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private-equity distribution data. Start with\ndeterministic reconciliation checks, expose the evidence to a Cortex Agent, and\noptionally run a managed evaluation. Inspect the trace before claiming that the\nagent selected a skill or invoked a tool.\u003C/p\u003E\n","\u003Cp\u003EThis tutorial covers one reconciliation workflow, not a complete contractual\nwaterfall, investment recommendation, or production financial system. All amounts\nare synthetic. Balanced totals do not establish recipient entitlements.\u003C/p\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EFamiliarity with SQL, Python, and Snowflake notebooks.\u003C/li\u003E\u003Cli\u003EAbility to review proposed SQL, agent definitions, and evaluation configuration.\u003C/li\u003E\u003Cli\u003EAn administrator-approved environment for creating the demo objects.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003ESeparate deterministic financial checks from model-generated explanations.\u003C/li\u003E\u003Cli\u003EConfigure and verify a hash-checked execution bundle.\u003C/li\u003E\u003Cli\u003EReview and explicitly approve setup, skill upload, and a billable evaluation.\u003C/li\u003E\u003Cli\u003EInspect evaluation results and traces without confusing configuration with execution.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Build\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EFive synthetic reconciliation cases, including deliberately invalid cases.\u003C/li\u003E\u003Cli\u003EA direct Data Metric Function (DMF) check and a reconciliation review function.\u003C/li\u003E\u003Cli\u003EA Cortex Agent configured with a reconciliation skill.\u003C/li\u003E\u003Cli\u003EOptionally, a six-prompt managed evaluation and an evidence export.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Need\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EAccess to a Snowflake Workspace notebook and an approved existing STANDARD\ndatabase, role, and warehouse. Do not use a personal \u003Ccode\u003EUSER$\u003C/code\u003E database.\u003C/li\u003E\u003Cli\u003EPrivileges for the objects in the setup bundle. Ask your administrator to review\nthe SQL before running it; this tutorial does not automatically grant privileges.\u003C/li\u003E\u003Cli\u003EAccount support for the agent skill and managed evaluation features used in the\nbundle. Confirm availability and judge-model access before proceeding.\u003C/li\u003E\u003Cli\u003EPython 3.10 or later locally for bundle preparation, plus Git.\u003C/li\u003E\u003Cli\u003ENotebook packages: \u003Ccode\u003Esnowflake-snowpark-python\u003C/code\u003E, its connector dependency,\n\u003Ccode\u003Epandas\u003C/code\u003E, and \u003Ccode\u003Ematplotlib\u003C/code\u003E.\u003C/li\u003E\u003Cli\u003EApproval for warehouse, agent, judge, and storage charges before the optional\nevaluation. Time/token budgets are not hard credit caps.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EThe \u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/tree/master/site/sfguides/src/pe-agentic-system-cortex-code/assets/pe-agentic-system\"\u003Ecompanion project\u003C/a\u003E\ncontains the notebooks, SQL, Python, Rust, tests, and project-local Cortex Code\nskills under this guide's \u003Ccode\u003Eassets/pe-agentic-system/\u003C/code\u003E directory. Non-image assets\nare available from the Git repository, not from the published guide website.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/pe-agentic-system-cortex-code/finview_skills.png?v=dafeb4da\" alt=\"Project-local Cortex Code skills\"\u003E\u003C/p\u003E\n","\u003Ch2\u003EStep 1: Get the Code\u003C/h2\u003E\n","\u003Cp\u003EClone the Quickstarts repository and enter the companion project directory:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Egit clone https://github.com/Snowflake-Labs/sfquickstarts.git\ncd sfquickstarts/site/sfguides/src/pe-agentic-system-cortex-code/assets/pe-agentic-system\npython3 tools/verify_bundle.py\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe verifier should report 35 artifact hashes and the skill hash verified. A\nmatching hash proves file integrity relative to the manifest, not correctness or\nauthorization to execute the file.\u003C/p\u003E\n","\u003Cp\u003EReview these files before continuing:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/pe-agentic-system-cortex-code/assets/pe-agentic-system/finview_verification_demo.ipynb\"\u003ENotebook\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/tree/master/site/sfguides/src/pe-agentic-system-cortex-code/assets/pe-agentic-system/generated\"\u003EExecution bundle\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/pe-agentic-system-cortex-code/assets/pe-agentic-system/generated/01_setup.sql\"\u003ESetup SQL\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/pe-agentic-system-cortex-code/assets/pe-agentic-system/generated/manifest.json\"\u003EManifest\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EDo not use Run All.\u003C/strong\u003E The notebook contains multiple exploratory tracks outside\nthe tutorial path. Every approval flag starts as \u003Ccode\u003EFalse\u003C/code\u003E. Any cell that creates,\nreplaces, deletes, or bills raises an error until you set its \u003Ccode\u003EAPPROVE_*\u003C/code\u003E flag to\n\u003Ccode\u003ETrue\u003C/code\u003E in a separate cell. Use only the reconciliation block identified below, in a fresh\nkernel. Review each cell before executing it. Other cells are not prerequisites.\u003C/p\u003E\n","\u003Ch2\u003EStep 2: Configure the Bundle\u003C/h2\u003E\n","\u003Cp\u003EIn the companion project directory, replace the five placeholder values in the following\nscript with your approved targets and run it locally. Use simple uppercase,\nunquoted identifiers for database, schema, role, and warehouse. The schema must\nbe new for fresh setup; the database should already exist and be approved.