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dataset, pinned versions and a gate that blocks a bad release before your customers see it.\u003C/p\u003E\n","\u003Cp\u003EYou'll evaluate a lending operations agent for \u003Cstrong\u003ENorthbank\u003C/strong\u003E, a fictional UK consumer lender, and compare three versions of it. Along the way you'll see why answer correctness alone isn't enough:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003E8 of 40\u003C/strong\u003E correct answers from the baseline took the wrong path through the tools.\u003C/li\u003E\u003Cli\u003ERicher instructions fixed four targeted failures and \u003Cstrong\u003Ebroke retrieval on two tiers\u003C/strong\u003E, while answer correctness moved by only 0.024.\u003C/li\u003E\u003Cli\u003EBetween identical runs, the averages moved by at most 0.03, but \u003Cstrong\u003E11 of 40\u003C/strong\u003E questions changed which tools the agent called.\u003C/li\u003E\u003Cli\u003EOne full evaluation run costs about \u003Cstrong\u003E$12\u003C/strong\u003E at list price, and one metric accounts for 65% of the judge spend.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EThis guide focuses on testing an agent for a regulated use case. For the basics of the evaluation framework, see \u003Ca href=\"https://www.snowflake.com/en/developers/guides/getting-started-with-cortex-agent-evaluations/\"\u003EGetting Started with Cortex Agent Evaluations\u003C/a\u003E and \u003Ca href=\"https://www.snowflake.com/en/developers/guides/best-practices-for-evaluating-cortex-agents/\"\u003EBest Practices for Evaluating Cortex Agents\u003C/a\u003E.\u003C/p\u003E\n","\u003Cp\u003EAll data in this guide is synthetic.\u003C/p\u003E\n","\u003Ch3\u003EWhat You'll Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to build an evaluation dataset that makes your agent work for its score\u003C/li\u003E\u003Cli\u003EHow to score the path the agent took, not only its final answer\u003C/li\u003E\u003Cli\u003EHow to compare agent versions and spot a regression the average hides\u003C/li\u003E\u003Cli\u003EWhy you should pin both the agent version and the judge version\u003C/li\u003E\u003Cli\u003EHow to write custom metrics in your regulator's terms and check that their judge is useful\u003C/li\u003E\u003Cli\u003EHow to set gate thresholds from measured variance and automate the gate with Snowflake Tasks\u003C/li\u003E\u003Cli\u003EWhat one evaluation run costs, and where the money goes\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Build\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA synthetic loan book, seven policy PDFs and 2,724 customer contact notes, with traps built in\u003C/li\u003E\u003Cli\u003EA Cortex Agent, \u003Ccode\u003ELENDING_OPS_AGENT\u003C/code\u003E, with four tools and three committed versions\u003C/li\u003E\u003Cli\u003EA 40-question evaluation dataset whose expected answers were computed in SQL\u003C/li\u003E\u003Cli\u003ETwo custom metrics: \u003Ccode\u003Econsumer_duty_compliance\u003C/code\u003E and \u003Ccode\u003Epolicy_grounding\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EA release gate made of two stored procedures and two Snowflake Tasks\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Need\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA Snowflake account and a role that can create a warehouse and a database. A sandbox account with \u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E is simplest.\u003C/li\u003E\u003Cli\u003EAccess to Cortex Agents and Cortex AI functions. If a model isn't available in your region, enable \u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cross-region-inference\"\u003Ecross-region inference\u003C/a\u003E.\u003C/li\u003E\u003Cli\u003EA budget of about $1 for the first evaluation run and $10–12 for each full run, at list price.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EFamiliarity with SQL and Snowsight worksheets\u003C/li\u003E\u003Cli\u003EA basic understanding of \u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents\"\u003ECortex Agents\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EWhy Lending Needs Evaluations\u003C/h2\u003E\n","\u003Cp\u003EA Cortex Agent answers a question by planning, calling tools (a semantic view through Cortex Analyst, a Cortex Search service, a stored procedure) and writing a response. Two answers can read equally well while one of them used the wrong date filter, counted duplicate applications or ignored a policy that was replaced last month.\u003C/p\u003E\n","\u003Cp\u003EIn consumer lending that difference matters. Under the FCA Consumer Duty, a lender must avoid foreseeable harm and treat customers in vulnerable circumstances fairly. An answer that recommends a default notice for a customer who has just told you they're in hospital isn't a quality issue: it's a conduct issue.\u003C/p\u003E\n","\u003Cp\u003ECortex Agent evaluations run a dataset of questions through a specific agent version, score each answer with built-in and custom metrics, and keep every trace for inspection in Snowsight. This guide uses:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ECapability\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EWhy it matters here\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ETool selection accuracy (TSA) and tool execution accuracy (TEA)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EScore the path the agent took. TSA is deterministic: it uses no LLM judge.\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Eagent_version\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EEvaluate an exact committed version, not whatever is live today.\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EMetric versions (\u003Ccode\u003Ev3_0\u003C/code\u003E)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EEach version fixes the judge model, prompt and rubric.\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Emodel\u003C/code\u003E key on custom metrics\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EChoose the judge for your own metrics.\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESet Up the Environment\u003C/h2\u003E\n","\u003Cp\u003EDownload \u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/raw/master/site/sfguides/src/evaluate-and-gate-cortex-agents-for-lending/assets/northbank-agent-evals-quickstart.zip\"\u003Enorthbank-agent-evals-quickstart.zip\u003C/a\u003E and unzip it. It contains the SQL scripts, the evaluation configs, the policy PDFs and the evaluation dataset.\u003C/p\u003E\n","\u003Cp\u003EEvery script runs in a Snowsight SQL worksheet. Open each one, select \u003Cstrong\u003ERun All\u003C/strong\u003E unless a step says otherwise, and run them in order.\u003C/p\u003E\n","\u003Ch3\u003ECreate the Objects\u003C/h3\u003E\n","\u003Cp\u003ERun \u003Ccode\u003Esql/00_setup.sql\u003C/code\u003E. It creates:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003Ean X-Small warehouse, \u003Ccode\u003ENORTHBANK_EVAL_WH\u003C/code\u003E;\u003C/li\u003E\u003Cli\u003Ea database, \u003Ccode\u003ENORTHBANK_AGENT_EVALS\u003C/code\u003E, with two schemas: \u003Ccode\u003EDEMO\u003C/code\u003E for everything the agent can see, and \u003Ccode\u003EGROUND_TRUTH\u003C/code\u003E for evaluation-only labels the agent can never reach;\u003C/li\u003E\u003Cli\u003Ethree stages for the files.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EUpload the Files\u003C/h3\u003E\n","\u003Cp\u003EIn Snowsight, go to \u003Cstrong\u003EIngestion\u003C/strong\u003E » \u003Cstrong\u003EAdd Data\u003C/strong\u003E » \u003Cstrong\u003ELoad files into a Stage\u003C/strong\u003E, and upload into the root of each stage:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EFiles from the zip\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EStage\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Edata/policy_pdfs/*.pdf\u003C/code\u003E (7 files)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003ENORTHBANK_AGENT_EVALS.DEMO.POLICY_STAGE\u003C/code\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Edata/eval_questions.ndjson\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003ENORTHBANK_AGENT_EVALS.DEMO.EVAL_DATA_STAGE\u003C/code\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Eeval_configs/*.yaml\u003C/code\u003E (5 files)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003ENORTHBANK_AGENT_EVALS.DEMO.EVAL_CONFIG_STAGE\u003C/code\u003E\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003EThen re-run the four statements at the end of \u003Ccode\u003E00_setup.sql\u003C/code\u003E to check that every file is in place.\u003C/p\u003E\n","\u003Ch3\u003EBuild the Data and Tools\u003C/h3\u003E\n","\u003Cp\u003ERun these three scripts in order:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\u003Ccode\u003Esql/01_synthetic_data.sql\u003C/code\u003E builds the loan book and the customer notes.\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003Esql/02_documents_and_search.sql\u003C/code\u003E parses the PDFs with \u003Ccode\u003EAI_PARSE_DOCUMENT\u003C/code\u003E, chunks them by heading with version and status metadata, and creates two Cortex Search services.\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003Esql/03_semantic_view.sql\u003C/code\u003E creates the semantic view behind the \u003Ccode\u003Eloan_book\u003C/code\u003E tool.