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Developers","url":"https://www.snowflake.com/content/snowflake-site/global/en/developers","currentPage":false}]},"flexible_column_cont":{"id":"flexible-column-container-ddf15c1151","propertiesId":"quickstart-template-main-flexible-container","type":"2-column-75-25","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"id":"container-2d034c42d6","layout":"SIMPLE",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"contentfragment":{"id":"contentfragment-d23d20902f","paragraphs":["\u003Ch2\u003EOverview\u003C/h2\u003E\n","\u003Cp\u003EBuilding AI agents is just the beginning—understanding how well they perform is critical for delivering reliable, production-ready applications. Snowflake Cortex Agent Evaluations provides a comprehensive framework for measuring agent performance across multiple dimensions, helping you identify issues and iterate toward better outcomes.\u003C/p\u003E\n","\u003Cp\u003EThis quickstart guides you through setting up and running evaluations on Cortex Agents, building evaluation datasets, defining custom metrics via YAML, and comparing agent configurations to optimize performance.\u003C/p\u003E\n","\u003Cp\u003EIf you are building external agents, such as in python or a framework like LangGraph, please check out \u003Ca href=\"https://www.snowflake.com/en/developers/guides/getting-started-with-ai-observability/\"\u003EGetting Started with AI Observability\u003C/a\u003E instead. This guide is entirely focused on evaluation of Cortex Agents.\u003C/p\u003E\n","\u003Ch3\u003EWhat is Cortex Agent Evaluations?\u003C/h3\u003E\n","\u003Cp\u003ECortex Agent Evaluations is a feature within Snowflake Cortex that enables you to systematically measure and improve your AI agents. It provides built-in metrics and support for custom metrics:\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EBuilt-in Metrics:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EAnswer Correctness\u003C/strong\u003E: Assesses the quality and accuracy of the agent's final response\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ELogical Consistency\u003C/strong\u003E: Assesses the agent trace for redundancies, superfluous tool calls or inefficient use of resources\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003ECustom Metrics via YAML:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EDefine your own evaluation criteria with custom prompts and scoring rubrics\u003C/li\u003E\u003Cli\u003EExamples included in this guide are \u003Cstrong\u003EGroundedness\u003C/strong\u003E (is the response supported by tool outputs?), \u003Cstrong\u003EExecution Efficiency\u003C/strong\u003E (are the agent's actions streamlined?), and \u003Cstrong\u003ETool Selection\u003C/strong\u003E (did the agent call the right tools at the right time?)\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to set up sample marketing data and agents for evaluation\u003C/li\u003E\u003Cli\u003EHow to create evaluation datasets with ground truth data\u003C/li\u003E\u003Cli\u003EHow to define built-in and custom metrics via a YAML configuration file\u003C/li\u003E\u003Cli\u003EHow to run evaluations programmatically using the SQL API\u003C/li\u003E\u003Cli\u003EHow to run evaluations using the Snowsight UI\u003C/li\u003E\u003Cli\u003EHow to compare agent configurations to identify improvements\u003C/li\u003E\u003Cli\u003EHow to use the Evalset Generator app to build custom evaluation datasets\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Build\u003C/h3\u003E\n","\u003Cp\u003EA complete evaluation workflow that enables you to:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ECreate evaluation datasets from agent logs or manual input\u003C/li\u003E\u003Cli\u003EConfigure built-in and custom evaluation metrics via YAML\u003C/li\u003E\u003Cli\u003ERun evaluations programmatically with \u003Ccode\u003EEXECUTE_AI_EVALUATION\u003C/code\u003E or through the Snowsight UI\u003C/li\u003E\u003Cli\u003ECompare different agent configurations\u003C/li\u003E\u003Cli\u003EIterate on agent design based on evaluation insights\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA \u003Ca href=\"https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides\"\u003ESnowflake account\u003C/a\u003E with access to Cortex Agent Evaluations\u003C/li\u003E\u003Cli\u003EA role with privileges to create databases, schemas, tables, and stages\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-suite-cross-region\"\u003ECross-region inference\u003C/a\u003E enabled for your account (required for evaluation LLM judge models — see Model Availability below)\u003C/li\u003E\u003Cli\u003EPython 3.8+ (for optional local Streamlit app)\u003C/li\u003E\u003Cli\u003EBasic familiarity with Snowflake SQL and Cortex Agents\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EModel Availability\u003C/h3\u003E\n","\u003Cp\u003ECortex Agent Evaluations uses \u003Cstrong\u003Eclaude-4-sonnet\u003C/strong\u003E or \u003Cstrong\u003Eclaude-3-5-sonnet\u003C/strong\u003E as the LLM judge for computing metrics. The system automatically selects from these models based on your account settings.