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eimport json\nfrom pathlib import Path\n\nbundle = Path(&quot;generated&quot;)\nvalues = {\n    &quot;&lt;your account&gt;&quot;: &quot;YOUR_ACCOUNT&quot;,\n    &quot;&lt;your db&gt;&quot;: &quot;YOUR_DB&quot;,\n    &quot;&lt;your schema&gt;&quot;: &quot;YOUR_SCHEMA&quot;,\n    &quot;&lt;your role&gt;&quot;: &quot;YOUR_ROLE&quot;,\n    &quot;&lt;your warehouse&gt;&quot;: &quot;YOUR_WAREHOUSE&quot;,\n}\nif any(value.startswith(&quot;YOUR_&quot;) for value in values.values()):\n    raise ValueError(&quot;Replace every YOUR_* value with an approved target first.&quot;)\n\nmanifest_path = bundle / &quot;manifest.json&quot;\nmanifest = json.loads(manifest_path.read_text())\nfor name in manifest[&quot;files&quot;]:\n    artifact = (bundle / name).resolve()\n    if not artifact.is_relative_to(bundle.resolve()):\n        raise ValueError(f&quot;Artifact outside bundle: {name}&quot;)\n    text = artifact.read_text()\n    for placeholder, value in values.items():\n        text = text.replace(placeholder, value)\n    artifact.write_text(text)\n\ntext = json.dumps(manifest, indent=2)\nfor placeholder, value in values.items():\n    text = text.replace(placeholder, value)\nmanifest_path.write_text(text + &quot;\\n&quot;)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EReview the resulting changes, then refresh the integrity manifest:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Egit diff -- generated\npython3 tools/verify_bundle.py --refresh\npython3 tools/verify_bundle.py\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EDo not commit your account-specific bundle back to the public repository. Refresh\nhashes only after reviewing the edits; do not use it to bypass an unexplained\nhash mismatch.\u003C/p\u003E\n","\u003Ch2\u003EStep 3: Open the Notebook\u003C/h2\u003E\n","\u003Cp\u003EUpload \u003Ccode\u003Efinview_verification_demo.ipynb\u003C/code\u003E and the configured \u003Ccode\u003Egenerated/\u003C/code\u003E directory\nto the same folder in your Snowflake Workspace, preserving the bundle's nested\ndirectories. Open the notebook with the required packages and a fresh kernel.\u003C/p\u003E\n","\u003Cp\u003ELocate the reconciliation configuration cell containing both \u003Ccode\u003EBUNDLE_DIR\u003C/code\u003E and\n\u003Ccode\u003ESETUP_MODE\u003C/code\u003E (zero-based cell 28 in the companion version prepared for this guide).\nThe tutorial runs cells 28-38 only. Identify cells by their content as well as\ntheir position; cell numbers can change when you edit a notebook.\u003C/p\u003E\n","\u003Cp\u003EBefore running that configuration cell:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EChange \u003Ccode\u003EBUNDLE_DIR\u003C/code\u003E to \u003Ccode\u003EPath('generated')\u003C/code\u003E.\u003C/li\u003E\u003Cli\u003ESet \u003Ccode\u003EEXPECTED_ACCOUNT\u003C/code\u003E, \u003Ccode\u003EDATABASE\u003C/code\u003E, \u003Ccode\u003ESCHEMA\u003C/code\u003E, \u003Ccode\u003EROLE\u003C/code\u003E, and \u003Ccode\u003EWAREHOUSE\u003C/code\u003E to the\nsame approved values used in the manifest.\u003C/li\u003E\u003Cli\u003ESet \u003Ccode\u003ESETUP_MODE = 'fresh'\u003C/code\u003E for a new demo schema. The existing default,\n\u003Ccode\u003Ereuse_finview\u003C/code\u003E, expects previously created fixtures and is not a first-run mode.\u003C/li\u003E\u003Cli\u003EKeep \u003Ccode\u003EAPPROVE_SETUP\u003C/code\u003E, \u003Ccode\u003EAPPROVE_SKILL_UPLOAD_AND_AGENT\u003C/code\u003E,\n\u003Ccode\u003EAPPROVE_ONE_BILLABLE_EVALUATION\u003C/code\u003E, and \u003Ccode\u003EAPPROVE_CANCEL\u003C/code\u003E set to \u003Ccode\u003EFalse\u003C/code\u003E.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003ERun only this configuration cell. It verifies bundle hashes and account identity,\nthen explicitly selects the configured role and warehouse. Confirm the displayed\ncontext before proceeding. A connection alias is not an account identity.\u003C/p\u003E\n","\u003Ch2\u003EStep 4: Review and Set Up\u003C/h2\u003E\n","\u003Cp\u003ERun the next cell, beginning with \u003Ccode\u003Eexecute_file('00_preflight.sql')\u003C/code\u003E. It displays\nthe setup SQL, support SQL, agent and evaluation definitions, and runtime skill.\nConfirm the database is STANDARD and the proposed fresh schema does not exist.\nAsk your administrator to verify required privileges, including evaluation task\nprivileges and Cortex model access if you will use the optional evaluation.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/pe-agentic-system-cortex-code/finview_preflight.png?v=dafeb4da\" alt=\"Preflight checks\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThe approval flags start as \u003Ccode\u003EFalse\u003C/code\u003E. The first screenshot shows the gates before approval, the second after you set them in a separate cell:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/pe-agentic-system-cortex-code/finview_synthetic_gates.png?v=dafeb4da\" alt=\"Approval gates before setup\"\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/pe-agentic-system-cortex-code/finview_gates_run.png?v=dafeb4da\" alt=\"Approval gates after approval\"\u003E\u003C/p\u003E\n","\u003Cp\u003EAfter reviewing the exact statements, set \u003Ccode\u003EAPPROVE_SETUP = True\u003C/code\u003E in a separate\ncell and run the setup cell beginning with \u003Ccode\u003Eif not APPROVE_SETUP:\u003C/code\u003E once. The gate\nresets to \u003Ccode\u003EFalse\u003C/code\u003E before execution. Do not rerun the configuration cell to approve\nsetup; doing so resets the gates.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EFresh setup can create a database if it is absent.\u003C/strong\u003E Use an approved existing\ndatabase and verify its name first. Setup is not transactional. If any statement\nfails, inspect the partially created state; do not rerun blindly or execute the\nnotebook's separate deletion/redeployment cells.\u003C/p\u003E\n","\u003Ch2\u003EStep 5: Check the Evidence\u003C/h2\u003E\n","\u003Cp\u003ERun the cell that calls \u003Ccode\u003Eexecute_file('02_checks.sql')\u003C/code\u003E and checks\n\u003Ccode\u003Eexpected_status\u003C/code\u003E. It verifies the five synthetic case outcomes:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ccode\u003EBALANCED\u003C/code\u003E: \u003Ccode\u003Echecks_passed\u003C/code\u003E.