli\u003ESlack workspace with a bot app\u003C/li\u003E\u003Cli\u003EPython 3.9+\u003C/li\u003E\u003C/ul\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EDetailed setup instructions\u003C/strong\u003E for Snowflake, Python, Slack, and GitHub are in the \u003Ca href=\"https://github.com/sfc-gh-aramasagaram/sfguide-getting-started-with-prefect-and-snowflake\"\u003Ecompanion repo\u003C/a\u003E. This guide focuses on the pipeline itself.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESetup\u003C/h2\u003E\n","\u003Cp\u003EThe companion repo has full setup instructions. Here's the minimum to get running:\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ESnowflake\u003C/strong\u003E — Run this in a SQL Worksheet to create the database, tables, and seed data:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE DATABASE IF NOT EXISTS PREFECT_QUICKSTART;\nCREATE SCHEMA IF NOT EXISTS PREFECT_QUICKSTART.RAW;\nCREATE SCHEMA IF NOT EXISTS PREFECT_QUICKSTART.ANALYTICS;\nCREATE WAREHOUSE IF NOT EXISTS PREFECT_WH WITH WAREHOUSE_SIZE='XSMALL' AUTO_SUSPEND=60 AUTO_RESUME=TRUE;\n\n-- Verify Cortex AI works in your region\nSELECT SNOWFLAKE.CORTEX.COMPLETE('mistral-large2', 'Say hello') AS test;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EIf the Cortex test fails, try llama3.1-70b or claude-3-5-sonnet — model availability varies by region. You can also enable cross-region inference: ALTER ACCOUNT SET CORTEX_ENABLED_CROSS_REGION = 'ANY_REGION';\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EFull table DDL and seed data are in the repo's \u003Ccode\u003Esetup.sql\u003C/code\u003E.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPython\u003C/strong\u003E — Install dependencies and authenticate:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Euv pip install prefect requests pandas "snowflake-connector-python[pandas]"\nprefect cloud login\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Ccode\u003Epause_flow_run\u003C/code\u003E requires a connection to Prefect Cloud (or a self-hosted Prefect server). Make sure \u003Ccode\u003Eprefect cloud login\u003C/code\u003E completes before running the pipeline.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ESecrets\u003C/strong\u003E — Set these environment variables (or use Prefect Blocks/Secrets):\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Eexport GITHUB_TOKEN=your_github_pat\nexport SNOWFLAKE_CONNECTION_NAME=your_connection\nexport SLACK_BOT_TOKEN=xoxb-your-bot-token\nexport SLACK_CHANNEL=your-channel\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003ESee the \u003Ca href=\"https://github.com/sfc-gh-aramasagaram/sfguide-getting-started-with-prefect-and-snowflake\"\u003Erepo README\u003C/a\u003E for Snowflake connection configuration, Slack app setup, GitHub token scopes, and \u003Ccode\u003Euv\u003C/code\u003E / \u003Ccode\u003Epyproject.toml\u003C/code\u003E setup.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EFetch and Ingest\u003C/h2\u003E\n","\u003Cp\u003ECreate \u003Ccode\u003Epr_review_pipeline.py\u003C/code\u003E. The pipeline starts by fetching PR data from GitHub and landing it in Snowflake.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E# retries with exponential backoff — handles GitHub rate limits automatically\n@task(retries=3, retry_delay_seconds=exponential_backoff(backoff_factor=2), log_prints=True)\ndef fetch_pr_data(repo: str, pr_number: int) -> dict:\n    logger = get_run_logger()\n    headers = {"Authorization": f"token {GITHUB_TOKEN}"} if GITHUB_TOKEN else {}\n\n    # Fetch PR metadata (title, author, stats)\n    pr_resp = requests.get(f"https://api.github.com/repos/{repo}/pulls/{pr_number}", headers=headers)\n    pr_resp.raise_for_status()\n    pr = pr_resp.json()\n\n    # Fetch the raw diff separately (same endpoint, different Accept header)\n    diff_resp = requests.get(\n        f"https://api.github.com/repos/{repo}/pulls/{pr_number}",\n        headers={**headers, "Accept": "application/vnd.github.v3.diff"},\n    )\n\n    # Return a clean dict — truncate diff to 50k chars to stay within Cortex limits\n    return {\n        "pr_number": pr_number, "repo": repo,\n        "title": pr["title"], "author": pr["user"]["login"],\n        "diff_content": diff_resp.text[:50000],\n        "files_changed": pr["changed_files"],\n        "additions": pr["additions"], "deletions": pr["deletions"],\n        "created_at": pr["created_at"],\n    }\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENote the \u003Cstrong\u003Eexponential backoff\u003C/strong\u003E: \u003Ccode\u003Eexponential_backoff(backoff_factor=2)\u003C/code\u003E generates delays of 2s, 4s, 8s, etc. If GitHub's API is rate-limited, Prefect retries automatically with increasing wait times.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E@task(retries=2, retry_delay_seconds=exponential_backoff(backoff_factor=2), log_prints=True)\ndef load_pr_to_snowflake(pr_data: dict) -> int:\n    conn = get_connection()\n    # Build a single-row DataFrame matching the PULL_REQUESTS table schema\n    df = pd.DataFrame([{\n        "PR_NUMBER": pr_data["pr_number"], "REPO": pr_data["repo"],\n        "TITLE": pr_data["title"], "AUTHOR": pr_data["author"],\n        "DIFF_CONTENT": pr_data["diff_content"],\n        "FILES_CHANGED": pr_data["files_changed"],\n        "ADDITIONS": pr_data["additions"], "DELETIONS": pr_data["deletions"],\n        "CREATED_AT": pd.Timestamp(pr_data["created_at"]),\n    }])\n\n    # write_pandas bulk-loads via PUT + COPY INTO — fast even for large diffs\n    write_pandas(conn=conn, df=df, table_name="PULL_REQUESTS",\n                 database="PREFECT_QUICKSTART", schema="RAW")\n    return 1\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EEvery PR that enters this pipeline gets a permanent record in Snowflake.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECortex AI Assessment\u003C/h2\u003E\n","\u003Cp\u003EThis is the most important step. Instead of calling an external LLM, the AI runs \u003Cstrong\u003Einside Snowflake\u003C/strong\u003E — co-located with your data, governed by your access policies. We use \u003Cstrong\u003E\u003Ccode\u003ECLASSIFY_TEXT\u003C/code\u003E\u003C/strong\u003E for risk classification (returns a clean category — no prompt engineering or output parsing needed) and \u003Cstrong\u003E\u003Ccode\u003ECOMPLETE\u003C/code\u003E\u003C/strong\u003E for the free-form summary.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E@task(retries=2, retry_delay_seconds=exponential_backoff(backoff_factor=2), log_prints=True)\ndef cortex_assess_pr(pr_data: dict) -> dict:\n    logger = get_run_logger()\n    conn = get_connection()\n    # Truncate diff to 5k chars — balances context vs. token cost\n    diff_preview = pr_data["diff_content"][:5000]\n\n    # Build a shared context string for both Cortex calls\n    pr_context = (\n        f"PR Title: {pr_data['title']}\\n"\n        f"Files changed: {pr_data['files_changed']}, "\n        f"+{pr_data['additions']}/-{pr_data['deletions']}\\n"\n        f"Diff:\\n{diff_preview}"\n    )\n\n    summarize_prompt = (\n        f"Summarize this PR in 2-3 sentences for a technical reviewer.