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ECross-region inference\u003C/strong\u003E is required:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003Eclaude-4-sonnet\u003C/strong\u003E: Requires cross-region inference enabled for at least one of: Any Region, AWS US, AWS US Commercial Gov, AWS EU, or AWS APJ\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003Eclaude-3-5-sonnet\u003C/strong\u003E: Requires cross-region inference for AWS US, or your account must be in an AWS cluster\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESetup Environment\u003C/h2\u003E\n","\u003Ch3\u003EClone the Repository\u003C/h3\u003E\n","\u003Cp\u003EFirst, clone the quickstart repository to get all the necessary files:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Egit clone https://github.com/Snowflake-Labs/sfguide-getting-started-with-cortex-agent-evaluations.git\ncd sfguide-getting-started-with-cortex-agent-evaluations\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EWhat's in the Repository\u003C/h3\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EFile\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EDescription\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003ESETUP.sql\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESQL script to create sample database, tables, agents, and services\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Emarketing_campaign_eval_config.yaml\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EYAML configuration for evaluation metrics (built-in + custom)\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Eagent_evalset_generator.py\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EStreamlit app for building evaluation datasets\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Erequirements.txt\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EPython dependencies for the Streamlit app\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EREADME.md\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ERepository documentation\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Ch3\u003ERun the Setup Script\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EStep 1.\u003C/strong\u003E Open Snowsight and create a new SQL Worksheet.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EStep 2.\u003C/strong\u003E Copy and paste the contents of \u003Ccode\u003ESETUP.sql\u003C/code\u003E into the worksheet.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EStep 3.\u003C/strong\u003E Execute all statements in order from top to bottom (through Section 10).\u003C/p\u003E\n","\u003Cp\u003EThis script will create:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EMARKETING_CAMPAIGNS_DB\u003C/strong\u003E: A database containing sample marketing data\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAGENT_EVAL_ROLE\u003C/strong\u003E: A dedicated role with all permissions needed for agent evaluation\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EMarketing data tables\u003C/strong\u003E: Campaign performance, feedback, and content data\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESemantic View\u003C/strong\u003E: For Cortex Analyst Service on campaign performance and feedback data\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ECortex Search Service\u003C/strong\u003E: On campaign content for semantic search capabilities\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EStored Procedure\u003C/strong\u003E: Custom agent tool for specialized operations\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ECortex Agent\u003C/strong\u003E: Pre-configured agent with the above services attached\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EUnderstanding the Role Configuration\u003C/h3\u003E\n","\u003Cp\u003EThe setup script creates a new role called \u003Ccode\u003EAGENT_EVAL_ROLE\u003C/code\u003E with all the necessary permissions for running agent evaluations. This includes:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EPermission Type\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EDescription\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EDatabase/Schema Usage\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAccess to the \u003Ccode\u003EMARKETING_CAMPAIGNS_DB\u003C/code\u003E database and \u003Ccode\u003EAGENTS\u003C/code\u003E schema\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EDatabase Roles\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003ESNOWFLAKE.CORTEX_USER\u003C/code\u003E for Cortex features and \u003Ccode\u003ESNOWFLAKE.AI_OBSERVABILITY_EVENTS_LOOKUP\u003C/code\u003E for accessing agent traces\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EDataset Creation\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003ECREATE DATASET\u003C/code\u003E, \u003Ccode\u003ECREATE FILE FORMAT\u003C/code\u003E, \u003Ccode\u003ECREATE TABLE\u003C/code\u003E, and \u003Ccode\u003ECREATE STAGE\u003C/code\u003E on the schema\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ETask Management\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003ECREATE TASK\u003C/code\u003E on the schema and \u003Ccode\u003EEXECUTE TASK\u003C/code\u003E on the account\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAgent Monitoring\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EOWNERSHIP\u003C/code\u003E or \u003Ccode\u003EMONITOR\u003C/code\u003E privilege on agents in the schema\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EWarehouse\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EUSAGE\u003C/code\u003E on the warehouse used for evaluations\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EStage Access\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EREAD\u003C/code\u003E privileges on the stage containing the YAML config\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EGit Integration\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECreate API integrations and Git repositories for loading sample data\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EService Creation\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECreate semantic views, Cortex Search services, procedures, and agents\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003EThe role is automatically granted to your current user, so you can switch to it after the setup completes:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE AGENT_EVAL_ROLE;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote\u003C/strong\u003E: The script starts with \u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E to create the database and role, then switches to \u003Ccode\u003EAGENT_EVAL_ROLE\u003C/code\u003E for all subsequent operations. This follows the principle of least privilege.