\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003ESHORTFALL\u003C/code\u003E and \u003Ccode\u003EOVERALLOCATED\u003C/code\u003E: \u003Ccode\u003Eblocked_reconciliation\u003C/code\u003E.\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003EMISSING\u003C/code\u003E and \u003Ccode\u003EINCOMPLETE\u003C/code\u003E: \u003Ccode\u003Eblocked_incomplete\u003C/code\u003E.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EThe direct DMF call must return \u003Cstrong\u003E4 intentionally failed cases\u003C/strong\u003E. This is expected\nfor these fixtures; an acceptance rule requiring zero failures would reject them.\nDo not reinterpret missing allocations as zero. The shortfall case represents a\nsynthetic $63 million discrepancy, not a rerun of a production financial algorithm.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/pe-agentic-system-cortex-code/finview_dmf.png?v=dafeb4da\" alt=\"Data Metric Function results\"\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/pe-agentic-system-cortex-code/finview_waterfall_run.png?v=dafeb4da\" alt=\"Reconciliation results\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThe notebook also runs input checks on valuation assumptions and a Monte Carlo sensitivity on WACC. Both use synthetic inputs:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/pe-agentic-system-cortex-code/finview_valuation_checks.png?v=dafeb4da\" alt=\"Valuation input checks\"\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/pe-agentic-system-cortex-code/finview_mc_wacc.png?v=dafeb4da\" alt=\"Monte Carlo WACC sensitivity\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThe following plotting cell is optional. Stop before agent creation if the fixture\nstatus or DMF assertions fail. These direct queries do not establish that an agent\nor skill executed.\u003C/p\u003E\n","\u003Ch2\u003EStep 6: Create the Agent\u003C/h2\u003E\n","\u003Cp\u003EReview the\n\u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/pe-agentic-system-cortex-code/assets/pe-agentic-system/generated/agent.yaml\"\u003Eagent definition\u003C/a\u003E\nand\n\u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/pe-agentic-system-cortex-code/assets/pe-agentic-system/generated/skills/waterfall-reconciliation-review/SKILL.md\"\u003Eruntime skill\u003C/a\u003E.\nThe instruction-only reconciliation skill uses the \u003Ccode\u003Ereview_reconciliation\u003C/code\u003E tool.\u003C/p\u003E\n","\u003Cp\u003ESet \u003Ccode\u003EAPPROVE_SKILL_UPLOAD_AND_AGENT = True\u003C/code\u003E in a separate cell. Run the cell\nstarting with \u003Ccode\u003Eif not APPROVE_SKILL_UPLOAD_AND_AGENT:\u003C/code\u003E once. It resets the gate,\nuploads the skill and evaluation configuration, verifies downloaded stage bytes,\nand executes the agent-creation SQL. This creates configuration; it does not prove\nthat the agent invoked a skill.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/pe-agentic-system-cortex-code/finview_waterfall_review_agent.png?v=dafeb4da\" alt=\"Waterfall review agent\"\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/pe-agentic-system-cortex-code/finview_agents.png?v=dafeb4da\" alt=\"Created agents\"\u003E\u003C/p\u003E\n","\u003Cp\u003EAn example answer, in which the agent blocks the shortfall case and cites the reconciliation evidence:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/pe-agentic-system-cortex-code/finview_review_agent_reconciliation.png?v=dafeb4da\" alt=\"Review agent reconciliation output\"\u003E\u003C/p\u003E\n","\u003Cp\u003EIf runtime skills or the definition are unsupported, stop rather than silently\nomitting the skill. Hash-addressed stage paths aid reproducibility but do not\nenforce immutability.\u003C/p\u003E\n","\u003Ch2\u003EStep 7: Evaluate Optionally\u003C/h2\u003E\n","\u003Cp\u003ESkip this step if you do not have approval for billable execution. Review the\n\u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/pe-agentic-system-cortex-code/assets/pe-agentic-system/generated/evaluation.yaml\"\u003Eevaluation configuration\u003C/a\u003E\nand confirm the created agent version, judge availability, and six-row input\ndataset. The prompts include the five cases and an override request to approve\nthe known shortfall.\u003C/p\u003E\n","\u003Cp\u003ESet \u003Ccode\u003EAPPROVE_ONE_BILLABLE_EVALUATION = True\u003C/code\u003E in a separate cell, then run the\ncell starting with \u003Ccode\u003Eif not APPROVE_ONE_BILLABLE_EVALUATION:\u003C/code\u003E once. It resets the\ngate and records that a start was attempted. A failed start can still create\nevaluation state; inspect status before considering a retry.\u003C/p\u003E\n","\u003Cp\u003EThe next status/results cell may be rerun to check progress. Do not rerun START\nto poll for results. Review failed records rather than tuning and repeating the\nevaluation solely to obtain a favorable screenshot.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/pe-agentic-system-cortex-code/finview_agent_eval_dashboard.png?v=dafeb4da\" alt=\"Evaluation dashboard example\"\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/pe-agentic-system-cortex-code/finview_groundtruth.png?v=dafeb4da\" alt=\"Ground truth comparison example\"\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/pe-agentic-system-cortex-code/finview_agents_invoked.png?v=dafeb4da\" alt=\"Agents invoked during evaluation\"\u003E\u003C/p\u003E\n","\u003Cp\u003EScreenshots illustrate prior runs; your account, model availability, and outputs\nmay differ. A small synthetic evaluation is a smoke test, not evidence of\nproduction accuracy or calibrated financial risk.\u003C/p\u003E\n","\u003Ch2\u003EStep 8: Inspect Traces\u003C/h2\u003E\n","\u003Cp\u003ESelect a real \u003Ccode\u003ERECORD_ID\u003C/code\u003E from the evaluation output and set it in the next trace\ncell. Inspect the trace for skill selection/loading and the reconciliation tool\ninvocation with returned evidence. Report uncertainty if the trace does not\nestablish that the skill ran; agent configuration and aggregate scores are not\nsubstitutes for execution evidence.