\\n\\n{pr_context}"\n    )\n\n    cur = conn.cursor()\n    # CLASSIFY_TEXT: purpose-built for classification — returns one of the given categories\n    cur.execute(\n        "SELECT SNOWFLAKE.CORTEX.CLASSIFY_TEXT(%s, ['low', 'medium', 'high'])",\n        (pr_context,),\n    )\n    risk_level = cur.fetchone()[0].strip().lower()\n\n    # COMPLETE: free-form generation for the summary\n    cur.execute("SELECT SNOWFLAKE.CORTEX.COMPLETE(%s, %s)", ("mistral-large2", summarize_prompt))\n    summary = cur.fetchone()[0].strip()\n    cur.close()\n\n    # Defensive fallback in case CLASSIFY_TEXT returns something unexpected\n    if risk_level not in ("low", "medium", "high"):\n        risk_level = "medium"\n\n    return {"risk_level": risk_level, "summary": summary}\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ETwo Cortex calls happen here — \u003Ccode\u003ECLASSIFY_TEXT\u003C/code\u003E for structured classification, \u003Ccode\u003ECOMPLETE\u003C/code\u003E for free-form generation. Each costs credits on an XSMALL warehouse (expect ~0.01-0.02 credits per call). This is the \u003Cstrong\u003Emost expensive step\u003C/strong\u003E in the pipeline. If anything downstream fails, Prefect doesn't re-execute these calls on retry — that's durable execution.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWhy two different Cortex functions?\u003C/strong\u003E Use the right tool for the job. \u003Ccode\u003ECLASSIFY_TEXT\u003C/code\u003E guarantees a clean category from your list — no regex parsing, no prompt engineering for output format. \u003Ccode\u003ECOMPLETE\u003C/code\u003E is for open-ended generation where you need free-form text.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EDecision and Human Gate\u003C/h2\u003E\n","\u003Cp\u003EBased on the Cortex AI classification and historical context, the pipeline \u003Cstrong\u003Edecides at runtime\u003C/strong\u003E what path to take.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E@task(retries=2, retry_delay_seconds=exponential_backoff(backoff_factor=2), log_prints=True)\ndef get_historical_context(pr_data: dict) -> dict:\n    conn = get_connection()\n    cur = conn.cursor()\n    # Bind parameter for AUTHOR — safe against SQL injection\n    cur.execute(\n        "SELECT COUNT(*) AS total_prs, "\n        "SUM(CASE WHEN HAD_INCIDENT THEN 1 ELSE 0 END) AS incidents "\n        "FROM PREFECT_QUICKSTART.ANALYTICS.PR_HISTORY "\n        "WHERE AUTHOR = %s",\n        (pr_data["author"],),\n    )\n    result = cur.fetchone()\n    cur.close()\n    return {"total_prs": result[0] or 0, "incident_count": result[1] or 0}\n\n\n@task(log_prints=True)\ndef make_decision(pr_data: dict, assessment: dict, history: dict) -> dict:\n    risk = assessment["risk_level"]\n\n    # Elevate risk if Cortex said "medium" but the author has caused incidents before\n    if risk == "high" or (risk == "medium" and history["incident_count"] > 0):\n        effective_risk = "high"\n    elif risk == "medium":\n        effective_risk = "medium"\n    else:\n        effective_risk = "low"\n\n    # This dict drives the runtime branching in the flow\n    return {\n        "effective_risk": effective_risk,\n        "requires_approval": effective_risk in ("medium", "high"),\n        "auto_approved": effective_risk == "low",\n    }\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis is \u003Cstrong\u003Eruntime branching\u003C/strong\u003E — the pipeline's next steps depend on values computed in this run, not a predetermined DAG. An author with prior incidents gets elevated risk, which triggers human approval.\u003C/p\u003E\n","\u003Cp\u003EFor human-in-the-loop, define a type-safe input schema:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E# Type-safe input schema — Prefect renders this as a form in the Cloud UI\nclass ReviewDecision(RunInput):\n    approved: bool\n    reviewer_name: str\n    notes: str = ""\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWhen the flow calls \u003Ccode\u003Epause_flow_run(wait_for_input=ReviewDecision, timeout=86400)\u003C/code\u003E, execution literally \u003Cstrong\u003Estops\u003C/strong\u003E and waits for a human to submit this form in Prefect Cloud (up to 24 hours). No custom webhook infrastructure. No polling loop.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EClose the Loop\u003C/h2\u003E\n","\u003Cp\u003EAfter the decision, the pipeline closes the loop across all systems — Snowflake, GitHub, and Slack.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E@task(retries=2, retry_delay_seconds=exponential_backoff(backoff_factor=2), log_prints=True)\ndef close_github_pr(pr_data: dict, assessment: dict, decision: dict) -> None:\n    headers = {"Authorization": f"token {GITHUB_TOKEN}"}\n    repo, pr_num = pr_data["repo"], pr_data["pr_number"]\n    approved = decision.get("final_approved", False)\n\n    # Post the Cortex AI summary as a review comment on the PR\n    comment = (\n        f"## {'✅' if approved else '❌'} Prefect PR Review\\n\\n"\n        f"**Risk:** {decision['effective_risk'].upper()}\\n"\n        f"**Reviewer:** {decision.get('reviewer', 'auto')}\\n\\n"\n        f"### Cortex AI Summary\\n{assessment['summary']}\\n\\n"\n        f"---\\n*Assessed by Prefect + Snowflake Cortex AI*"\n    )\n    requests.post(f"https://api.github.com/repos/{repo}/issues/{pr_num}/comments",\n                  headers=headers, json={"body": comment})\n\n    # Merge (squash) if approved, close if rejected\n    if approved:\n        requests.put(f"https://api.github.com/repos/{repo}/pulls/{pr_num}/merge",\n                     headers=headers, json={"merge_method": "squash"})\n    else:\n        requests.patch(f"https://api.github.com/repos/{repo}/pulls/{pr_num}",\n                       headers=headers, json={"state": "closed"})\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe pipeline also sends \u003Cstrong\u003Etwo Slack notifications\u003C/strong\u003E — one when approval is needed (with a deep-link to Prefect Cloud), and one when the pipeline completes with the final decision.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-prefect-and-snowflake/SS4.png?v=07692dea\" alt=\"SS4 — Slack channel showing the approval request message with Review and Approve button\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EWiring It Together\u003C/h2\u003E\n","\u003Cp\u003EHere's where the agentic behavior comes together. One flow, four systems, runtime branching, and a human gate:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E@flow(name="pr-review-agent", log_prints=True, persist_result=True)\ndef pr_review_agent(repo: str, pr_number: int):\n    logger = get_run_logger()\n\n    # Get the flow run ID so we can deep-link Slack notifications to Prefect Cloud\n    from prefect.runtime import flow_run\n    flow_run_id = str(flow_run.id)\n\n    # Open a single Snowflake connection for the entire flow — avoids repeated\n    # OAuth browser popups and is more efficient than connecting per-task.