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EUnderstanding the Evaluation Schema\u003C/h2\u003E\n","\u003Cp\u003EBefore creating evaluations, it's important to understand the expected dataset schema that Cortex Agent Evaluations uses.\u003C/p\u003E\n","\u003Ch3\u003EEvaluation Dataset Schema\u003C/h3\u003E\n","\u003Cp\u003EYour evaluation dataset should follow this structure:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EColumn\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EType\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EDescription\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EINPUT_QUERY\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EVARCHAR\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EThe natural language query to send to the agent\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EGROUND_TRUTH_DATA\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EVARIANT\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EJSON object containing ground truth data\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Ch3\u003EGround Truth Data Format\u003C/h3\u003E\n","\u003Cp\u003EThe \u003Ccode\u003EGROUND_TRUTH_DATA\u003C/code\u003E column contains a JSON object with the following structure:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-json\"\u003E{\n  "ground_truth_invocations": [\n    {\n      "tool_name": "cortex_analyst",\n      "parameters": {...}\n    }\n  ],\n  "ground_truth_output": "Expected response text..."\n}\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EExample Dataset Row\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EINSERT INTO EVALS_TABLE (INPUT_QUERY, GROUND_TRUTH_DATA)\nVALUES (\n  'What was the total spend on our summer campaign?',\n  PARSE_JSON('{\n    "ground_truth_invocations": [\n      {"tool_name": "cortex_analyst", "service": "campaign_analytics"}\n    ],\n    "ground_truth_output": "The total spend on the summer campaign was $45,000."\n  }')\n);\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EUnderstanding the YAML Evaluation Config\u003C/h2\u003E\n","\u003Cp\u003ECortex Agent Evaluations uses a YAML configuration file to define the agent being evaluated, the dataset to use, and the metrics to compute — including custom metrics with LLM-as-judge prompts.\u003C/p\u003E\n","\u003Ch3\u003EYAML Config Structure\u003C/h3\u003E\n","\u003Cp\u003EThe config file has three main sections:\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E1. Evaluation\u003C/strong\u003E — specifies the agent, run metadata, and dataset:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-yaml\"\u003Eevaluation:\n  agent_params:\n    agent_name: MARKETING_CAMPAIGNS_DB.AGENTS.MARKETING_CAMPAIGNS_AGENT\n    agent_type: CORTEX AGENT\n  run_params:\n    label: Marketing Campaign Agent Evaluation\n    description: Evaluating metrics for the marketing campaign analytics agent\n  source_metadata:\n    type: dataset\n    dataset_name: MARKETING_CAMPAIGNS_DB.AGENTS.MARKETING_CAMPAIGN_EVALSET\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003E2. Built-in Metrics\u003C/strong\u003E — referenced by name:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-yaml\"\u003Emetrics:\n  - correctness\n  - logical_consistency\n  - tool_selection_accuracy\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003E3. Custom Metrics\u003C/strong\u003E — defined inline with a name, scoring rubric, and LLM-as-judge prompt:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-yaml\"\u003Emetrics:\n  # ... built-in metrics above ...\n\n  - name: groundedness\n    score_ranges:\n      min_score: [0, 0.33]\n      median_score: [0.34, 0.66]\n      max_score: [0.67, 1]\n    prompt: |\n      You are evaluating the groundedness of an AI agent's response.\n      \n      User Query: {{input}}\n      Agent Response: {{output}}\n      Expected Answer: {{ground_truth}}\n      \n      Rate whether each statement in the response is supported\n      by the tool outputs and retrieved data in the execution trace...\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EPrompt Template Variables\u003C/h3\u003E\n","\u003Cp\u003ECustom metric prompts can use template variables that are automatically populated from the evaluation run. These let you explicitly reference specific fields in your prompt:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EVariable\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EDescription\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E{{input}}\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EUser query/question sent to the agent\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E{{output}}\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAgent's generated response\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E{{ground_truth}}\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EExpected answer from the dataset\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E{{tool_info}}\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ETool information used during execution\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E{{start_timestamp}}\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EStart timestamp of the span\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E{{duration}}\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EDuration in milliseconds\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E{{span_id}}\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EUnique span identifier\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E{{span_type}}\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EType of the