\u003C/p\u003E\n","\u003Cp\u003EYou can also open the agent's Evaluations tab in Snowsight, select the run and\nrecord, and inspect Trace details.\u003C/p\u003E\n","\u003Cp\u003ERun the evidence-export cell only if you intend to retain the results locally.\nReview exported rows and query identifiers before sharing them. If an evaluation\nis still active and you need to cancel it, review the final cancellation cell and\nexplicitly enable \u003Ccode\u003EAPPROVE_CANCEL\u003C/code\u003E. Cancellation does not delete demo objects.\nThis walkthrough does not attach scheduled DMFs. Obtain separate approval for\nobject cleanup; do not run the notebook's unrelated deletion cells.\u003C/p\u003E\n","\u003Ch2\u003EOptional Companion Examples\u003C/h2\u003E\n","\u003Cp\u003EThe repository also includes exploratory MCP, retrieval/judge, financial modeling,\nand Rust examples. They are not required for this tutorial and are not covered by\nthe reconciliation block's approvals or validation. Each track has its own\n\u003Ccode\u003EAPPROVE_*\u003C/code\u003E flags. Review their requirements, feature availability, and side\neffects independently. The screenshots below come from earlier runs.\u003C/p\u003E\n","\u003Ch3\u003EManaged MCP Tool\u003C/h3\u003E\n","\u003Cp\u003EThe notebook can expose the reconciliation as a managed MCP tool, call it for each\nsynthetic case, and export every request and response as evidence. A TruLens run\nthen scores those calls with a Cortex judge model.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/pe-agentic-system-cortex-code/finview_mcp_six_instrument_calls.png?v=dafeb4da\" alt=\"Six instrumented MCP calls\"\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/pe-agentic-system-cortex-code/finview_tool_call_results.png?v=dafeb4da\" alt=\"Tool call results\"\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/pe-agentic-system-cortex-code/finview_mcp_evidence.png?v=dafeb4da\" alt=\"MCP evidence export\"\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/pe-agentic-system-cortex-code/finview_mcp_eval.png?v=dafeb4da\" alt=\"MCP evaluation run\"\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/pe-agentic-system-cortex-code/finview_mcp_judgemetrics.png?v=dafeb4da\" alt=\"MCP judge metrics\"\u003E\u003C/p\u003E\n","\u003Ch3\u003ESEC Filing Agents\u003C/h3\u003E\n","\u003Cp\u003EA separate track builds semantic views over SEC filing data and two analyst agents\nfor diligence and capital-markets disclosure. Run its preflight cell before\ncreating anything.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/pe-agentic-system-cortex-code/finview_preflight_secagent.png?v=dafeb4da\" alt=\"SEC agent preflight\"\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/pe-agentic-system-cortex-code/finview_sv.png?v=dafeb4da\" alt=\"Semantic view\"\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/pe-agentic-system-cortex-code/finview_mvp_issuer_filingssv.png?v=dafeb4da\" alt=\"Issuer filings semantic view\"\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/pe-agentic-system-cortex-code/finview_issuer_filings_custom_instructions.png?v=dafeb4da\" alt=\"Issuer filings custom instructions\"\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/pe-agentic-system-cortex-code/finview_all_agents.png?v=dafeb4da\" alt=\"All agents in the schema\"\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/pe-agentic-system-cortex-code/finview_pe_dilligence_agent_plot.png?v=dafeb4da\" alt=\"PE diligence agent output\"\u003E\u003C/p\u003E\n","\u003Ch3\u003ERetrieval And Judge\u003C/h3\u003E\n","\u003Cp\u003EA standalone synthetic track reranks passages with ColBERT MaxSim, fuses rankings\nwith reciprocal rank fusion, and judges answers with \u003Ccode\u003EAI_COMPLETE\u003C/code\u003E and the\n\u003Ccode\u003Esnowflake-decision\u003C/code\u003E model. The model is in public preview. The judge scores two\nsynthetic cases with a Brier score. Treat it as a smoke test, not a calibration\nresult.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/pe-agentic-system-cortex-code/finview_colbert.png?v=dafeb4da\" alt=\"ColBERT reranking\"\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/pe-agentic-system-cortex-code/finview_rrf.png?v=dafeb4da\" alt=\"Reciprocal rank fusion\"\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/pe-agentic-system-cortex-code/finview_briersum.png?v=dafeb4da\" alt=\"Judge results with Brier score\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EMore Companion Files\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/pe-agentic-system-cortex-code/assets/pe-agentic-system/generated/managed_mcp_readme.md\"\u003EManaged MCP notes\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/tree/master/site/sfguides/src/pe-agentic-system-cortex-code/assets/pe-agentic-system/.cortex/skills\"\u003EProject-local Cortex Code skills\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/pe-agentic-system-cortex-code/assets/pe-agentic-system/readme.md\"\u003EReference implementation and limitations\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/pe-agentic-system-cortex-code/assets/pe-agentic-system/claims.md\"\u003EFinancial and performance claims\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/tree/master/site/sfguides/src/pe-agentic-system-cortex-code/assets/pe-agentic-system/integrations/rust\"\u003ERust implementation\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EDo not automatically execute model-generated code or treat illustrative DCF/LBO\nexamples as validated financial models.\u003C/p\u003E\n","\u003Ch2\u003ETroubleshooting\u003C/h2\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EBundle hash mismatch:\u003C/strong\u003E stop and compare the artifact to your reviewed copy.\nRefresh hashes only for intentional, reviewed edits.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EWrong account or context:\u003C/strong\u003E select the approved notebook account, then verify\nthe configured role and warehouse. Do not change configuration to conceal a mismatch.