\n    conn = snowflake.connector.connect(connection_name=SF_CONNECTION)\n    # Tag all queries so you can find them in Snowflake's QUERY_HISTORY\n    conn.cursor().execute("ALTER SESSION SET QUERY_TAG = 'prefect-pr-review'")\n\n    try:\n        # ---- Phase 1: Fetch and Ingest ----\n        # Pull PR metadata + diff from GitHub API\n        pr_data = fetch_pr_data(repo=repo, pr_number=pr_number)\n        # Land the raw data in Snowflake for auditability\n        load_pr_to_snowflake(pr_data)\n\n        # ---- Phase 2: AI Assessment ----\n        # Use Snowflake Cortex AI to classify risk and generate a summary\n        assessment = cortex_assess_pr(pr_data)\n        # Query Snowflake for this author's historical PR track record\n        history = get_historical_context(pr_data)\n        # Combine Cortex output + history into an effective risk decision\n        decision = make_decision(pr_data, assessment, history)\n\n        # ---- Phase 3: Human Gate (conditional) ----\n        # This is runtime branching — the path depends on what Cortex returned\n        if decision["requires_approval"]:\n            # Send a rich Slack message with a "Review & Approve" button\n            slack_approval_request(pr_data, assessment, decision, flow_run_id)\n\n            # PAUSE the flow — execution literally stops here and waits\n            # for a human to submit the ReviewDecision form in Prefect Cloud.\n            # timeout=86400 means the flow will fail if no one responds in 24h.\n            review = pause_flow_run(\n                wait_for_input=ReviewDecision.with_initial_data(\n                    approved=False, reviewer_name="",\n                ),\n                timeout=86400,\n            )\n\n            decision["final_approved"] = review.approved\n            decision["reviewer"] = review.reviewer_name\n\n            # Write the assessment BEFORE the early return — every decision\n            # (approved or rejected) gets an audit trail in Snowflake\n            write_assessment(pr_data, assessment, decision,\n                             reviewer=review.reviewer_name, reviewer_notes=review.notes)\n\n            if not review.approved:\n                # Rejected: close the PR on GitHub + notify Slack, then exit\n                close_github_pr(pr_data, assessment, decision)\n                slack_completion_notice(pr_data, assessment, decision)\n                return\n\n        else:\n            # Low risk — auto-approve without human intervention\n            decision["final_approved"] = True\n            decision["reviewer"] = "auto"\n            write_assessment(pr_data, assessment, decision)\n\n        # ---- Phase 4: Close the Loop ----\n        # Merge the PR on GitHub and post the Cortex AI review comment\n        close_github_pr(pr_data, assessment, decision)\n        # Send a final Slack notification with the outcome\n        slack_completion_notice(pr_data, assessment, decision)\n\n    finally:\n        # Always close the Snowflake connection, even if the flow fails\n        conn.close()\n\n\nif __name__ == "__main__":\n    pr_review_agent(repo="your-org/your-repo", pr_number=1)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWhat's happening:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EResult persistence\u003C/strong\u003E — set \u003Ccode\u003EPREFECT_RESULTS_PERSIST_BY_DEFAULT=true\u003C/code\u003E in your environment to persist all task results automatically. This enables durable execution: retries skip completed work. By default results persist to \u003Ccode\u003E~/.prefect/storage/\u003C/code\u003E locally; for production, configure a remote result storage block (S3, GCS, or Azure Blob) so results survive across machines.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003E\u003Ccode\u003Epause_flow_run(timeout=86400)\u003C/code\u003E\u003C/strong\u003E — execution stops and waits up to 24 hours for human input\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003E\u003Ccode\u003EQUERY_TAG\u003C/code\u003E\u003C/strong\u003E — tags all Snowflake queries for cost attribution in \u003Ccode\u003EQUERY_HISTORY\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EThe \u003Ccode\u003Eif\u003C/code\u003E branch\u003C/strong\u003E — a Python \u003Ccode\u003Eif\u003C/code\u003E evaluated at runtime based on Cortex AI output\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003E\u003Ccode\u003Ewrite_assessment\u003C/code\u003E runs for both approved AND rejected PRs\u003C/strong\u003E — every decision gets an audit trail in Snowflake\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EModule-level connection\u003C/strong\u003E — tasks call \u003Ccode\u003Eget_connection()\u003C/code\u003E internally rather than receiving the connection as a parameter. This keeps task signatures clean (only business data in/out), avoids serialization issues with caching, and follows idiomatic Prefect patterns.\u003C/li\u003E\u003C/ul\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ESlack tasks and \u003Ccode\u003Ewrite_assessment\u003C/code\u003E\u003C/strong\u003E are defined in the companion repo. They follow the same pattern: tasks call \u003Ccode\u003Eget_connection()\u003C/code\u003E internally and use bind parameters for all Snowflake queries.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ERun It\u003C/h2\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003EGITHUB_TOKEN=your_token \\\nSNOWFLAKE_CONNECTION_NAME=your_connection \\\nSLACK_BOT_TOKEN=xoxb-your-token \\\nSLACK_CHANNEL=your-channel \\\npython pr_review_pipeline.py\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWatch the pipeline execute in real-time in Prefect Cloud. You'll see:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EEach task completing on the timeline\u003C/li\u003E\u003Cli\u003EThe Cortex AI assessment results in the logs\u003C/li\u003E\u003Cli\u003EThe Slack notification arrive\u003C/li\u003E\u003Cli\u003EThe flow pause, waiting for your approval\u003C/li\u003E\u003Cli\u003EAfter you approve — the PR merges on GitHub, Slack gets the completion notice, and the assessment lands in Snowflake\u003C/li\u003E\u003C/ol\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EThis runs the flow as a local Python process connected to Prefect Cloud. For production, deploy with \u003Ccode\u003Eprefect deploy\u003C/code\u003E and run your worker on \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowpark-container-services/overview\"\u003ESnowpark Container Services\u003C/a\u003E — your orchestration compute stays inside Snowflake's security perimeter. See the \u003Ca href=\"https://github.com/sfc-gh-aramasagaram/sfguide-getting-started-with-prefect-and-snowflake\"\u003Ecompanion repo\u003C/a\u003E for a \u003Ccode\u003Eprefect.yaml\u003C/code\u003E deployment example.