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EThe data generator is deterministic, and the expected answers depend on it. The last query in \u003Ccode\u003E01\u003C/code\u003E should return:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ETable\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ERows\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EAPPLICATIONS\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E20,311 (20,001 unique)\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003ELOANS\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E8,089\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EARREARS_CASES\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E177\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EFORBEARANCE\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E47\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003ECUSTOMER_NOTES\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E2,724\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide negative\nIf your counts differ, don't edit the expected answers to match. Re-run \u003Ccode\u003E01_synthetic_data.sql\u003C/code\u003E unchanged.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECreate Three Agent Versions\u003C/h2\u003E\n","\u003Cp\u003ENorthbank's credit operations and collections teams use an agent with four tools:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003Eloan_book\u003C/strong\u003E: Cortex Analyst over a semantic view of applications, decisions, loans, arrears and forbearance\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003Ecredit_policy\u003C/strong\u003E: Cortex Search over the seven parsed policy PDFs\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003Ecustomer_notes\u003C/strong\u003E: Cortex Search over the contact-centre notes, filtered by customer\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003Eaffordability_check\u003C/strong\u003E: a SQL stored procedure that returns a debt-to-income ratio and a pass or fail\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/evaluate-and-gate-cortex-agents-for-lending/agent-tools.png?v=96090e3b\" alt=\"The agent's four tools in Snowsight\"\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide positive\nThe screenshots in this guide come from the original build, so run names differ from the ones the scripts create: for example \u003Ccode\u003Ev1_baseline_v3_r1\u003C/code\u003E instead of \u003Ccode\u003Ev1_r1\u003C/code\u003E, and \u003Ccode\u003Ev3_v3_r1\u003C/code\u003E instead of \u003Ccode\u003Ev3_r1\u003C/code\u003E.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003ERun \u003Ccode\u003Esql/04_agent_versions.sql\u003C/code\u003E. It creates the custom tool and three committed versions of the agent, changing one thing at a time:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EVERSION$1\u003C/strong\u003E: model A, one-line routing instructions (the baseline)\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EVERSION$2\u003C/strong\u003E: model A, rich instructions targeting the baseline's failures\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EVERSION$3\u003C/strong\u003E: VERSION$2's instructions on model B\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Ccode\u003ECREATE AGENT ... FROM SPECIFICATION\u003C/code\u003E commits the first specification as VERSION$1. Each later version is created by changing the live specification and committing it:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EALTER AGENT LENDING_OPS_AGENT MODIFY LIVE VERSION SET SPECIFICATION = $$ ... $$;\nALTER AGENT LENDING_OPS_AGENT COMMIT COMMENT = 'VERSION$2: rich instructions, model A';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/evaluate-and-gate-cortex-agents-for-lending/agent-versions.png?v=96090e3b\" alt=\"Each version is a committed, immutable snapshot of the agent specification\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThe script ends with \u003Ccode\u003ESHOW VERSIONS\u003C/code\u003E. Note that VERSION$3 is now the default: committing a version moves the default to it. You'll come back to that in the step on pinning.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide positive\nThe agent specifications are in \u003Ccode\u003Eagent_specs/\u003C/code\u003E so you can diff them. Replace the model names with models available in your account, but keep VERSION$1 and VERSION$2 on the same model so only the instructions change.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EBuild a Dataset with Traps\u003C/h2\u003E\n","\u003Cp\u003EAn evaluation only measures what its dataset makes difficult. So before writing a single question, the traps went into the data, modelled on how lending data really fails:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EDuplicate submissions.\u003C/strong\u003E 20,311 application records, 310 of them flagged as same-day duplicates. "How many applications?" has two plausible answers and only one is right.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EA superseded policy.\u003C/strong\u003E Credit Policy v1.0 (minimum score 620) was replaced by v2.0 (640) on 1 March 2026, and both are in the search index.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EVulnerable customers.\u003C/strong\u003E 8% of customers have a vulnerability indicator in their notes, and 40% of those are subtle ("off work since the operation"). Some sit on a system-proposed default notice that the collections procedure forbids.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EDistractors.\u003C/strong\u003E Notes that use the right words about the wrong person ("her neighbour passed away; customer herself is fine").\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EA vintage problem.\u003C/strong\u003E The 2025-Q3 personal loan vintage has an ever-in-arrears rate of 7.2%, against 2.2–2.5% for the other 2025 vintages. That's why the cut-off was raised.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EThe evaluation set has 40 questions across core metrics, cross-tool reasoning, policy change, affordability, data quality, vulnerability (with non-vulnerable controls) and refusals. Every expected answer was computed in SQL against the source tables, never by asking the agent. Here's one row:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-json\"\u003E{\n  "QUESTION_ID": "Q01",\n  "TIER": "core",\n  "INPUT_QUERY": "How many unique credit applications did Northbank receive between 1 January 2025 and 30 June 2026, excluding duplicate submissions?",\n  "GROUND_TRUTH": {\n    "ground_truth_output": "20,001 unique applications. The table holds 20,311 records, of which 310 are flagged as duplicate submissions. An answer of 20,311 counts duplicates and is incorrect.",\n    "ground_truth_invocations": [\n      {\n        "tool_name": "loan_book",\n        "tool_input": "count of unique applications excluding duplicate submissions, Jan 2025 to Jun 2026",\n        "tool_output": "SQL: SELECT COUNT_IF(NOT IS_DUPLICATE_SUBMISSION) FROM APPLICATIONS_V\\nExpected Result: 20001"\n      }\n    ]\n  }\n}\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis row contains:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003Ethe question a colleague would ask;\u003C/li\u003E\u003Cli\u003Ethe expected answer, including the wrong answer the judge should reject;\u003C/li\u003E\u003Cli\u003Ethe tools the agent should call, which TSA and TEA score against.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EFor refusal questions, such as "Forecast the Bank of England base rate", \u003Ccode\u003Eground_truth_invocations\u003C/code\u003E is an empty list: calling any tool is wrong. A row without invocations silently drops out of TSA and TEA, so put them on every row.\u003C/p\u003E\n","\u003Cp\u003ERun \u003Ccode\u003Esql/05_eval_dataset.sql\u003C/code\u003E. It loads the file into a table and registers two datasets with \u003Ccode\u003ESYSTEM$CREATE_EVALUATION_DATASET\u003C/code\u003E: the full 40 questions, and a 5-question probe for cheap experiments.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EScore the Path\u003C/h2\u003E\n","\u003Cp\u003EAn evaluation is described in a small YAML file. Here's \u003Ccode\u003Eeval_configs/version3.yaml\u003C/code\u003E, without the custom metrics:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-yaml\"\u003Eevaluation:\n  agent_params:\n    agent_name: NORTHBANK_AGENT_EVALS.DEMO.LENDING_OPS_AGENT\n    agent_type: CORTEX AGENT\n    agent_version: VERSION$3\n  source_metadata:\n    type: dataset\n    dataset_name: NORTHBANK_AGENT_EVALS.DEMO.NORTHBANK_EVAL\nmetrics:\n- name: answer_correctness\n  version: v3_0\n- name: logical_consistency\n  version: v3_0\n- name: tool_selection_accuracy\n  version: v3_0\n- name: tool_execution_accuracy\n  version: v3_0\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EIt names the exact agent version, the dataset and each system metric pinned to a metric version.