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESchema already exists:\u003C/strong\u003E inspect its state. Use \u003Ccode\u003Ereuse_finview\u003C/code\u003E only if the\nexisting fixtures match the notebook's checks; do not delete it to force a retry.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ENo evaluation rows yet:\u003C/strong\u003E rerun only the status/results cell, not START.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EUnsupported agent or judge feature:\u003C/strong\u003E stop and confirm availability with your\nadministrator. Do not weaken the verification steps to make the demo pass.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch2\u003EConclusion And Resources\u003C/h2\u003E\n","\u003Cp\u003EYou prepared a reviewed execution bundle, checked deliberately flawed synthetic\nfinancial data, and configured a reconciliation agent. If you completed the\noptional evaluation, you also inspected its outputs and trace evidence. Validate\nfinancial conclusions independently; this reference implementation is not\ninvestment advice.\u003C/p\u003E\n","\u003Ch3\u003EWhat You Learned\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EUse deterministic checks before accepting an agent's financial explanation.\u003C/li\u003E\u003Cli\u003EKeep integrity checks, human approval, and execution evidence separate.\u003C/li\u003E\u003Cli\u003ERun setup and optional evaluation deliberately, without blind retries.\u003C/li\u003E\u003Cli\u003EDistinguish a synthetic smoke test from production validation.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ERelated Resources\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/tree/master/site/sfguides/src/pe-agentic-system-cortex-code/assets/pe-agentic-system\"\u003ECompanion project\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/cortex-code/cortex-code\"\u003ECortex Code\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents-evaluations\"\u003ECortex Agent evaluations\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/data-quality-working\"\u003EData quality monitoring\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/guides-overview-ai-features\"\u003ESnowflake Cortex AI\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E"],"description":"","title":"PE Agentic System with Cortex Code","isDeveloperGuidesPage":false,":type":"snowflake-site/components/contentfragment",":items":{},":itemsOrder":[],"elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"## Overview\n\nBuild a review workflow for synthetic private-equity distribution data. Start with\ndeterministic reconciliation checks, expose the evidence to a Cortex Agent, and\noptionally run a managed evaluation. Inspect the trace before claiming that the\nagent selected a skill or invoked a tool.\n\nThis tutorial covers one reconciliation workflow, not a complete contractual\nwaterfall, investment recommendation, or production financial system. All amounts\nare synthetic. Balanced totals do not establish recipient entitlements.\n\n### Prerequisites\n\n- Familiarity with SQL, Python, and Snowflake notebooks.\n- Ability to review proposed SQL, agent definitions, and evaluation configuration.\n- An administrator-approved environment for creating the demo objects.\n\n### What You'll Learn\n\n- Separate deterministic financial checks from model-generated explanations.\n- Configure and verify a hash-checked execution bundle.\n- Review and explicitly approve setup, skill upload, and a billable evaluation.\n- Inspect evaluation results and traces without confusing configuration with execution.\n\n### What You'll Build\n\n- Five synthetic reconciliation cases, including deliberately invalid cases.\n- A direct Data Metric Function (DMF) check and a reconciliation review function.\n- A Cortex Agent configured with a reconciliation skill.\n- Optionally, a six-prompt managed evaluation and an evidence export.\n\n### What You'll Need\n\n- Access to a Snowflake Workspace notebook and an approved existing STANDARD\n  database, role, and warehouse. Do not use a personal `USER$` database.\n- Privileges for the objects in the setup bundle. Ask your administrator to review\n  the SQL before running it; this tutorial does not automatically grant privileges.\n- Account support for the agent skill and managed evaluation features used in the\n  bundle. Confirm availability and judge-model access before proceeding.\n- Python 3.10 or later locally for bundle preparation, plus Git.\n- Notebook packages: `snowflake-snowpark-python`, its connector dependency,\n  `pandas`, and `matplotlib`.\n- Approval for warehouse, agent, judge, and storage charges before the optional\n  evaluation. Time/token budgets are not hard credit caps.\n\nThe [companion project](https://github.com/Snowflake-Labs/sfquickstarts/tree/master/site/sfguides/src/pe-agentic-system-cortex-code/assets/pe-agentic-system)\ncontains the notebooks, SQL, Python, Rust, tests, and project-local Cortex Code\nskills under this guide's `assets/pe-agentic-system/` directory. Non-image assets\nare available from the Git repository, not from the published guide website.\n\n![Project-local Cortex Code skills](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/pe-agentic-system-cortex-code/finview_skills.png?v=dafeb4da)\n\n## Step 1: Get the Code\n\nClone the Quickstarts repository and enter the companion project directory:\n\n```bash\ngit clone https://github.com/Snowflake-Labs/sfquickstarts.git\ncd sfquickstarts/site/sfguides/src/pe-agentic-system-cortex-code/assets/pe-agentic-system\npython3 tools/verify_bundle.py\n```\n\nThe verifier should report 35 artifact hashes and the skill hash verified. A\nmatching hash proves file integrity relative to the manifest, not correctness or\nauthorization to execute the file.