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-prefect-and-snowflake/SS5.png?v=07692dea\" alt=\"SS5 — Prefect Cloud flow run timeline showing all tasks completed in sequence\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-prefect-and-snowflake/SS6.png?v=07692dea\" alt=\"SS6 — Slack channel showing the completion notice (APPROVED)\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EDeploy It\u003C/h2\u003E\n","\u003Cp\u003ERunning \u003Ccode\u003Epython pr_review_pipeline.py\u003C/code\u003E is great for development. To operate this as a production workflow — with authentication, RBAC, scheduling, and event triggers — deploy to \u003Cstrong\u003EPrefect Cloud\u003C/strong\u003E. You'll need a worker to execute your flows. Options include:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EProcess worker\u003C/strong\u003E — runs on your local machine (simplest for testing)\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EPrefect Managed Execution\u003C/strong\u003E — Prefect runs it for you (no infrastructure to manage)\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESPCS worker\u003C/strong\u003E — runs inside Snowpark Container Services (keeps compute in Snowflake's perimeter)\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003ECreate a \u003Ccode\u003Eprefect.yaml\u003C/code\u003E at the root of your project:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-yaml\"\u003Epull:\n  - prefect.deployments.steps.git_clone:\n      repository: "{{ $GIT_REPO_URL }}"\n      branch: main\n      access_token: "{{ $GIT_ACCESS_TOKEN }}"\n\ndeployments:\n  - name: pr-review-agent\n    entrypoint: pr_review_pipeline.py:pr_review_agent\n    work_pool:\n      name: spcs-pool    # Prefect Worker on Snowpark Container Services\n    tags: [github, code-review, cortex-ai]\n    parameters:\n      repo: your-org/your-repo\n      pr_number: 1\n    enforce_parameter_schema: true\n    description: "Agentic PR review: GitHub → Cortex AI → Human approval → Merge"\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EDeploy and run:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003E# Register the deployment with Prefect Cloud\nprefect deploy --all\n\n# Trigger it on-demand from the CLI\nprefect deployment run pr-review-agent/pr-review-agent \\\n  --param repo=your-org/your-repo \\\n  --param pr_number=42\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EFrom here you can add \u003Cstrong\u003EAutomations\u003C/strong\u003E in Prefect Cloud to trigger this deployment from a GitHub webhook — turning \u003Ccode\u003Epython pr_review_pipeline.py\u003C/code\u003E into a fully event-driven workflow.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EFor production, we recommend \u003Cstrong\u003EPrefect Cloud\u003C/strong\u003E (authentication, RBAC, audit logs, SSO) with a \u003Cstrong\u003EPrefect Worker running on Snowpark Container Services (SPCS)\u003C/strong\u003E — keeping your orchestration compute inside Snowflake's security perimeter.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EWhat Just Happened\u003C/h2\u003E\n","\u003Cp\u003EOnce your accounts are set up, the pipeline runs in under a minute:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\u003Cstrong\u003EFetched\u003C/strong\u003E a PR from GitHub (external API)\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ELoaded\u003C/strong\u003E it into Snowflake (data persistence)\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ERan Cortex AI\u003C/strong\u003E to classify risk and summarize changes (AI inside Snowflake)\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EQueried\u003C/strong\u003E author history for context (analytical query)\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EDecided at runtime\u003C/strong\u003E whether to auto-approve or escalate (dynamic branching)\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ENotified\u003C/strong\u003E your team in Slack with a rich message (external notification)\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EPaused\u003C/strong\u003E and waited for a human to approve (human-in-the-loop)\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EWrote\u003C/strong\u003E the assessment to Snowflake (audit trail)\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EMerged\u003C/strong\u003E the PR on GitHub with a Cortex AI review comment (closing the loop)\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESent\u003C/strong\u003E a final Slack notification confirming the decision (completion notice)\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EThat's \u003Cstrong\u003Efour systems\u003C/strong\u003E (GitHub, Snowflake, Slack, Prefect), \u003Cstrong\u003Etwo Cortex AI calls\u003C/strong\u003E, a \u003Cstrong\u003Ehuman approval gate\u003C/strong\u003E, and \u003Cstrong\u003Eruntime decision-making\u003C/strong\u003E — all in one Python flow with full observability.\u003C/p\u003E\n","\u003Cp\u003EIf the pipeline had failed at step 8, retrying would skip steps 1–5 entirely. Durable execution means you don't pay for expensive work twice.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-prefect-and-snowflake/SS7.png?v=07692dea\" alt=\"SS7 — GitHub PR showing the Cortex AI review comment posted by the pipeline and Merged status\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECleanup\u003C/h2\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003ESkip this if you want to keep your PR assessment history in Snowflake.\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EDROP DATABASE IF EXISTS PREFECT_QUICKSTART;\nDROP WAREHOUSE IF EXISTS PREFECT_WH;\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion\u003C/h2\u003E\n","\u003Cp\u003EYou built a pipeline that no single platform can run alone. GitHub triggers the work. Snowflake stores the data and runs the AI. Prefect coordinates everything — with durability, human gates, and runtime decisions. Slack keeps the team in the loop. And the PR gets merged or closed automatically.\u003C/p\u003E\n","\u003Ch3\u003ENow Imagine Your Workflows\u003C/h3\u003E\n","\u003Cp\u003EThe PR review pipeline is one example. But think about the workflows in your organization that span systems and require runtime decisions:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003EData quality incident response\u003C/strong\u003E — A freshness check fails in Snowflake. The pipeline queries downstream impact using Cortex AI, pages the on-call team in PagerDuty, pauses for a human to decide whether to halt dependent pipelines or proceed, then updates the data catalog and Slack channel with the resolution.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003EML model deployment with guardrails\u003C/strong\u003E — A new model version is trained. The pipeline runs validation against holdout data in Snowflake, uses Cortex AI to compare performance against the current production model, pauses for ML lead approval if performance regresses on any segment, then promotes to production and notifies stakeholders.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003ECompliance-driven data access\u003C/strong\u003E — A user requests access to a sensitive dataset. The pipeline checks their role and clearance in Snowflake, uses Cortex AI to classify the sensitivity of the requested data, routes approval to the data steward if sensitivity is high, provisions access on approval, and logs the entire chain for audit.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003EMulti-vendor ETL orchestration\u003C/strong\u003E — Data arrives from Salesforce, Stripe, and a partner SFTP. The pipeline validates each source, loads to Snowflake, uses Cortex AI to detect anomalies in the combined dataset, pauses for analyst review if anomalies exceed thresholds, then triggers dbt transformations and refreshes downstream dashboards.\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EAny time your workflow crosses system boundaries, makes decisions at runtime, or needs a human in the loop — that's where Prefect and Snowflake work together.