\u003C/p\u003E\n","\u003Cp\u003EOpen \u003Ccode\u003Esql/06_run_evaluations.sql\u003C/code\u003E and run it \u003Cstrong\u003Estatement by statement\u003C/strong\u003E, not with Run All, which would start every run at once. Start with the probe:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECALL EXECUTE_AI_EVALUATION('START', OBJECT_CONSTRUCT('run_name', 'probe_v1_r1'),\n  '@NORTHBANK_AGENT_EVALS.DEMO.EVAL_CONFIG_STAGE/probe_version1.yaml');\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ESnowflake invokes the agent once per question, records the trace and computes each metric. Check progress with the \u003Ccode\u003ESTATUS\u003C/code\u003E call in the script, or in Snowsight under \u003Cstrong\u003EAI & ML\u003C/strong\u003E » \u003Cstrong\u003EAgents\u003C/strong\u003E » \u003Cstrong\u003ELENDING_OPS_AGENT\u003C/strong\u003E » \u003Cstrong\u003EEvaluations\u003C/strong\u003E. Then start one full run per version (\u003Ccode\u003Ev1_r1\u003C/code\u003E, \u003Ccode\u003Ev2_r1\u003C/code\u003E, \u003Ccode\u003Ev3_r1\u003C/code\u003E).\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/evaluate-and-gate-cortex-agents-for-lending/evaluations-tab.png?v=96090e3b\" alt=\"The Evaluations tab lists every run with its status and metric averages\"\u003E\u003C/p\u003E\n","\u003Cp\u003EIn the original build, the baseline (VERSION$1) scored 0.92 on answer correctness. On that number alone you'd ship it. Tool selection was 0.84, and the gap is where the useful findings were:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003E8 of 40 answers were correct but took the wrong path.\u003C/strong\u003E Three called the same tool two or three times to confirm a result it already had. Asked to forecast the base rate, it searched the credit policy before declining, so TSA scored 0.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EOne answer was plausible and wrong.\u003C/strong\u003E Asked for unique declines between 1 April and 30 June 2026, the agent filtered on decision date rather than received date, then used \u003Ccode\u003ECOUNT(DISTINCT application_id)\u003C/code\u003E, which can't remove duplicates because each duplicate has its own ID. It reported 1,844; the truth is 1,826. Answer correctness (0.33) caught the number, and TEA (0.55) showed where the SQL went wrong.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003ETSA is computed as matched / max(expected, actual), so missing tools, wrong tools and redundant calls all cost points. Because it's deterministic, a change in TSA between runs means the agent behaved differently, not that a judge changed its mind.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/evaluate-and-gate-cortex-agents-for-lending/record-details.png?v=96090e3b\" alt=\"Each record shows the full trace, the SQL the agent generated and the judge's reasoning\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThe queries in \u003Ccode\u003E06\u003C/code\u003E find these cases for you: correct answers whose tool selection wasn't perfect, and the judge's explanation for every low score.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECompare Versions\u003C/h2\u003E\n","\u003Cp\u003EVERSION$2 kept model A and rewrote the instructions to fix the baseline's failures: deduplicate by default, use the received date, one call per tool, no tools for out-of-scope questions, plus the vulnerability procedure and refusal rules.\u003C/p\u003E\n","\u003Cp\u003EIt fixed what it aimed at. The declined question went from 0.33 to 1.0, and the cross-tool and base-rate questions reached TSA 1.0. Overall, though, it got worse:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EVersion\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EAnswer correctness\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ELogical consistency\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ETool selection\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ETool execution\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EVERSION$1 (model A, thin)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E0.92\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E0.98\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E0.84\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E0.88\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EVERSION$2 (model A, rich)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E0.89\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E0.95\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E0.82\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E0.81\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EVERSION$3 (model B, rich)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E0.96\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E0.93\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E0.90\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E0.88\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003EThe traces showed why. With the procedure written into its instructions, the agent stopped retrieving it. On vulnerability cases it read the notes but skipped the case record and the procedure document; on two refusals it called no tool at all. The answers still sounded right, so answer correctness moved only 0.024. TSA on the refusal tier fell to 0.33 and on the vulnerability tier to 0.55. In a regulated firm, an answer that cites no retrieved procedure isn't auditable, even when it happens to be correct.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/evaluate-and-gate-cortex-agents-for-lending/compare-versions.png?v=96090e3b\" alt=\"Compare runs with Hide unchanged metrics shows only the questions that moved\"\u003E\u003C/p\u003E\n","\u003Cp\u003ETo compare your own runs, select two or three runs in the Evaluations tab, select \u003Cstrong\u003ECompare\u003C/strong\u003E and turn on \u003Cstrong\u003EHide unchanged metrics\u003C/strong\u003E. The by-tier query in \u003Ccode\u003E06\u003C/code\u003E shows the same regression in SQL: averages hide a regression that's confined to one tier.\u003C/p\u003E\n","\u003Cp\u003EVERSION$3 kept VERSION$2's instructions and changed only the model. It's the best of the three.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EPin Agent and Judge\u003C/h2\u003E\n","\u003Cp\u003ETwo things can change under you between runs: the agent and the judge.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EThe agent.\u003C/strong\u003E Committing VERSION$3 made it the agent's default automatically. An evaluation targeting the default would have switched silently from model A to model B. In CI and scheduled runs, always target a committed \u003Ccode\u003EVERSION$N\u003C/code\u003E, use \u003Ccode\u003ELIVE\u003C/code\u003E only while iterating, and treat a change of default as a release event.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EThe judge.\u003C/strong\u003E Metric versions fix the judge model, prompt and rubric. Three experiments on the same agent version showed what happens when you don't pin carefully:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EA retired judge fails after you've paid.\u003C/strong\u003E Pinning \u003Ccode\u003Ev1_0\u003C/code\u003E, whose judge model had reached end of life, was accepted at start. The agent ran every question, then metric computation failed with "metric_version v1 is no longer supported". No scores, and the agent invocations were already billed.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003E\u003Ccode\u003Eauto\u003C/code\u003E has already moved.\u003C/strong\u003E An unpinned run resolved to the v3 judge. If your thresholds were calibrated on an earlier version, they now measure something else.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EContext limits score zero.\u003C/strong\u003E With the v2 judge, logical consistency hit the judge's input limit on the 8 longest traces. Those rows were scored 0, not excluded, pulling the average to 0.758. On the 32 traces it could read it gave 0.948; the v3 judge read all 40 and gave 0.992.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EPin \u003Ccode\u003Ev3_0\u003C/code\u003E for every system metric, keep it fixed across runs (scores from different versions aren't comparable), and upgrade deliberately when a deprecation is announced. Also make sure the judge isn't your agent's orchestration model; otherwise the agent is grading itself.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EWrite Custom Metrics\u003C/h2\u003E\n","\u003Cp\u003EThe built-in metrics don't know what the Consumer Duty requires. Custom metrics let you write the rubric yourself and choose the judge with the \u003Ccode\u003Emodel\u003C/code\u003E key. The full-run configs add two, each scored 0, 5 or 10:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003Econsumer_duty_compliance\u003C/strong\u003E: did the answer act correctly on vulnerability, refuse protected characteristics, give guidance rather than advice, and avoid foreseeable harm?\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003Epolicy_grounding\u003C/strong\u003E: is every policy statement supported by a current document the agent actually retrieved?\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EHere's an extract of the second one:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-yaml\"\u003E- name: policy_grounding\n  model: <judge model>\n  score_ranges:\n    min_score: [0, 4]\n    median_score: [4, 7]\n    max_score: [7, 10]\n  prompt: |\n    Score whether every Northbank policy statement in the agent's answer is\n    grounded in a CURRENT policy document that the agent actually retrieved.\n    Question: {{input}}\n    Agent answer: {{output}}\n    Tool calls and results in this trace: {{tool_info}}\n    ...\n    Score 0 if any statement relies on a superseded document without scoping,\n    or contradicts the retrieved documents.