\n\nReview these files before continuing:\n\n- [Notebook](https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/pe-agentic-system-cortex-code/assets/pe-agentic-system/finview_verification_demo.ipynb)\n- [Execution bundle](https://github.com/Snowflake-Labs/sfquickstarts/tree/master/site/sfguides/src/pe-agentic-system-cortex-code/assets/pe-agentic-system/generated)\n- [Setup SQL](https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/pe-agentic-system-cortex-code/assets/pe-agentic-system/generated/01_setup.sql)\n- [Manifest](https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/pe-agentic-system-cortex-code/assets/pe-agentic-system/generated/manifest.json)\n\n**Do not use Run All.** The notebook contains multiple exploratory tracks outside\nthe tutorial path. Every approval flag starts as `False`. Any cell that creates,\nreplaces, deletes, or bills raises an error until you set its `APPROVE_*` flag to\n`True` in a separate cell. Use only the reconciliation block identified below, in a fresh\nkernel. Review each cell before executing it. Other cells are not prerequisites.\n\n## Step 2: Configure the Bundle\n\nIn the companion project directory, replace the five placeholder values in the following\nscript with your approved targets and run it locally. Use simple uppercase,\nunquoted identifiers for database, schema, role, and warehouse. The schema must\nbe new for fresh setup; the database should already exist and be approved.\n\n```python\nimport json\nfrom pathlib import Path\n\nbundle = Path(\"generated\")\nvalues = {\n    \"\u003Cyour account\u003E\": \"YOUR_ACCOUNT\",\n    \"\u003Cyour db\u003E\": \"YOUR_DB\",\n    \"\u003Cyour schema\u003E\": \"YOUR_SCHEMA\",\n    \"\u003Cyour role\u003E\": \"YOUR_ROLE\",\n    \"\u003Cyour warehouse\u003E\": \"YOUR_WAREHOUSE\",\n}\nif any(value.startswith(\"YOUR_\") for value in values.values()):\n    raise ValueError(\"Replace every YOUR_* value with an approved target first.\")\n\nmanifest_path = bundle / \"manifest.json\"\nmanifest = json.loads(manifest_path.read_text())\nfor name in manifest[\"files\"]:\n    artifact = (bundle / name).resolve()\n    if not artifact.is_relative_to(bundle.resolve()):\n        raise ValueError(f\"Artifact outside bundle: {name}\")\n    text = artifact.read_text()\n    for placeholder, value in values.items():\n        text = text.replace(placeholder, value)\n    artifact.write_text(text)\n\ntext = json.dumps(manifest, indent=2)\nfor placeholder, value in values.items():\n    text = text.replace(placeholder, value)\nmanifest_path.write_text(text + \"\\n\")\n```\n\nReview the resulting changes, then refresh the integrity manifest:\n\n```bash\ngit diff -- generated\npython3 tools/verify_bundle.py --refresh\npython3 tools/verify_bundle.py\n```\n\nDo not commit your account-specific bundle back to the public repository. Refresh\nhashes only after reviewing the edits; do not use it to bypass an unexplained\nhash mismatch.\n\n## Step 3: Open the Notebook\n\nUpload `finview_verification_demo.ipynb` and the configured `generated/` directory\nto the same folder in your Snowflake Workspace, preserving the bundle's nested\ndirectories. Open the notebook with the required packages and a fresh kernel.\n\nLocate the reconciliation configuration cell containing both `BUNDLE_DIR` and\n`SETUP_MODE` (zero-based cell 28 in the companion version prepared for this guide).\nThe tutorial runs cells 28-38 only. Identify cells by their content as well as\ntheir position; cell numbers can change when you edit a notebook.\n\nBefore running that configuration cell:\n\n1. Change `BUNDLE_DIR` to `Path('generated')`.\n2. Set `EXPECTED_ACCOUNT`, `DATABASE`, `SCHEMA`, `ROLE`, and `WAREHOUSE` to the\n   same approved values used in the manifest.\n3. Set `SETUP_MODE = 'fresh'` for a new demo schema. The existing default,\n   `reuse_finview`, expects previously created fixtures and is not a first-run mode.\n4. Keep `APPROVE_SETUP`, `APPROVE_SKILL_UPLOAD_AND_AGENT`,\n   `APPROVE_ONE_BILLABLE_EVALUATION`, and `APPROVE_CANCEL` set to `False`.\n\nRun only this configuration cell. It verifies bundle hashes and account identity,\nthen explicitly selects the configured role and warehouse. Confirm the displayed\ncontext before proceeding. A connection alias is not an account identity.\n\n## Step 4: Review and Set Up\n\nRun the next cell, beginning with `execute_file('00_preflight.sql')`. It displays\nthe setup SQL, support SQL, agent and evaluation definitions, and runtime skill.\nConfirm the database is STANDARD and the proposed fresh schema does not exist.\nAsk your administrator to verify required privileges, including evaluation task\nprivileges and Cortex model access if you will use the optional evaluation.\n\n![Preflight checks](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/pe-agentic-system-cortex-code/finview_preflight.png?v=dafeb4da)\n\nThe approval flags start as `False`. The first screenshot shows the gates before approval, the second after you set them in a separate cell:\n\n![Approval gates before setup](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/pe-agentic-system-cortex-code/finview_synthetic_gates.png?v=dafeb4da)\n![Approval gates after approval](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/pe-agentic-system-cortex-code/finview_gates_run.png?v=dafeb4da)\n\nAfter reviewing the exact statements, set `APPROVE_SETUP = True` in a separate\ncell and run the setup cell beginning with `if not APPROVE_SETUP:` once. The gate\nresets to `False` before execution. Do not rerun the configuration cell to approve\nsetup; doing so resets the gates.\n\n**Fresh setup can create a database if it is absent.** Use an approved existing\ndatabase and verify its name first. Setup is not transactional. If any statement\nfails, inspect the partially created state; do not rerun blindly or execute the\nnotebook's separate deletion/redeployment cells.\n\n## Step 5: Check the Evidence\n\nRun the cell that calls `execute_file('02_checks.sql')` and checks\n`expected_status`. It verifies the five synthetic case outcomes:\n\n- `BALANCED`: `checks_passed`.\n- `SHORTFALL` and `OVERALLOCATED`: `blocked_reconciliation`.\n- `MISSING` and `INCOMPLETE`: `blocked_incomplete`.\n\nThe direct DMF call must return **4 intentionally failed cases**. This is expected\nfor these fixtures; an acceptance rule requiring zero failures would reject them.