\u003C/p\u003E\n","\u003Cp\u003EWe hope this guide gave you a glimpse of what's possible. \u003Cstrong\u003ESnowflake powers the intelligence. Prefect orchestrates the workflow.\u003C/strong\u003E The code is in the \u003Ca href=\"https://github.com/sfc-gh-aramasagaram/sfguide-getting-started-with-prefect-and-snowflake\"\u003Ecompanion repo\u003C/a\u003E — fork it and make it yours.\u003C/p\u003E\n","\u003Ch3\u003EResources\u003C/h3\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ETopic\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ELink\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EFull source code and setup\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ca href=\"https://github.com/sfc-gh-aramasagaram/sfguide-getting-started-with-prefect-and-snowflake\"\u003ECompanion Repo\u003C/a\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EPrefect Documentation\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ca href=\"https://docs.prefect.io/\"\u003Edocs.prefect.io\u003C/a\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESnowflake Cortex AI\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/llm-functions\"\u003ECortex AI Functions\u003C/a\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EDurable Execution\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ca href=\"https://docs.prefect.io/v3/develop/task-caching\"\u003ETask Caching\u003C/a\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EHuman-in-the-Loop\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ca href=\"https://docs.prefect.io/v3/advanced/interactive\"\u003EInteractive Workflows\u003C/a\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EPrefect Workers on SPCS\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowpark-container-services/overview\"\u003ESnowpark Container Services\u003C/a\u003E\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E"],"description":"","title":"Build Workflows That Think, Decide, and Act Across Systems",":type":"snowflake-site/components/contentfragment",":items":{},":itemsOrder":[],"elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\u003C!-- ------------------------ --\u003E\n## Overview\n\nEvery organization runs workflows that span multiple systems. A customer submits a loan application — it needs to be scored by an AI model, checked against compliance rules, routed to an underwriter if the amount is high enough, and the decision needs to land back in the CRM and notify the operations team. A supplier sends an invoice — it needs to be matched against purchase orders in the ERP, flagged for anomalies, approved by finance if the amount exceeds a threshold, and posted to the ledger. A patient's lab results come in — they need to be compared against clinical baselines, escalated to the care team if values are critical, and the chart needs to be updated across three systems before the next appointment.\n\nThese workflows are not static. The next step depends on what the AI model returns, what the data says, or what a human decides. They cross system boundaries — databases, SaaS platforms, notification channels, approval systems. They need to recover when something fails midway. And they need observability — not just \"did it run?\" but \"what did it decide, why, and who approved it?\"\n\nThis is the kind of workflow [Prefect](https://www.prefect.io/) is purpose-built for. Prefect is a Python-native orchestration platform that brings **durable execution**, **event-driven automation**, **human-in-the-loop approvals**, and **runtime decision-making** to any Python workflow. When paired with Snowflake and [Cortex AI](https://docs.snowflake.com/en/user-guide/snowflake-cortex/llm-functions), Prefect becomes the nervous system that coordinates intelligent, multi-system workflows — while Snowflake serves as the data platform and AI engine at the center.\n\n### What We'll Build\n\nIn this guide, we'll build an end-to-end **agentic PR review pipeline** in a matter of minutes — a practical example that demonstrates how Prefect and Snowflake work together for workflows that cross system boundaries and make decisions at runtime. The pipeline will:\n\n1. **React** to a pull request in GitHub\n2. **Ingest** the PR data into Snowflake\n3. **Classify risk and summarize changes** using Snowflake Cortex AI — the intelligence runs inside Snowflake, not an external LLM\n4. **Query historical context** from Snowflake to inform the decision\n5. **Branch at runtime** based on the AI assessment — low risk auto-approves, medium/high risk escalates\n6. **Notify** the team in Slack and **pause for human approval**\n7. **Write** the final assessment back to Snowflake\n8. **Merge or close** the PR on GitHub with a Cortex AI review comment\n9. **Send** a completion notice to Slack\n\nIf the pipeline fails at step 7, Prefect **resumes from step 7** — it doesn't re-run the Cortex AI calls or re-fetch from GitHub. That's durable execution.\n\n### The Architecture\n\n![Architecture](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-prefect-and-snowflake/architecture.png?v=07692dea)\n\n### Prerequisites\n\n- Snowflake account with Cortex AI access\n- Prefect Cloud account (free tier works)\n- GitHub account with a test repository\n- Slack workspace with a bot app\n- Python 3.9+\n\n\u003E **Detailed setup instructions** for Snowflake, Python, Slack, and GitHub are in the [companion repo](https://github.com/sfc-gh-aramasagaram/sfguide-getting-started-with-prefect-and-snowflake). This guide focuses on the pipeline itself.\n\n\u003C!-- ------------------------ --\u003E\n## Setup\n\nThe companion repo has full setup instructions. Here's the minimum to get running:\n\n**Snowflake** — Run this in a SQL Worksheet to create the database, tables, and seed data:\n\n```sql\nCREATE DATABASE IF NOT EXISTS PREFECT_QUICKSTART;\nCREATE SCHEMA IF NOT EXISTS PREFECT_QUICKSTART.RAW;\nCREATE SCHEMA IF NOT EXISTS PREFECT_QUICKSTART.ANALYTICS;\nCREATE WAREHOUSE IF NOT EXISTS PREFECT_WH WITH WAREHOUSE_SIZE='XSMALL' AUTO_SUSPEND=60 AUTO_RESUME=TRUE;\n\n-- Verify Cortex AI works in your region\nSELECT SNOWFLAKE.CORTEX.COMPLETE('mistral-large2', 'Say hello') AS test;\n```\n\n\u003E If the Cortex test fails, try llama3.1-70b or claude-3-5-sonnet — model availability varies by region. You can also enable cross-region inference: ALTER ACCOUNT SET CORTEX_ENABLED_CROSS_REGION = 'ANY_REGION';\n\n\u003E Full table DDL and seed data are in the repo's `setup.sql`.\n\n**Python** — Install dependencies and authenticate:\n\n```bash\nuv pip install prefect requests pandas \"snowflake-connector-python[pandas]\"\nprefect cloud login\n```\n\n\u003E `pause_flow_run` requires a connection to Prefect Cloud (or a self-hosted Prefect server). Make sure `prefect cloud login` completes before running the pipeline.\n\n**Secrets** — Set these environment variables (or use Prefect Blocks/Secrets):\n\n```bash\nexport GITHUB_TOKEN=your_github_pat\nexport SNOWFLAKE_CONNECTION_NAME=your_connection\nexport SLACK_BOT_TOKEN=xoxb-your-bot-token\nexport SLACK_CHANNEL=your-channel\n```\n\n\u003E See the [repo README](https://github.com/sfc-gh-aramasagaram/sfguide-getting-started-with-prefect-and-snowflake) for Snowflake connection configuration, Slack app setup, GitHub token scopes, and `uv` / `pyproject.toml` setup.