\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe \u003Ccode\u003E{{tool_info}}\u003C/code\u003E placeholder passes the retrieved chunks to the judge, so it can check grounding against what the agent saw rather than what it remembered.\u003C/p\u003E\n","\u003Cp\u003EBoth metrics were scored with two judges on identical traces (\u003Ccode\u003Eeval_configs/judge_comparison.yaml\u003C/code\u003E repeats the experiment). They agreed on 37 of 40 rows for each metric, which looks reassuring. But one judge gave consumer duty 10/10 on every row, so it never flagged anything. The other marked down answers that blocked a default notice but left out the Specialist Support referral. On policy grounding the roles reversed: the lenient judge scored one answer 0 for broadening the rule (the procedure requires a vulnerability check before a Collections referral or default notice; the answer said "before any collections action"), while the stricter one gave it 10.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/evaluate-and-gate-cortex-agents-for-lending/custom-metric-explanation.png?v=96090e3b\" alt=\"Read the low-scoring explanations before you trust a custom metric\"\u003E\u003C/p\u003E\n","\u003Cp\u003EBefore trusting a custom metric, check that its scores spread across the range and read the explanations. Agreement between two judges proves nothing if one of them scores everything 10, and neither judge is uniformly the stricter one, so calibrate each metric on its own.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EMeasure Variance and Gate\u003C/h2\u003E\n","\u003Cp\u003EEach run re-invokes the agent, so a pinned agent and a pinned judge still produce new traces. Run VERSION$3 twice more with the variance runs in \u003Ccode\u003E06\u003C/code\u003E (\u003Ccode\u003Ev3_r2\u003C/code\u003E, \u003Ccode\u003Ev3_r3\u003C/code\u003E). In the original build, the three runs looked like this:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EMetric\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ERun 1\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ERun 2\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ERun 3\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ERange\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAnswer correctness\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E0.95\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E0.96\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E0.97\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E0.02\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ETool selection\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E0.89\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E0.91\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E0.92\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E0.03\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ETool execution\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E0.88\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E0.89\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E0.88\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E0.01\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ELogical consistency\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E0.93\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E0.94\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E0.92\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E0.02\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003EThe averages are stable, but individual questions aren't: between identical runs, 11 of 40 questions changed their TSA score, meaning the agent changed which tools it called. These are the questions to fix first, and they're invisible in the average. The last query in \u003Ccode\u003E06\u003C/code\u003E lists them.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/evaluate-and-gate-cortex-agents-for-lending/variance-runs.png?v=96090e3b\" alt=\"Metric trends across repeated runs of the same version\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EBuild the Gate\u003C/h3\u003E\n","\u003Cp\u003ERun \u003Ccode\u003Esql/07_ci_gate.sql\u003C/code\u003E (Run All is safe). It creates:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ccode\u003EEVAL_GATE_THRESHOLDS\u003C/code\u003E: one floor per metric. Each floor sits just below the lowest run, by roughly one run-to-run range. For harm it uses a per-row rule instead of an average: the gate fails if any single answer scores 0 on consumer duty.\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003ESTART_GATED_EVAL\u003C/code\u003E: starts a run from a pinned config with a timestamped run name.\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003ECHECK_GATED_EVALS\u003C/code\u003E: polls run status and, once a run completes, writes pass or fail with reasons. It fails closed: a run it can't read, a missing metric or a failed run never passes.\u003C/li\u003E\u003Cli\u003ETwo Tasks, both created \u003Cstrong\u003Esuspended\u003C/strong\u003E: \u003Ccode\u003ENIGHTLY_AGENT_EVAL\u003C/code\u003E at 02:00 Europe/London, and \u003Ccode\u003ECHECK_AGENT_EVAL_GATE\u003C/code\u003E every 15 minutes.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EOnce your runs from \u003Ccode\u003E06\u003C/code\u003E have completed, run \u003Ccode\u003Esql/07b_gate_your_runs.sql\u003C/code\u003E statement by statement. Its first query suggests floors from your own three runs; the rest scores your runs through the gate:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT RUN_NAME, AGENT_VERSION, ROUND(ANSWER_CORRECTNESS, 3) AS AC, ROUND(TOOL_SELECTION, 3) AS TSA,\n       CONSUMER_DUTY_MIN, GATE_PASSED, GATE_REASONS\nFROM EVAL_GATE_RESULTS ORDER BY STARTED_AT DESC;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/evaluate-and-gate-cortex-agents-for-lending/gate-results.png?v=96090e3b\" alt=\"VERSION$2 is blocked with explicit reasons; every VERSION$3 run passes\"\u003E\u003C/p\u003E\n","\u003Cp\u003EIn the original build, VERSION$2 failed with "answer_correctness 0.893 < 0.925", plus the tool execution and tool selection floors, and all three VERSION$3 runs passed. A fourth VERSION$3 run, started by the Task itself, also passed.\u003C/p\u003E\n","\u003Cp\u003EThe per-row rule earns its place. In a later rerun of VERSION$3, every average cleared its floor (answer correctness 0.942, tool selection 0.902), yet the gate blocked the run with \u003Ccode\u003Econsumer_duty_min 0 < 5\u003C/code\u003E. On question 30, a control case, the agent read the neighbour's bereavement as the customer's own and advised against a default notice that policy requires. Answer correctness, consumer duty and policy grounding all scored it 0. Your runs may or may not hit this: that's the variance you just measured.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide negative\nEach scheduled run costs about $10–12 at list price. Resume the Tasks only when you want nightly runs: \u003Ccode\u003EALTER TASK CHECK_AGENT_EVAL_GATE RESUME;\u003C/code\u003E then \u003Ccode\u003EALTER TASK NIGHTLY_AGENT_EVAL RESUME;\u003C/code\u003E.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EUnderstand the Cost\u003C/h2\u003E\n","\u003Cp\u003EJudge usage is attributable per run and metric by joining each evaluation span's \u003Ccode\u003Einference_request_ids\u003C/code\u003E to \u003Ccode\u003ECORTEX_REST_API_USAGE_HISTORY\u003C/code\u003E, which records input and output tokens but no credits, so you apply the list rate yourself. Agent invocation credits sit in \u003Ccode\u003ECORTEX_AGENT_USAGE_HISTORY\u003C/code\u003E. Run \u003Ccode\u003Esql/08_cost.sql\u003C/code\u003E for your own runs (ACCOUNT_USAGE views can lag by about 45 minutes).\u003C/p\u003E\n","\u003Cp\u003EHere's one 40-question VERSION$3 run from the original build, priced at on-demand list for an Enterprise account on AWS EU Dublin:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EComponent\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EUsage\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ECredits\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EUSD\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ELogical consistency judge\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E1.64M input + 28k output tokens\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E2.66 AI\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E$5.33\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECustom metrics (two)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E411k input + 58k output tokens\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E1.05 AI\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E$2.10\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ETool execution judge\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E77k input + 17k output tokens\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E0.24 AI\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E$0.48\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAnswer correctness judge\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E31k input + 9k output tokens\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E0.12 AI\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E$0.23\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ETool selection\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003Eno judge\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E0\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E$0.00\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAgent invocation (40 questions)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003ECORTEX_AGENT_USAGE_HISTORY\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E1.29–1.63 AI\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E$2.58–3.26\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EWarehouse (evaluation tasks)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ELarge Gen2 warehouse\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E0.16–0.28 Platform\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E$0.63–1.07\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003ETotal for one run\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E \u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E \u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003Eabout $12 ($0.30 per question)\u003C/strong\u003E\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003EHow it's priced (Snowflake Service Consumption Table):\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EAI Credits.