\nDo not reinterpret missing allocations as zero. The shortfall case represents a\nsynthetic $63 million discrepancy, not a rerun of a production financial algorithm.\n\n![Data Metric Function results](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/pe-agentic-system-cortex-code/finview_dmf.png?v=dafeb4da)\n![Reconciliation results](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/pe-agentic-system-cortex-code/finview_waterfall_run.png?v=dafeb4da)\n\nThe notebook also runs input checks on valuation assumptions and a Monte Carlo sensitivity on WACC. Both use synthetic inputs:\n\n![Valuation input checks](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/pe-agentic-system-cortex-code/finview_valuation_checks.png?v=dafeb4da)\n![Monte Carlo WACC sensitivity](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/pe-agentic-system-cortex-code/finview_mc_wacc.png?v=dafeb4da)\n\nThe following plotting cell is optional. Stop before agent creation if the fixture\nstatus or DMF assertions fail. These direct queries do not establish that an agent\nor skill executed.\n\n## Step 6: Create the Agent\n\nReview the\n[agent definition](https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/pe-agentic-system-cortex-code/assets/pe-agentic-system/generated/agent.yaml)\nand\n[runtime skill](https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/pe-agentic-system-cortex-code/assets/pe-agentic-system/generated/skills/waterfall-reconciliation-review/SKILL.md).\nThe instruction-only reconciliation skill uses the `review_reconciliation` tool.\n\nSet `APPROVE_SKILL_UPLOAD_AND_AGENT = True` in a separate cell. Run the cell\nstarting with `if not APPROVE_SKILL_UPLOAD_AND_AGENT:` once. It resets the gate,\nuploads the skill and evaluation configuration, verifies downloaded stage bytes,\nand executes the agent-creation SQL. This creates configuration; it does not prove\nthat the agent invoked a skill.\n\n![Waterfall review agent](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/pe-agentic-system-cortex-code/finview_waterfall_review_agent.png?v=dafeb4da)\n![Created agents](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/pe-agentic-system-cortex-code/finview_agents.png?v=dafeb4da)\n\nAn example answer, in which the agent blocks the shortfall case and cites the reconciliation evidence:\n\n![Review agent reconciliation output](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/pe-agentic-system-cortex-code/finview_review_agent_reconciliation.png?v=dafeb4da)\n\nIf runtime skills or the definition are unsupported, stop rather than silently\nomitting the skill. Hash-addressed stage paths aid reproducibility but do not\nenforce immutability.\n\n## Step 7: Evaluate Optionally\n\nSkip this step if you do not have approval for billable execution. Review the\n[evaluation configuration](https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/pe-agentic-system-cortex-code/assets/pe-agentic-system/generated/evaluation.yaml)\nand confirm the created agent version, judge availability, and six-row input\ndataset. The prompts include the five cases and an override request to approve\nthe known shortfall.\n\nSet `APPROVE_ONE_BILLABLE_EVALUATION = True` in a separate cell, then run the\ncell starting with `if not APPROVE_ONE_BILLABLE_EVALUATION:` once. It resets the\ngate and records that a start was attempted. A failed start can still create\nevaluation state; inspect status before considering a retry.\n\nThe next status/results cell may be rerun to check progress. Do not rerun START\nto poll for results. Review failed records rather than tuning and repeating the\nevaluation solely to obtain a favorable screenshot.\n\n![Evaluation dashboard example](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/pe-agentic-system-cortex-code/finview_agent_eval_dashboard.png?v=dafeb4da)\n![Ground truth comparison example](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/pe-agentic-system-cortex-code/finview_groundtruth.png?v=dafeb4da)\n![Agents invoked during evaluation](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/pe-agentic-system-cortex-code/finview_agents_invoked.png?v=dafeb4da)\n\nScreenshots illustrate prior runs; your account, model availability, and outputs\nmay differ. A small synthetic evaluation is a smoke test, not evidence of\nproduction accuracy or calibrated financial risk.\n\n## Step 8: Inspect Traces\n\nSelect a real `RECORD_ID` from the evaluation output and set it in the next trace\ncell. Inspect the trace for skill selection/loading and the reconciliation tool\ninvocation with returned evidence. Report uncertainty if the trace does not\nestablish that the skill ran; agent configuration and aggregate scores are not\nsubstitutes for execution evidence.\n\nYou can also open the agent's Evaluations tab in Snowsight, select the run and\nrecord, and inspect Trace details.\n\nRun the evidence-export cell only if you intend to retain the results locally.\nReview exported rows and query identifiers before sharing them. If an evaluation\nis still active and you need to cancel it, review the final cancellation cell and\nexplicitly enable `APPROVE_CANCEL`. Cancellation does not delete demo objects.\nThis walkthrough does not attach scheduled DMFs. Obtain separate approval for\nobject cleanup; do not run the notebook's unrelated deletion cells.\n\n## Optional Companion Examples\n\nThe repository also includes exploratory MCP, retrieval/judge, financial modeling,\nand Rust examples. They are not required for this tutorial and are not covered by\nthe reconciliation block's approvals or validation. Each track has its own\n`APPROVE_*` flags. Review their requirements, feature availability, and side\neffects independently. The screenshots below come from earlier runs.\n\n### Managed MCP Tool\n\nThe notebook can expose the reconciliation as a managed MCP tool, call it for each\nsynthetic case, and export every request and response as evidence. A TruLens run\nthen scores those calls with a Cortex judge model.