\n\n\u003C!-- ------------------------ --\u003E\n## Fetch and Ingest\n\nCreate `pr_review_pipeline.py`. The pipeline starts by fetching PR data from GitHub and landing it in Snowflake.\n\n```python\n# retries with exponential backoff — handles GitHub rate limits automatically\n@task(retries=3, retry_delay_seconds=exponential_backoff(backoff_factor=2), log_prints=True)\ndef fetch_pr_data(repo: str, pr_number: int) -\u003E dict:\n    logger = get_run_logger()\n    headers = {\"Authorization\": f\"token {GITHUB_TOKEN}\"} if GITHUB_TOKEN else {}\n\n    # Fetch PR metadata (title, author, stats)\n    pr_resp = requests.get(f\"https://api.github.com/repos/{repo}/pulls/{pr_number}\", headers=headers)\n    pr_resp.raise_for_status()\n    pr = pr_resp.json()\n\n    # Fetch the raw diff separately (same endpoint, different Accept header)\n    diff_resp = requests.get(\n        f\"https://api.github.com/repos/{repo}/pulls/{pr_number}\",\n        headers={**headers, \"Accept\": \"application/vnd.github.v3.diff\"},\n    )\n\n    # Return a clean dict — truncate diff to 50k chars to stay within Cortex limits\n    return {\n        \"pr_number\": pr_number, \"repo\": repo,\n        \"title\": pr[\"title\"], \"author\": pr[\"user\"][\"login\"],\n        \"diff_content\": diff_resp.text[:50000],\n        \"files_changed\": pr[\"changed_files\"],\n        \"additions\": pr[\"additions\"], \"deletions\": pr[\"deletions\"],\n        \"created_at\": pr[\"created_at\"],\n    }\n```\n\nNote the **exponential backoff**: `exponential_backoff(backoff_factor=2)` generates delays of 2s, 4s, 8s, etc. If GitHub's API is rate-limited, Prefect retries automatically with increasing wait times.\n\n```python\n@task(retries=2, retry_delay_seconds=exponential_backoff(backoff_factor=2), log_prints=True)\ndef load_pr_to_snowflake(pr_data: dict) -\u003E int:\n    conn = get_connection()\n    # Build a single-row DataFrame matching the PULL_REQUESTS table schema\n    df = pd.DataFrame([{\n        \"PR_NUMBER\": pr_data[\"pr_number\"], \"REPO\": pr_data[\"repo\"],\n        \"TITLE\": pr_data[\"title\"], \"AUTHOR\": pr_data[\"author\"],\n        \"DIFF_CONTENT\": pr_data[\"diff_content\"],\n        \"FILES_CHANGED\": pr_data[\"files_changed\"],\n        \"ADDITIONS\": pr_data[\"additions\"], \"DELETIONS\": pr_data[\"deletions\"],\n        \"CREATED_AT\": pd.Timestamp(pr_data[\"created_at\"]),\n    }])\n\n    # write_pandas bulk-loads via PUT + COPY INTO — fast even for large diffs\n    write_pandas(conn=conn, df=df, table_name=\"PULL_REQUESTS\",\n                 database=\"PREFECT_QUICKSTART\", schema=\"RAW\")\n    return 1\n```\n\nEvery PR that enters this pipeline gets a permanent record in Snowflake.\n\n\u003C!-- ------------------------ --\u003E\n## Cortex AI Assessment\n\nThis is the most important step. Instead of calling an external LLM, the AI runs **inside Snowflake** — co-located with your data, governed by your access policies. We use **`CLASSIFY_TEXT`** for risk classification (returns a clean category — no prompt engineering or output parsing needed) and **`COMPLETE`** for the free-form summary.\n\n```python\n@task(retries=2, retry_delay_seconds=exponential_backoff(backoff_factor=2), log_prints=True)\ndef cortex_assess_pr(pr_data: dict) -\u003E dict:\n    logger = get_run_logger()\n    conn = get_connection()\n    # Truncate diff to 5k chars — balances context vs. token cost\n    diff_preview = pr_data[\"diff_content\"][:5000]\n\n    # Build a shared context string for both Cortex calls\n    pr_context = (\n        f\"PR Title: {pr_data['title']}\\n\"\n        f\"Files changed: {pr_data['files_changed']}, \"\n        f\"+{pr_data['additions']}/-{pr_data['deletions']}\\n\"\n        f\"Diff:\\n{diff_preview}\"\n    )\n\n    summarize_prompt = (\n        f\"Summarize this PR in 2-3 sentences for a technical reviewer.\\n\\n{pr_context}\"\n    )\n\n    cur = conn.cursor()\n    # CLASSIFY_TEXT: purpose-built for classification — returns one of the given categories\n    cur.execute(\n        \"SELECT SNOWFLAKE.CORTEX.CLASSIFY_TEXT(%s, ['low', 'medium', 'high'])\",\n        (pr_context,),\n    )\n    risk_level = cur.fetchone()[0].strip().lower()\n\n    # COMPLETE: free-form generation for the summary\n    cur.execute(\"SELECT SNOWFLAKE.CORTEX.COMPLETE(%s, %s)\", (\"mistral-large2\", summarize_prompt))\n    summary = cur.fetchone()[0].strip()\n    cur.close()\n\n    # Defensive fallback in case CLASSIFY_TEXT returns something unexpected\n    if risk_level not in (\"low\", \"medium\", \"high\"):\n        risk_level = \"medium\"\n\n    return {\"risk_level\": risk_level, \"summary\": summary}\n```\n\nTwo Cortex calls happen here — `CLASSIFY_TEXT` for structured classification, `COMPLETE` for free-form generation. Each costs credits on an XSMALL warehouse (expect ~0.01-0.02 credits per call). This is the **most expensive step** in the pipeline. If anything downstream fails, Prefect doesn't re-execute these calls on retry — that's durable execution.\n\n\u003E **Why two different Cortex functions?** Use the right tool for the job. `CLASSIFY_TEXT` guarantees a clean category from your list — no regex parsing, no prompt engineering for output format. `COMPLETE` is for open-ended generation where you need free-form text.\n\n\u003C!-- ------------------------ --\u003E\n## Decision and Human Gate\n\nBased on the Cortex AI classification and historical context, the pipeline **decides at runtime** what path to take.\n\n```python\n@task(retries=2, retry_delay_seconds=exponential_backoff(backoff_factor=2), log_prints=True)\ndef get_historical_context(pr_data: dict) -\u003E dict:\n    conn = get_connection()\n    cur = conn.cursor()\n    # Bind parameter for AUTHOR — safe against SQL injection\n    cur.execute(\n        \"SELECT COUNT(*) AS total_prs, \"\n        \"SUM(CASE WHEN HAD_INCIDENT THEN 1 ELSE 0 END) AS incidents \"\n        \"FROM PREFECT_QUICKSTART.ANALYTICS.PR_HISTORY \"\n        \"WHERE AUTHOR = %s\",\n        (pr_data[\"author\"],),\n    )\n    result = cur.fetchone()\n    cur.close()\n    return {\"total_prs\": result[0] or 0, \"incident_count\": result[1] or 0}\n\n\n@task(log_prints=True)\ndef make_decision(pr_data: dict, assessment: dict, history: dict) -\u003E dict:\n    risk = assessment[\"risk_level\"]\n\n    # Elevate risk if Cortex said \"medium\" but the author has caused incidents before\n    if risk == \"high\" or (risk == \"medium\" and history[\"incident_count\"] \u003E 0):\n        effective_risk = \"high\"\n    elif risk == \"medium\":\n        effective_risk = \"medium\"\n    else:\n        effective_risk = \"low\"\n\n    # This dict drives the runtime branching in the flow\n    return {\n        \"effective_risk\": effective_risk,\n        \"requires_approval\": effective_risk in (\"medium\", \"high\"),\n        \"auto_approved\": effective_risk == \"low\",\n    }\n```\n\nThis is **runtime branching** — the pipeline's next steps depend on values computed in this run, not a predetermined DAG. An author with prior incidents gets elevated risk, which triggers human approval.