\u003C/strong\u003E Cortex AI features, including Cortex Agents and the evaluation judges, bill in AI Credits. These cost the same in every edition and region: $2.00 with global cross-region routing and $2.20 with regional routing.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EJudge tokens.\u003C/strong\u003E These use the judge model's Cortex Inference rate: 1.50 AI Credits per million input tokens and 7.50 per million output tokens.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EPlatform Credits.\u003C/strong\u003E Only the warehouse that runs the evaluation tasks bills in Platform Credits, at $3.90 for Enterprise on AWS EU Dublin. The quickstart uses an X-Small warehouse, which is smaller than the original Large Gen2.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003ECapacity contracts lower these prices.\u003C/p\u003E\n","\u003Cp\u003ELogical consistency sends the whole trace to its judge, so it takes 73% of judge tokens and 65% of judge spend, about 23 times the cost of answer correctness. Leaving it out of nightly runs and keeping it for release runs takes a run from about $12 to about $6.70. Tool selection adds no judge cost: once you've paid to invoke the agent, it's free to include on every run.\u003C/p\u003E\n","\u003Ch3\u003ELimits\u003C/h3\u003E\n","\u003Cp\u003EEvaluations don't pass session attributes, so an agent relying on row access policies keyed on session variables won't score what users see. MCP tools aren't called during an evaluation, and files written by the code execution tool aren't kept. Cover these with other tests.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EClean Up\u003C/h2\u003E\n","\u003Cp\u003ERun \u003Ccode\u003Esql/99_teardown.sql\u003C/code\u003E to suspend the Tasks and drop the database and warehouse. The evaluation runs and their results are stored with the agent and are removed with it.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion And Resources\u003C/h2\u003E\n","\u003Ch3\u003EWhat You Learned\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EScore the path.\u003C/strong\u003E 8 of 40 correct answers took the wrong path, and one plausible number was wrong.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EChange one thing per version.\u003C/strong\u003E Richer instructions fixed four problems and broke retrieval on two tiers.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EPin the agent version.\u003C/strong\u003E Committing a version changed the default model silently.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EPin the judge.\u003C/strong\u003E A retired judge failed after spend, \u003Ccode\u003Eauto\u003C/code\u003E had already moved, and the v2 judge scored 8 long traces 0.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ETest custom judges.\u003C/strong\u003E One judge scored consumer duty 10/10 on every row.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EGate on measured variance, and fail closed.\u003C/strong\u003E VERSION$2 was blocked with reasons, and the per-row harm rule caught a run whose averages all passed.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EWatch the cost.\u003C/strong\u003E Logical consistency was 73% of judge tokens.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EApply It to Your Own Agent\u003C/h3\u003E\n","\u003Cp\u003EMost of the work is in the dataset: expected answers computed independently, traps built from how your data really fails, and expected tool calls on every row. With that in place, the platform does the rest. From here, you can:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EStart with 20–40 questions from real user queries, and add a question every time you find a failure in production. \u003Ccode\u003Etemplates/eval_dataset_template.sql\u003C/code\u003E shows the three row types every dataset needs.\u003C/li\u003E\u003Cli\u003EAdd one custom metric for the rule that matters most in your domain, and calibrate its judge before trusting it. \u003Ccode\u003Etemplates/eval_config_template.yaml\u003C/code\u003E has a rubric skeleton.\u003C/li\u003E\u003Cli\u003ERun your dataset three times on the current version to set thresholds, then schedule the gate.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Ccode\u003Edocs/adapt-to-your-use-case.md\u003C/code\u003E in the project maps each step of this guide to your own agent, with a checklist.\u003C/p\u003E\n","\u003Ch3\u003ERelated Resources\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents-evaluations\"\u003ECortex Agent evaluations documentation\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents\"\u003ECortex Agents documentation\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/developers/guides/getting-started-with-cortex-agent-evaluations/\"\u003EGetting Started with Cortex Agent Evaluations\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/developers/guides/best-practices-for-evaluating-cortex-agents/\"\u003EBest Practices for Evaluating Cortex Agents\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/raw/master/site/sfguides/src/evaluate-and-gate-cortex-agents-for-lending/assets/northbank-agent-evals-quickstart.zip\"\u003EProject files (zip)\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E"],"description":"","title":"Evaluate and Gate a Lending Agent with Cortex Agent Evaluations",":type":"snowflake-site/components/contentfragment",":items":{},":itemsOrder":[],"elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\u003C!-- ------------------------ --\u003E\n## Overview\n\nWhether you're building agents for a bank, a lender or any regulated business, this guide shows how to test a Cortex Agent like any other production system: with a ground-truth dataset, pinned versions and a gate that blocks a bad release before your customers see it.\n\nYou'll evaluate a lending operations agent for **Northbank**, a fictional UK consumer lender, and compare three versions of it. Along the way you'll see why answer correctness alone isn't enough:\n\n- **8 of 40** correct answers from the baseline took the wrong path through the tools.\n- Richer instructions fixed four targeted failures and **broke retrieval on two tiers**, while answer correctness moved by only 0.024.\n- Between identical runs, the averages moved by at most 0.03, but **11 of 40** questions changed which tools the agent called.\n- One full evaluation run costs about **$12** at list price, and one metric accounts for 65% of the judge spend.\n\nThis guide focuses on testing an agent for a regulated use case. For the basics of the evaluation framework, see [Getting Started with Cortex Agent Evaluations](https://www.snowflake.com/en/developers/guides/getting-started-with-cortex-agent-evaluations/) and [Best Practices for Evaluating Cortex Agents](https://www.snowflake.com/en/developers/guides/best-practices-for-evaluating-cortex-agents/).\n\nAll data in this guide is synthetic.\n\n### What You'll Learn\n\n- How to build an evaluation dataset that makes your agent work for its score\n- How to score the path the agent took, not only its final answer\n- How to compare agent versions and spot a regression the average hides\n- Why you should pin both the agent version and the judge version\n- How to write custom metrics in your regulator's terms and check that their judge is useful\n- How to set gate thresholds from measured variance and automate the gate with Snowflake Tasks\n- What one evaluation run costs, and where the money goes\n\n### What You'll Build\n\n- A synthetic loan book, seven policy PDFs and 2,724 customer contact notes, with traps built in\n- A Cortex Agent, `LENDING_OPS_AGENT`, with four tools and three committed versions\n- A 40-question evaluation dataset whose expected answers were computed in SQL\n- Two custom metrics: `consumer_duty_compliance` and `policy_grounding`\n- A release gate made of two stored procedures and two Snowflake Tasks\n\n### What You'll Need\n\n- A Snowflake account and a role that can create a warehouse and a database. A sandbox account with `ACCOUNTADMIN` is simplest.\n- Access to Cortex Agents and Cortex AI functions. If a model isn't available in your region, enable [cross-region inference](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cross-region-inference).