\n\n![Six instrumented MCP calls](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/pe-agentic-system-cortex-code/finview_mcp_six_instrument_calls.png?v=dafeb4da)\n![Tool call results](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/pe-agentic-system-cortex-code/finview_tool_call_results.png?v=dafeb4da)\n![MCP evidence export](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/pe-agentic-system-cortex-code/finview_mcp_evidence.png?v=dafeb4da)\n![MCP evaluation run](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/pe-agentic-system-cortex-code/finview_mcp_eval.png?v=dafeb4da)\n![MCP judge metrics](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/pe-agentic-system-cortex-code/finview_mcp_judgemetrics.png?v=dafeb4da)\n\n### SEC Filing Agents\n\nA separate track builds semantic views over SEC filing data and two analyst agents\nfor diligence and capital-markets disclosure. Run its preflight cell before\ncreating anything.\n\n![SEC agent preflight](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/pe-agentic-system-cortex-code/finview_preflight_secagent.png?v=dafeb4da)\n![Semantic view](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/pe-agentic-system-cortex-code/finview_sv.png?v=dafeb4da)\n![Issuer filings semantic view](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/pe-agentic-system-cortex-code/finview_mvp_issuer_filingssv.png?v=dafeb4da)\n![Issuer filings custom instructions](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/pe-agentic-system-cortex-code/finview_issuer_filings_custom_instructions.png?v=dafeb4da)\n![All agents in the schema](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/pe-agentic-system-cortex-code/finview_all_agents.png?v=dafeb4da)\n![PE diligence agent output](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/pe-agentic-system-cortex-code/finview_pe_dilligence_agent_plot.png?v=dafeb4da)\n\n### Retrieval And Judge\n\nA standalone synthetic track reranks passages with ColBERT MaxSim, fuses rankings\nwith reciprocal rank fusion, and judges answers with `AI_COMPLETE` and the\n`snowflake-decision` model. The model is in public preview. The judge scores two\nsynthetic cases with a Brier score. Treat it as a smoke test, not a calibration\nresult.\n\n![ColBERT reranking](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/pe-agentic-system-cortex-code/finview_colbert.png?v=dafeb4da)\n![Reciprocal rank fusion](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/pe-agentic-system-cortex-code/finview_rrf.png?v=dafeb4da)\n![Judge results with Brier score](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/pe-agentic-system-cortex-code/finview_briersum.png?v=dafeb4da)\n\n### More Companion Files\n\n- [Managed MCP notes](https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/pe-agentic-system-cortex-code/assets/pe-agentic-system/generated/managed_mcp_readme.md)\n- [Project-local Cortex Code skills](https://github.com/Snowflake-Labs/sfquickstarts/tree/master/site/sfguides/src/pe-agentic-system-cortex-code/assets/pe-agentic-system/.cortex/skills)\n- [Reference implementation and limitations](https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/pe-agentic-system-cortex-code/assets/pe-agentic-system/readme.md)\n- [Financial and performance claims](https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/pe-agentic-system-cortex-code/assets/pe-agentic-system/claims.md)\n- [Rust implementation](https://github.com/Snowflake-Labs/sfquickstarts/tree/master/site/sfguides/src/pe-agentic-system-cortex-code/assets/pe-agentic-system/integrations/rust)\n\nDo not automatically execute model-generated code or treat illustrative DCF/LBO\nexamples as validated financial models.\n\n## Troubleshooting\n\n- **Bundle hash mismatch:** stop and compare the artifact to your reviewed copy.\n  Refresh hashes only for intentional, reviewed edits.\n- **Wrong account or context:** select the approved notebook account, then verify\n  the configured role and warehouse. Do not change configuration to conceal a mismatch.\n- **Schema already exists:** inspect its state. Use `reuse_finview` only if the\n  existing fixtures match the notebook's checks; do not delete it to force a retry.\n- **No evaluation rows yet:** rerun only the status/results cell, not START.\n- **Unsupported agent or judge feature:** stop and confirm availability with your\n  administrator. Do not weaken the verification steps to make the demo pass.\n\n## Conclusion And Resources\n\nYou prepared a reviewed execution bundle, checked deliberately flawed synthetic\nfinancial data, and configured a reconciliation agent. If you completed the\noptional evaluation, you also inspected its outputs and trace evidence. Validate\nfinancial conclusions independently; this reference implementation is not\ninvestment advice.\n\n### What You Learned\n\n- Use deterministic checks before accepting an agent's financial explanation.\n- Keep integrity checks, human approval, and execution evidence separate.\n- Run setup and optional evaluation deliberately, without blind retries.\n- Distinguish a synthetic smoke test from production validation.\n\n### Related Resources\n\n- [Companion project](https://github.com/Snowflake-Labs/sfquickstarts/tree/master/site/sfguides/src/pe-agentic-system-cortex-code/assets/pe-agentic-system)\n- [Cortex Code](https://docs.snowflake.com/en/user-guide/cortex-code/cortex-code)\n- [Cortex Agent evaluations](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents-evaluations)\n- [Data quality monitoring](https://docs.snowflake.com/en/user-guide/data-quality-working)\n- [Snowflake Cortex AI](https://docs.snowflake.com/en/guides-overview-ai-features)","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-58f76013c2","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-184fe8cd41",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-43a197c7eb","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2026-10-28",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-c546c3bd74","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. It may be out of date with current Snowflake 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