\n\nFor human-in-the-loop, define a type-safe input schema:\n\n```python\n# Type-safe input schema — Prefect renders this as a form in the Cloud UI\nclass ReviewDecision(RunInput):\n    approved: bool\n    reviewer_name: str\n    notes: str = \"\"\n```\n\nWhen the flow calls `pause_flow_run(wait_for_input=ReviewDecision, timeout=86400)`, execution literally **stops** and waits for a human to submit this form in Prefect Cloud (up to 24 hours). No custom webhook infrastructure. No polling loop.\n\n\u003C!-- ------------------------ --\u003E\n## Close the Loop\n\nAfter the decision, the pipeline closes the loop across all systems — Snowflake, GitHub, and Slack.\n\n```python\n@task(retries=2, retry_delay_seconds=exponential_backoff(backoff_factor=2), log_prints=True)\ndef close_github_pr(pr_data: dict, assessment: dict, decision: dict) -\u003E None:\n    headers = {\"Authorization\": f\"token {GITHUB_TOKEN}\"}\n    repo, pr_num = pr_data[\"repo\"], pr_data[\"pr_number\"]\n    approved = decision.get(\"final_approved\", False)\n\n    # Post the Cortex AI summary as a review comment on the PR\n    comment = (\n        f\"## {'✅' if approved else '❌'} Prefect PR Review\\n\\n\"\n        f\"**Risk:** {decision['effective_risk'].upper()}\\n\"\n        f\"**Reviewer:** {decision.get('reviewer', 'auto')}\\n\\n\"\n        f\"### Cortex AI Summary\\n{assessment['summary']}\\n\\n\"\n        f\"---\\n*Assessed by Prefect + Snowflake Cortex AI*\"\n    )\n    requests.post(f\"https://api.github.com/repos/{repo}/issues/{pr_num}/comments\",\n                  headers=headers, json={\"body\": comment})\n\n    # Merge (squash) if approved, close if rejected\n    if approved:\n        requests.put(f\"https://api.github.com/repos/{repo}/pulls/{pr_num}/merge\",\n                     headers=headers, json={\"merge_method\": \"squash\"})\n    else:\n        requests.patch(f\"https://api.github.com/repos/{repo}/pulls/{pr_num}\",\n                       headers=headers, json={\"state\": \"closed\"})\n```\n\nThe pipeline also sends **two Slack notifications** — one when approval is needed (with a deep-link to Prefect Cloud), and one when the pipeline completes with the final decision.\n\n![SS4 — Slack channel showing the approval request message with Review and Approve button](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-prefect-and-snowflake/SS4.png?v=07692dea)\n\n\u003C!-- ------------------------ --\u003E\n## Wiring It Together\n\nHere's where the agentic behavior comes together. One flow, four systems, runtime branching, and a human gate:\n\n```python\n@flow(name=\"pr-review-agent\", log_prints=True, persist_result=True)\ndef pr_review_agent(repo: str, pr_number: int):\n    logger = get_run_logger()\n\n    # Get the flow run ID so we can deep-link Slack notifications to Prefect Cloud\n    from prefect.runtime import flow_run\n    flow_run_id = str(flow_run.id)\n\n    # Open a single Snowflake connection for the entire flow — avoids repeated\n    # OAuth browser popups and is more efficient than connecting per-task.\n    conn = snowflake.connector.connect(connection_name=SF_CONNECTION)\n    # Tag all queries so you can find them in Snowflake's QUERY_HISTORY\n    conn.cursor().execute(\"ALTER SESSION SET QUERY_TAG = 'prefect-pr-review'\")\n\n    try:\n        # ---- Phase 1: Fetch and Ingest ----\n        # Pull PR metadata + diff from GitHub API\n        pr_data = fetch_pr_data(repo=repo, pr_number=pr_number)\n        # Land the raw data in Snowflake for auditability\n        load_pr_to_snowflake(pr_data)\n\n        # ---- Phase 2: AI Assessment ----\n        # Use Snowflake Cortex AI to classify risk and generate a summary\n        assessment = cortex_assess_pr(pr_data)\n        # Query Snowflake for this author's historical PR track record\n        history = get_historical_context(pr_data)\n        # Combine Cortex output + history into an effective risk decision\n        decision = make_decision(pr_data, assessment, history)\n\n        # ---- Phase 3: Human Gate (conditional) ----\n        # This is runtime branching — the path depends on what Cortex returned\n        if decision[\"requires_approval\"]:\n            # Send a rich Slack message with a \"Review & Approve\" button\n            slack_approval_request(pr_data, assessment, decision, flow_run_id)\n\n            # PAUSE the flow — execution literally stops here and waits\n            # for a human to submit the ReviewDecision form in Prefect Cloud.\n            # timeout=86400 means the flow will fail if no one responds in 24h.\n            review = pause_flow_run(\n                wait_for_input=ReviewDecision.with_initial_data(\n                    approved=False, reviewer_name=\"\",\n                ),\n                timeout=86400,\n            )\n\n            decision[\"final_approved\"] = review.approved\n            decision[\"reviewer\"] = review.reviewer_name\n\n            # Write the assessment BEFORE the early return — every decision\n            # (approved or rejected) gets an audit trail in Snowflake\n            write_assessment(pr_data, assessment, decision,\n                             reviewer=review.reviewer_name, reviewer_notes=review.notes)\n\n            if not review.approved:\n                # Rejected: close the PR on GitHub + notify Slack, then exit\n                close_github_pr(pr_data, assessment, decision)\n                slack_completion_notice(pr_data, assessment, decision)\n                return\n\n        else:\n            # Low risk — auto-approve without human intervention\n            decision[\"final_approved\"] = True\n            decision[\"reviewer\"] = \"auto\"\n            write_assessment(pr_data, assessment, decision)\n\n        # ---- Phase 4: Close the Loop ----\n        # Merge the PR on GitHub and post the Cortex AI review comment\n        close_github_pr(pr_data, assessment, decision)\n        # Send a final Slack notification with the outcome\n        slack_completion_notice(pr_data, assessment, decision)\n\n    finally:\n        # Always close the Snowflake connection, even if the flow fails\n        conn.close()\n\n\nif __name__ == \"__main__\":\n    pr_review_agent(repo=\"your-org/your-repo\", pr_number=1)\n```\n\n**What's happening:**\n\n- **Result persistence** — set `PREFECT_RESULTS_PERSIST_BY_DEFAULT=true` in your environment to persist all task results automatically. This enables durable execution: retries skip completed work. By default results persist to `~/.prefect/storage/` locally; for production, configure a remote result storage block (S3, GCS, or Azure Blob) so results survive across machines.\n- **`pause_flow_run(timeout=86400)`** — execution stops and waits up to 24 hours for human input\n- **`QUERY_TAG`** — tags all Snowflake queries for cost attribution in `QUERY_HISTORY`\n- **The `if` branch** — a Python `if` evaluated at runtime based on Cortex AI output\n- **`write_assessment` runs for both approved AND rejected PRs** — every decision gets an audit trail in Snowflake\n- **Module-level connection** — tasks call `get_connection()` internally rather than receiving the connection as a parameter. This keeps task signatures clean (only business data in/out), avoids serialization issues with caching, and follows idiomatic Prefect patterns.\n\n\u003E **Slack tasks and `write_assessment`** are defined in the companion repo. They follow the same pattern: tasks call `get_connection()` internally and use bind parameters for all Snowflake queries.