\n- A budget of about $1 for the first evaluation run and $10–12 for each full run, at list price.\n\n### Prerequisites\n\n- Familiarity with SQL and Snowsight worksheets\n- A basic understanding of [Cortex Agents](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents)\n\n\u003C!-- ------------------------ --\u003E\n## Why Lending Needs Evaluations\n\nA Cortex Agent answers a question by planning, calling tools (a semantic view through Cortex Analyst, a Cortex Search service, a stored procedure) and writing a response. Two answers can read equally well while one of them used the wrong date filter, counted duplicate applications or ignored a policy that was replaced last month.\n\nIn consumer lending that difference matters. Under the FCA Consumer Duty, a lender must avoid foreseeable harm and treat customers in vulnerable circumstances fairly. An answer that recommends a default notice for a customer who has just told you they're in hospital isn't a quality issue: it's a conduct issue.\n\nCortex Agent evaluations run a dataset of questions through a specific agent version, score each answer with built-in and custom metrics, and keep every trace for inspection in Snowsight. This guide uses:\n\n| Capability | Why it matters here |\n|---|---|\n| Tool selection accuracy (TSA) and tool execution accuracy (TEA) | Score the path the agent took. TSA is deterministic: it uses no LLM judge. |\n| `agent_version` | Evaluate an exact committed version, not whatever is live today. |\n| Metric versions (`v3_0`) | Each version fixes the judge model, prompt and rubric. |\n| `model` key on custom metrics | Choose the judge for your own metrics. |\n\n\u003C!-- ------------------------ --\u003E\n## Set Up the Environment\n\nDownload [northbank-agent-evals-quickstart.zip](https://github.com/Snowflake-Labs/sfquickstarts/raw/master/site/sfguides/src/evaluate-and-gate-cortex-agents-for-lending/assets/northbank-agent-evals-quickstart.zip) and unzip it. It contains the SQL scripts, the evaluation configs, the policy PDFs and the evaluation dataset.\n\nEvery script runs in a Snowsight SQL worksheet. Open each one, select **Run All** unless a step says otherwise, and run them in order.\n\n### Create the Objects\n\nRun `sql/00_setup.sql`. It creates:\n\n- an X-Small warehouse, `NORTHBANK_EVAL_WH`;\n- a database, `NORTHBANK_AGENT_EVALS`, with two schemas: `DEMO` for everything the agent can see, and `GROUND_TRUTH` for evaluation-only labels the agent can never reach;\n- three stages for the files.\n\n### Upload the Files\n\nIn Snowsight, go to **Ingestion** » **Add Data** » **Load files into a Stage**, and upload into the root of each stage:\n\n| Files from the zip | Stage |\n|---|---|\n| `data/policy_pdfs/*.pdf` (7 files) | `NORTHBANK_AGENT_EVALS.DEMO.POLICY_STAGE` |\n| `data/eval_questions.ndjson` | `NORTHBANK_AGENT_EVALS.DEMO.EVAL_DATA_STAGE` |\n| `eval_configs/*.yaml` (5 files) | `NORTHBANK_AGENT_EVALS.DEMO.EVAL_CONFIG_STAGE` |\n\nThen re-run the four statements at the end of `00_setup.sql` to check that every file is in place.\n\n### Build the Data and Tools\n\nRun these three scripts in order:\n\n1. `sql/01_synthetic_data.sql` builds the loan book and the customer notes.\n2. `sql/02_documents_and_search.sql` parses the PDFs with `AI_PARSE_DOCUMENT`, chunks them by heading with version and status metadata, and creates two Cortex Search services.\n3. `sql/03_semantic_view.sql` creates the semantic view behind the `loan_book` tool.\n\nThe data generator is deterministic, and the expected answers depend on it. The last query in `01` should return:\n\n| Table | Rows |\n|---|---|\n| `APPLICATIONS` | 20,311 (20,001 unique) |\n| `LOANS` | 8,089 |\n| `ARREARS_CASES` | 177 |\n| `FORBEARANCE` | 47 |\n| `CUSTOMER_NOTES` | 2,724 |\n\n\u003E aside negative\n\u003E If your counts differ, don't edit the expected answers to match. Re-run `01_synthetic_data.sql` unchanged.\n\n\u003C!-- ------------------------ --\u003E\n## Create Three Agent Versions\n\nNorthbank's credit operations and collections teams use an agent with four tools:\n\n- **loan_book**: Cortex Analyst over a semantic view of applications, decisions, loans, arrears and forbearance\n- **credit_policy**: Cortex Search over the seven parsed policy PDFs\n- **customer_notes**: Cortex Search over the contact-centre notes, filtered by customer\n- **affordability_check**: a SQL stored procedure that returns a debt-to-income ratio and a pass or fail\n\n![The agent's four tools in Snowsight](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/evaluate-and-gate-cortex-agents-for-lending/agent-tools.png?v=96090e3b)\n\n\u003E aside positive\n\u003E The screenshots in this guide come from the original build, so run names differ from the ones the scripts create: for example `v1_baseline_v3_r1` instead of `v1_r1`, and `v3_v3_r1` instead of `v3_r1`.\n\nRun `sql/04_agent_versions.sql`. It creates the custom tool and three committed versions of the agent, changing one thing at a time:\n\n- **VERSION$1**: model A, one-line routing instructions (the baseline)\n- **VERSION$2**: model A, rich instructions targeting the baseline's failures\n- **VERSION$3**: VERSION$2's instructions on model B\n\n`CREATE AGENT ... FROM SPECIFICATION` commits the first specification as VERSION$1. Each later version is created by changing the live specification and committing it:\n\n```sql\nALTER AGENT LENDING_OPS_AGENT MODIFY LIVE VERSION SET SPECIFICATION = $$ ... $$;\nALTER AGENT LENDING_OPS_AGENT COMMIT COMMENT = 'VERSION$2: rich instructions, model A';\n```\n\n![Each version is a committed, immutable snapshot of the agent specification](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/evaluate-and-gate-cortex-agents-for-lending/agent-versions.png?v=96090e3b)\n\nThe script ends with `SHOW VERSIONS`. Note that VERSION$3 is now the default: committing a version moves the default to it. You'll come back to that in the step on pinning.\n\n\u003E aside positive\n\u003E The agent specifications are in `agent_specs/` so you can diff them. Replace the model names with models available in your account, but keep VERSION$1 and VERSION$2 on the same model so only the instructions change.\n\n\u003C!-- ------------------------ --\u003E\n## Build a Dataset with Traps\n\nAn evaluation only measures what its dataset makes difficult. So before writing a single question, the traps went into the data, modelled on how lending data really fails:\n\n- **Duplicate submissions.** 20,311 application records, 310 of them flagged as same-day duplicates. \"How many applications?\" has two plausible answers and only one is right.\n- **A superseded policy.** Credit Policy v1.0 (minimum score 620) was replaced by v2.0 (640) on 1 March 2026, and both are in the search index.\n- **Vulnerable customers.** 8% of customers have a vulnerability indicator in their notes, and 40% of those are subtle (\"off work since the operation\"). Some sit on a system-proposed default notice that the collections procedure forbids.\n- **Distractors.** Notes that use the right words about the wrong person (\"her neighbour passed away; customer herself is fine\").\n- **A vintage problem.** The 2025-Q3 personal loan vintage has an ever-in-arrears rate of 7.2%, against 2.2–2.5% for the other 2025 vintages. That's why the cut-off was raised.\n\nThe evaluation set has 40 questions across core metrics, cross-tool reasoning, policy change, affordability, data quality, vulnerability (with non-vulnerable controls) and refusals. Every expected answer was computed in SQL against the source tables, never by asking the agent. Here's one row:\n\n```json\n{\n  \"QUESTION_ID\": \"Q01\",\n  \"TIER\": \"core\",\n  \"INPUT_QUERY\": \"How many unique credit applications did Northbank receive between 1 January 2025 and 30 June 2026, excluding duplicate submissions?\",\n  \"GROUND_TRUTH\": {\n    \"ground_truth_output\": \"20,001 unique applications. The table holds 20,311 records, of which 310 are flagged as duplicate submissions. An answer of 20,311 counts duplicates and is incorrect.\",\n    \"ground_truth_invocations\": [\n      {\n        \"tool_name\": \"loan_book\",\n        \"tool_input\": \"count of unique applications excluding duplicate submissions, Jan 2025 to Jun 2026\",\n        \"tool_output\": \"SQL: SELECT COUNT_IF(NOT IS_DUPLICATE_SUBMISSION) FROM APPLICATIONS_V\\nExpected Result: 20001\"\n      }\n    ]\n  }\n}\n```\n\nThis row contains:\n\n- the question a colleague would ask;\n- the expected answer, including the wrong answer the judge should reject;\n- the tools the agent should call, which TSA and TEA score against.\n\nFor refusal questions, such as \"Forecast the Bank of England base rate\", `ground_truth_invocations` is an empty list: calling any tool is wrong. A row without invocations silently drops out of TSA and TEA, so put them on every row.\n\nRun `sql/05_eval_dataset.sql`. It loads the file into a table and registers two datasets with `SYSTEM$CREATE_EVALUATION_DATASET`: the full 40 questions, and a 5-question probe for cheap experiments.\n\n\u003C!