\n\n\u003C!-- ------------------------ --\u003E\n## Run It\n\n```bash\nGITHUB_TOKEN=your_token \\\nSNOWFLAKE_CONNECTION_NAME=your_connection \\\nSLACK_BOT_TOKEN=xoxb-your-token \\\nSLACK_CHANNEL=your-channel \\\npython pr_review_pipeline.py\n```\n\nWatch the pipeline execute in real-time in Prefect Cloud. You'll see:\n\n1. Each task completing on the timeline\n2. The Cortex AI assessment results in the logs\n3. The Slack notification arrive\n4. The flow pause, waiting for your approval\n5. After you approve — the PR merges on GitHub, Slack gets the completion notice, and the assessment lands in Snowflake\n\n\u003E This runs the flow as a local Python process connected to Prefect Cloud. For production, deploy with `prefect deploy` and run your worker on [Snowpark Container Services](https://docs.snowflake.com/en/developer-guide/snowpark-container-services/overview) — your orchestration compute stays inside Snowflake's security perimeter. See the [companion repo](https://github.com/sfc-gh-aramasagaram/sfguide-getting-started-with-prefect-and-snowflake) for a `prefect.yaml` deployment example.\n\n![SS5 — Prefect Cloud flow run timeline showing all tasks completed in sequence](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-prefect-and-snowflake/SS5.png?v=07692dea)\n\n![SS6 — Slack channel showing the completion notice (APPROVED)](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-prefect-and-snowflake/SS6.png?v=07692dea)\n\n\u003C!-- ------------------------ --\u003E\n## Deploy It\n\nRunning `python pr_review_pipeline.py` is great for development. To operate this as a production workflow — with authentication, RBAC, scheduling, and event triggers — deploy to **Prefect Cloud**. You'll need a worker to execute your flows. Options include:\n\n- **Process worker** — runs on your local machine (simplest for testing)\n- **Prefect Managed Execution** — Prefect runs it for you (no infrastructure to manage)\n- **SPCS worker** — runs inside Snowpark Container Services (keeps compute in Snowflake's perimeter)\n\nCreate a `prefect.yaml` at the root of your project:\n\n```yaml\npull:\n  - prefect.deployments.steps.git_clone:\n      repository: \"{{ $GIT_REPO_URL }}\"\n      branch: main\n      access_token: \"{{ $GIT_ACCESS_TOKEN }}\"\n\ndeployments:\n  - name: pr-review-agent\n    entrypoint: pr_review_pipeline.py:pr_review_agent\n    work_pool:\n      name: spcs-pool    # Prefect Worker on Snowpark Container Services\n    tags: [github, code-review, cortex-ai]\n    parameters:\n      repo: your-org/your-repo\n      pr_number: 1\n    enforce_parameter_schema: true\n    description: \"Agentic PR review: GitHub → Cortex AI → Human approval → Merge\"\n```\n\nDeploy and run:\n\n```bash\n# Register the deployment with Prefect Cloud\nprefect deploy --all\n\n# Trigger it on-demand from the CLI\nprefect deployment run pr-review-agent/pr-review-agent \\\n  --param repo=your-org/your-repo \\\n  --param pr_number=42\n```\n\nFrom here you can add **Automations** in Prefect Cloud to trigger this deployment from a GitHub webhook — turning `python pr_review_pipeline.py` into a fully event-driven workflow.\n\n\u003E For production, we recommend **Prefect Cloud** (authentication, RBAC, audit logs, SSO) with a **Prefect Worker running on Snowpark Container Services (SPCS)** — keeping your orchestration compute inside Snowflake's security perimeter.\n\n\u003C!-- ------------------------ --\u003E\n## What Just Happened\n\nOnce your accounts are set up, the pipeline runs in under a minute:\n\n1. **Fetched** a PR from GitHub (external API)\n2. **Loaded** it into Snowflake (data persistence)\n3. **Ran Cortex AI** to classify risk and summarize changes (AI inside Snowflake)\n4. **Queried** author history for context (analytical query)\n5. **Decided at runtime** whether to auto-approve or escalate (dynamic branching)\n6. **Notified** your team in Slack with a rich message (external notification)\n7. **Paused** and waited for a human to approve (human-in-the-loop)\n8. **Wrote** the assessment to Snowflake (audit trail)\n9. **Merged** the PR on GitHub with a Cortex AI review comment (closing the loop)\n10. **Sent** a final Slack notification confirming the decision (completion notice)\n\nThat's **four systems** (GitHub, Snowflake, Slack, Prefect), **two Cortex AI calls**, a **human approval gate**, and **runtime decision-making** — all in one Python flow with full observability.\n\nIf the pipeline had failed at step 8, retrying would skip steps 1–5 entirely. Durable execution means you don't pay for expensive work twice.\n\n![SS7 — GitHub PR showing the Cortex AI review comment posted by the pipeline and Merged status](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-prefect-and-snowflake/SS7.png?v=07692dea)\n\n\u003C!-- ------------------------ --\u003E\n## Cleanup\n\n\u003E Skip this if you want to keep your PR assessment history in Snowflake.\n\n```sql\nDROP DATABASE IF EXISTS PREFECT_QUICKSTART;\nDROP WAREHOUSE IF EXISTS PREFECT_WH;\n```\n\n\u003C!-- ------------------------ --\u003E\n## Conclusion\n\nYou built a pipeline that no single platform can run alone. GitHub triggers the work. Snowflake stores the data and runs the AI. Prefect coordinates everything — with durability, human gates, and runtime decisions. Slack keeps the team in the loop. And the PR gets merged or closed automatically.\n\n### Now Imagine Your Workflows\n\nThe PR review pipeline is one example. But think about the workflows in your organization that span systems and require runtime decisions:\n\n- **Data quality incident response** — A freshness check fails in Snowflake. The pipeline queries downstream impact using Cortex AI, pages the on-call team in PagerDuty, pauses for a human to decide whether to halt dependent pipelines or proceed, then updates the data catalog and Slack channel with the resolution.\n\n- **ML model deployment with guardrails** — A new model version is trained. The pipeline runs validation against holdout data in Snowflake, uses Cortex AI to compare performance against the current production model, pauses for ML lead approval if performance regresses on any segment, then promotes to production and notifies stakeholders.\n\n- **Compliance-driven data access** — A user requests access to a sensitive dataset. The pipeline checks their role and clearance in Snowflake, uses Cortex AI to classify the sensitivity of the requested data, routes approval to the data steward if sensitivity is high, provisions access on approval, and logs the entire chain for audit.\n\n- **Multi-vendor ETL orchestration** — Data arrives from Salesforce, Stripe, and a partner SFTP. The pipeline validates each source, loads to Snowflake, uses Cortex AI to detect anomalies in the combined dataset, pauses for analyst review if anomalies exceed thresholds, then triggers dbt transformations and refreshes downstream dashboards.\n\nAny time your workflow crosses system boundaries, makes decisions at runtime, or needs a human in the loop — that's where Prefect and Snowflake work together.\n\nWe hope this guide gave you a glimpse of what's possible. **Snowflake powers the intelligence. 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