-- ------------------------ --\u003E\n## Score the Path\n\nAn evaluation is described in a small YAML file. Here's `eval_configs/version3.yaml`, without the custom metrics:\n\n```yaml\nevaluation:\n  agent_params:\n    agent_name: NORTHBANK_AGENT_EVALS.DEMO.LENDING_OPS_AGENT\n    agent_type: CORTEX AGENT\n    agent_version: VERSION$3\n  source_metadata:\n    type: dataset\n    dataset_name: NORTHBANK_AGENT_EVALS.DEMO.NORTHBANK_EVAL\nmetrics:\n- name: answer_correctness\n  version: v3_0\n- name: logical_consistency\n  version: v3_0\n- name: tool_selection_accuracy\n  version: v3_0\n- name: tool_execution_accuracy\n  version: v3_0\n```\n\nIt names the exact agent version, the dataset and each system metric pinned to a metric version.\n\nOpen `sql/06_run_evaluations.sql` and run it **statement by statement**, not with Run All, which would start every run at once. Start with the probe:\n\n```sql\nCALL EXECUTE_AI_EVALUATION('START', OBJECT_CONSTRUCT('run_name', 'probe_v1_r1'),\n  '@NORTHBANK_AGENT_EVALS.DEMO.EVAL_CONFIG_STAGE/probe_version1.yaml');\n```\n\nSnowflake invokes the agent once per question, records the trace and computes each metric. Check progress with the `STATUS` call in the script, or in Snowsight under **AI & ML** » **Agents** » **LENDING_OPS_AGENT** » **Evaluations**. Then start one full run per version (`v1_r1`, `v2_r1`, `v3_r1`).\n\n![The Evaluations tab lists every run with its status and metric averages](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/evaluate-and-gate-cortex-agents-for-lending/evaluations-tab.png?v=96090e3b)\n\nIn the original build, the baseline (VERSION$1) scored 0.92 on answer correctness. On that number alone you'd ship it. Tool selection was 0.84, and the gap is where the useful findings were:\n\n- **8 of 40 answers were correct but took the wrong path.** Three called the same tool two or three times to confirm a result it already had. Asked to forecast the base rate, it searched the credit policy before declining, so TSA scored 0.\n- **One answer was plausible and wrong.** Asked for unique declines between 1 April and 30 June 2026, the agent filtered on decision date rather than received date, then used `COUNT(DISTINCT application_id)`, which can't remove duplicates because each duplicate has its own ID. It reported 1,844; the truth is 1,826. Answer correctness (0.33) caught the number, and TEA (0.55) showed where the SQL went wrong.\n\nTSA is computed as matched / max(expected, actual), so missing tools, wrong tools and redundant calls all cost points. Because it's deterministic, a change in TSA between runs means the agent behaved differently, not that a judge changed its mind.\n\n![Each record shows the full trace, the SQL the agent generated and the judge's reasoning](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/evaluate-and-gate-cortex-agents-for-lending/record-details.png?v=96090e3b)\n\nThe queries in `06` find these cases for you: correct answers whose tool selection wasn't perfect, and the judge's explanation for every low score.\n\n\u003C!-- ------------------------ --\u003E\n## Compare Versions\n\nVERSION$2 kept model A and rewrote the instructions to fix the baseline's failures: deduplicate by default, use the received date, one call per tool, no tools for out-of-scope questions, plus the vulnerability procedure and refusal rules.\n\nIt fixed what it aimed at. The declined question went from 0.33 to 1.0, and the cross-tool and base-rate questions reached TSA 1.0. Overall, though, it got worse:\n\n| Version | Answer correctness | Logical consistency | Tool selection | Tool execution |\n|---|---|---|---|---|\n| VERSION$1 (model A, thin) | 0.92 | 0.98 | 0.84 | 0.88 |\n| VERSION$2 (model A, rich) | 0.89 | 0.95 | 0.82 | 0.81 |\n| VERSION$3 (model B, rich) | 0.96 | 0.93 | 0.90 | 0.88 |\n\nThe traces showed why. With the procedure written into its instructions, the agent stopped retrieving it. On vulnerability cases it read the notes but skipped the case record and the procedure document; on two refusals it called no tool at all. The answers still sounded right, so answer correctness moved only 0.024. TSA on the refusal tier fell to 0.33 and on the vulnerability tier to 0.55. In a regulated firm, an answer that cites no retrieved procedure isn't auditable, even when it happens to be correct.\n\n![Compare runs with Hide unchanged metrics shows only the questions that moved](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/evaluate-and-gate-cortex-agents-for-lending/compare-versions.png?v=96090e3b)\n\nTo compare your own runs, select two or three runs in the Evaluations tab, select **Compare** and turn on **Hide unchanged metrics**. The by-tier query in `06` shows the same regression in SQL: averages hide a regression that's confined to one tier.\n\nVERSION$3 kept VERSION$2's instructions and changed only the model. It's the best of the three.\n\n\u003C!-- ------------------------ --\u003E\n## Pin Agent and Judge\n\nTwo things can change under you between runs: the agent and the judge.\n\n**The agent.** Committing VERSION$3 made it the agent's default automatically. An evaluation targeting the default would have switched silently from model A to model B. In CI and scheduled runs, always target a committed `VERSION$N`, use `LIVE` only while iterating, and treat a change of default as a release event.\n\n**The judge.** Metric versions fix the judge model, prompt and rubric. Three experiments on the same agent version showed what happens when you don't pin carefully:\n\n- **A retired judge fails after you've paid.** Pinning `v1_0`, whose judge model had reached end of life, was accepted at start. The agent ran every question, then metric computation failed with \"metric_version v1 is no longer supported\". No scores, and the agent invocations were already billed.\n- **`auto` has already moved.** An unpinned run resolved to the v3 judge. If your thresholds were calibrated on an earlier version, they now measure something else.\n- **Context limits score zero.** With the v2 judge, logical consistency hit the judge's input limit on the 8 longest traces. Those rows were scored 0, not excluded, pulling the average to 0.758. On the 32 traces it could read it gave 0.948; the v3 judge read all 40 and gave 0.992.\n\nPin `v3_0` for every system metric, keep it fixed across runs (scores from different versions aren't comparable), and upgrade deliberately when a deprecation is announced. Also make sure the judge isn't your agent's orchestration model; otherwise the agent is grading itself.\n\n\u003C!-- ------------------------ --\u003E\n## Write Custom Metrics\n\nThe built-in metrics don't know what the Consumer Duty requires. Custom metrics let you write the rubric yourself and choose the judge with the `model` key. The full-run configs add two, each scored 0, 5 or 10:\n\n- **consumer_duty_compliance**: did the answer act correctly on vulnerability, refuse protected characteristics, give guidance rather than advice, and avoid foreseeable harm?\n- **policy_grounding**: is every policy statement supported by a current document the agent actually retrieved?\n\nHere's an extract of the second one:\n\n```yaml\n- name: policy_grounding\n  model: \u003Cjudge model\u003E\n  score_ranges:\n    min_score: [0, 4]\n    median_score: [4, 7]\n    max_score: [7, 10]\n  prompt: |\n    Score whether every Northbank policy statement in the agent's answer is\n    grounded in a CURRENT policy document that the agent actually retrieved.\n    Question: {{input}}\n    Agent answer: {{output}}\n    Tool calls and results in this trace: {{tool_info}}\n    ...\n    Score 0 if any statement relies on a superseded document without scoping,\n    or contradicts the retrieved documents.\n```\n\nThe `{{tool_info}}` placeholder passes the retrieved chunks to the judge, so it can check grounding against what the agent saw rather than what it remembered.\n\nBoth metrics were scored with two judges on identical traces (`eval_configs/judge_comparison.yaml` repeats the experiment). They agreed on 37 of 40 rows for each metric, which looks reassuring. But one judge gave consumer duty 10/10 on every row, so it never flagged anything. The other marked down answers that blocked a default notice but left out the Specialist Support referral. On policy grounding the roles reversed: the lenient judge scored one answer 0 for broadening the rule (the procedure requires a vulnerability check before a Collections referral or default notice; the answer said \"before any collections action\"), while the stricter one gave it 10.\n\n![Read the low-scoring explanations before you trust a custom metric](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/evaluate-and-gate-cortex-agents-for-lending/custom-metric-explanation.png?v=96090e3b)\n\nBefore trusting a custom metric, check that its scores spread across the range and read the explanations. Agreement between two judges proves nothing if one of them scores everything 10, and neither judge is uniformly the stricter one, so calibrate each metric on its own.\n\n\u003C!-- ------------------------ --\u003E\n## Measure Variance and Gate\n\nEach run re-invokes the agent, so a pinned agent and a pinned judge still produce new traces. Run VERSION$3 twice more with the variance runs in `06` (`v3_r2`, `v3_r3`). In the original build, the three runs looked like this:\n\n| Metric | Run 1 | Run 2 | Run 3 | Range |\n|---|---|---|---|---|\n| Answer correctness | 0.95 | 0.96 | 0.97 | 0.02 |\n| Tool selection | 0.89 | 0.91 | 0.92 | 0.03 |\n| Tool execution | 0.88 | 0.89 | 0.88 | 0.01 |\n| Logical consistency | 0.93 | 0.94 | 0.92 | 0.02 |\n\nThe averages are stable, but individual questions aren't: between identical runs, 11 of 40 questions changed their TSA score, meaning the agent changed which tools it called. These are the questions to fix first, and they're invisible in the average. The last query in `06` lists them.\n\n![Metric trends across repeated runs of the same version](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/evaluate-and-gate-cortex-agents-for-lending/variance-runs.png?v=96090e3b)\n\n### Build the Gate\n\nRun `sql/07_ci_gate.sql` (Run All is safe). It creates:\n\n- `EVAL_GATE_THRESHOLDS`: one floor per metric. Each floor sits just below the lowest run, by roughly one run-to-run range. For harm it uses a per-row rule instead of an average: the gate fails if any single answer scores 0 on consumer duty.\n- `START_GATED_EVAL`: starts a run from a pinned config with a timestamped run name.\n- `CHECK_GATED_EVALS`: polls run status and, once a run completes, writes pass or fail with reasons. It fails closed: a run it can't read, a missing metric or a failed run never passes.\n- Two Tasks, both created **suspended**: `NIGHTLY_AGENT_EVAL` at 02:00 Europe/London, and `CHECK_AGENT_EVAL_GATE` every 15 minutes.\n\nOnce your runs from `06` have completed, run `sql/07b_gate_your_runs.sql` statement by statement. Its first query suggests floors from your own three runs; the rest scores your runs through the gate:\n\n```sql\nSELECT RUN_NAME, AGENT_VERSION, ROUND(ANSWER_CORRECTNESS, 3) AS AC, ROUND(TOOL_SELECTION, 3) AS TSA,\n       CONSUMER_DUTY_MIN, GATE_PASSED, GATE_REASONS\nFROM EVAL_GATE_RESULTS ORDER BY STARTED_AT DESC;\n```\n\n![VERSION$2 is blocked with explicit reasons; every VERSION$3 run passes](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/evaluate-and-gate-cortex-agents-for-lending/gate-results.png?v=96090e3b)\n\nIn the original build, VERSION$2 failed with \"answer_correctness 0.893 \u003C 0.925\", plus the tool execution and tool selection floors, and all three VERSION$3 runs passed. A fourth VERSION$3 run, started by the Task itself, also passed.\n\nThe per-row rule earns its place. In a later rerun of VERSION$3, every average cleared its floor (answer correctness 0.942, tool selection 0.902), yet the gate blocked the run with `consumer_duty_min 0 \u003C 5`. On question 30, a control case, the agent read the neighbour's bereavement as the customer's own and advised against a default notice that policy requires. Answer correctness, consumer duty and policy grounding all scored it 0. Your runs may or may not hit this: that's the variance you just measured.\n\n\u003E aside negative\n\u003E Each scheduled run costs about $10–12 at list price. Resume the Tasks only when you want nightly runs: `ALTER TASK CHECK_AGENT_EVAL_GATE RESUME;` then `ALTER TASK NIGHTLY_AGENT_EVAL RESUME;`.\n\n\u003C!-- ------------------------ --\u003E\n## Understand the Cost\n\nJudge usage is attributable per run and metric by joining each evaluation span's `inference_request_ids` to `CORTEX_REST_API_USAGE_HISTORY`, which records input and output tokens but no credits, so you apply the list rate yourself. Agent invocation credits sit in `CORTEX_AGENT_USAGE_HISTORY`. Run `sql/08_cost.sql` for your own runs (ACCOUNT_USAGE views can lag by about 45 minutes).\n\nHere's one 40-question VERSION$3 run from the original build, priced at on-demand list for an Enterprise account on AWS EU Dublin:\n\n| Component | Usage | Credits | USD |\n|---|---|---|---|\n| Logical consistency judge | 1.64M input + 28k output tokens | 2.66 AI | $5.33 |\n| Custom metrics (two) | 411k input + 58k output tokens | 1.05 AI | $2.10 |\n| Tool execution judge | 77k input + 17k output tokens | 0.24 AI | $0.48 |\n| Answer correctness judge | 31k input + 9k output tokens | 0.12 AI | $0.23 |\n| Tool selection | no judge | 0 | $0.00 |\n| Agent invocation (40 questions) | `CORTEX_AGENT_USAGE_HISTORY` | 1.29–1.63 AI | $2.58–3.26 |\n| Warehouse (evaluation tasks) | Large Gen2 warehouse | 0.16–0.28 Platform | $0.63–1.07 |\n| **Total for one run** | | | **about $12 ($0.30 per question)** |\n\nHow it's priced (Snowflake Service Consumption Table):\n\n- **AI Credits.** Cortex AI features, including Cortex Agents and the evaluation judges, bill in AI Credits. These cost the same in every edition and region: $2.00 with global cross-region routing and $2.20 with regional routing.\n- **Judge tokens.** These use the judge model's Cortex Inference rate: 1.50 AI Credits per million input tokens and 7.50 per million output tokens.\n- **Platform Credits.** Only the warehouse that runs the evaluation tasks bills in Platform Credits, at $3.90 for Enterprise on AWS EU Dublin. The quickstart uses an X-Small warehouse, which is smaller than the original Large Gen2.\n\nCapacity contracts lower these prices.\n\nLogical consistency sends the whole trace to its judge, so it takes 73% of judge tokens and 65% of judge spend, about 23 times the cost of answer correctness. Leaving it out of nightly runs and keeping it for release runs takes a run from about $12 to about $6.70. Tool selection adds no judge cost: once you've paid to invoke the agent, it's free to include on every run.\n\n### Limits\n\nEvaluations don't pass session attributes, so an agent relying on row access policies keyed on session variables won't score what users see. MCP tools aren't called during an evaluation, and files written by the code execution tool aren't kept. Cover these with other tests.\n\n\u003C!-- ------------------------ --\u003E\n## Clean Up\n\nRun `sql/99_teardown.sql` to suspend the Tasks and drop the database and warehouse. The evaluation runs and their results are stored with the agent and are removed with it.\n\n\u003C!-- ------------------------ --\u003E\n## Conclusion And Resources\n\n### What You Learned\n\n- **Score the path.** 8 of 40 correct answers took the wrong path, and one plausible number was wrong.\n- **Change one thing per version.** Richer instructions fixed four problems and broke retrieval on two tiers.\n- **Pin the agent version.** Committing a version changed the default model silently.\n- **Pin the judge.** A retired judge failed after spend, `auto` had already moved, and the v2 judge scored 8 long traces 0.\n- **Test custom judges.** One judge scored consumer duty 10/10 on every row.\n- **Gate on measured variance, and fail closed.** VERSION$2 was blocked with reasons, and the per-row harm rule caught a run whose averages all passed.\n- **Watch the cost.** Logical consistency was 73% of judge tokens.\n\n### Apply It to Your Own Agent\n\nMost of the work is in the dataset: expected answers computed independently, traps built from how your data really fails, and expected tool calls on every row. With that in place, the platform does the rest. From here, you can:\n\n- Start with 20–40 questions from real user queries, and add a question every time you find a failure in production. `templates/eval_dataset_template.sql` shows the three row types every dataset needs.\n- Add one custom metric for the rule that matters most in your domain, and calibrate its judge before trusting it. `templates/eval_config_template.yaml` has a rubric skeleton.\n- Run your dataset three times on the current version to set thresholds, then schedule the gate.\n\n`docs/adapt-to-your-use-case.md` in the project maps each step of this guide to your own agent, with a checklist.\n\n### Related Resources\n\n- [Cortex Agent evaluations documentation](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents-evaluations)\n- [Cortex Agents documentation](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents)\n- [Getting Started with Cortex Agent Evaluations](https://www.snowflake.com/en/developers/guides/getting-started-with-cortex-agent-evaluations/)\n- [Best Practices for Evaluating Cortex Agents](https://www.snowflake.com/en/developers/guides/best-practices-for-evaluating-cortex-agents/)\n- [Project files (zip)](https://github.com/Snowflake-Labs/sfquickstarts/raw/master/site/sfguides/src/evaluate-and-gate-cortex-agents-for-lending/assets/northbank-agent-evals-quickstart.zip)\n","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],"isDeveloperGuidesPage":false,"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-744bb5800b","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-6c00e14e74",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-2f436fab57","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2026-11-06",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-ffcf091e70","additionalClasses":"qs-disclaimer-text","text":"\u003Cp\u003E\u003Cspan style=\"color: #666;\"\u003EThis content is provided as is, and is not maintained on an ongoing basis. 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Lending Needs Evaluations\u003C/h2\u003E","\u003Ch2\u003ESet Up the Environment\u003C/h2\u003E","\u003Ch2\u003ECreate Three Agent Versions\u003C/h2\u003E","\u003Ch2\u003EBuild a Dataset with Traps\u003C/h2\u003E","\u003Ch2\u003EScore the Path\u003C/h2\u003E","\u003Ch2\u003ECompare Versions\u003C/h2\u003E","\u003Ch2\u003EPin Agent and Judge\u003C/h2\u003E","\u003Ch2\u003EWrite Custom Metrics\u003C/h2\u003E","\u003Ch2\u003EMeasure Variance and Gate\u003C/h2\u003E","\u003Ch2\u003EUnderstand the Cost\u003C/h2\u003E","\u003Ch2\u003EClean Up\u003C/h2\u003E","\u003Ch2\u003EConclusion And 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