span\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E{{span_name}}\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EName of the span\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E{{llm_model}}\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ELLM model used\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E{{error}}\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EError message (if any)\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E{{status}}\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EExecution status\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote\u003C/strong\u003E: The complete agent execution trace is always provided to the LLM judge as context, regardless of which template variables you use in your prompt. Template variables give you a way to explicitly call out specific fields in your scoring rubric, but the judge has access to the full trace — including all tool calls, tool outputs, intermediate reasoning steps, and span details — even if you don't reference them via variables.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003ECustom Metrics in This Quickstart\u003C/h3\u003E\n","\u003Cp\u003EThe included \u003Ccode\u003Emarketing_campaign_eval_config.yaml\u003C/code\u003E defines two custom metrics in addition to the three built-in metrics:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003EGroundedness\u003C/strong\u003E: Evaluates whether each statement in the agent's response is supported by information from the execution trace (tool outputs, retrieved data). Scores range from 0 (fabricated content) to 1 (all claims traceable to tool outputs).\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003EExecution Efficiency\u003C/strong\u003E: Evaluates how optimally the agent uses its available tools. Considers whether the right tools were selected, whether there were redundant calls, and whether the execution path was direct. Scores range from 0 (inefficient) to 1 (optimal tool usage).\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ERun Your First Evaluation\u003C/h2\u003E\n","\u003Cp\u003ENow let's create and run your first agent evaluation. We'll cover both the programmatic SQL API and the Snowsight UI approach.\u003C/p\u003E\n","\u003Ch3\u003EStep 1: Create an Evaluation Dataset\u003C/h3\u003E\n","\u003Cp\u003EFirst, create a dataset from the evaluation table that was populated by the setup script.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EUsing the SQL API:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECALL SYSTEM$CREATE_EVALUATION_DATASET(\n    'Cortex Agent',\n    'MARKETING_CAMPAIGNS_DB.AGENTS.EVALS_TABLE',\n    'MARKETING_CAMPAIGNS_DB.AGENTS.MARKETING_CAMPAIGN_EVALSET',\n    OBJECT_CONSTRUCT('query_text', 'INPUT_QUERY', 'expected_tools', 'GROUND_TRUTH_DATA'));\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EConfirm the dataset was created:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESHOW DATASETS;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EUsing the UI:\u003C/strong\u003E In Snowsight, navigate to \u003Cstrong\u003EAI & ML > Agents\u003C/strong\u003E, click on your agent, then click the \u003Cstrong\u003EEvaluations\u003C/strong\u003E tab. Click \u003Cstrong\u003ECreate New Evaluation\u003C/strong\u003E, enter a name (e.g., "marketing-campaign-agent-baseline"), and click \u003Cstrong\u003ENext\u003C/strong\u003E. Select \u003Cstrong\u003ECreate New Dataset\u003C/strong\u003E, choose \u003Ccode\u003EMARKETING_CAMPAIGNS_DB.AGENTS.EVALS_TABLE\u003C/code\u003E as the input table, and specify \u003Ccode\u003EMARKETING_CAMPAIGNS_DB.AGENTS\u003C/code\u003E as the destination with dataset name \u003Ccode\u003EMARKETING_CAMPAIGN_EVAL_DATASET\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-cortex-agent-evaluations/agent-evaluations-page.png?v=76dcf90b\" alt=\"Agent Evaluations Tab\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EStep 2: Upload the YAML Config\u003C/h3\u003E\n","\u003Cp\u003ECreate a stage to store the evaluation configuration file, then copy the YAML from the Git repository:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE STAGE MARKETING_CAMPAIGNS_DB.AGENTS.EVAL_CONFIG_STAGE\n  DIRECTORY = (ENABLE = TRUE)\n  COMMENT = 'Internal stage to host evaluation config files';\n\nCOPY FILES INTO @MARKETING_CAMPAIGNS_DB.AGENTS.EVAL_CONFIG_STAGE\nFROM @CORTEX_AGENT_QUICKSTART_REPO/branches/main/\nFILES = ('marketing_campaign_eval_config.yaml');\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EConfirm the YAML was uploaded:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ELS @MARKETING_CAMPAIGNS_DB.AGENTS.EVAL_CONFIG_STAGE;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStep 3: Start the Evaluation Run\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EUsing the SQL API:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECALL EXECUTE_AI_EVALUATION(\n  'START',\n  OBJECT_CONSTRUCT('run_name', 'BASELINE_MARKETING_AGENT_EVAL_RUN'),\n  '@MARKETING_CAMPAIGNS_DB.AGENTS.EVAL_CONFIG_STAGE/marketing_campaign_eval_config.yaml'\n);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ECheck the status of the run (re-run periodically until complete):\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECALL EXECUTE_AI_EVALUATION(\n  'STATUS',\n  OBJECT_CONSTRUCT('run_name', 'BASELINE_MARKETING_AGENT_EVAL_RUN'),\n  '@MARKETING_CAMPAIGNS_DB.AGENTS.EVAL_CONFIG_STAGE/marketing_campaign_eval_config.yaml'\n);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe evaluation will execute your queries and compute all metrics (built-in and custom). This typically takes \u003Cstrong\u003E3-5 minutes\u003C/strong\u003E depending on dataset size.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EUsing the UI:\u003C/strong\u003E After selecting your dataset in the evaluation wizard, select \u003Ccode\u003EINPUT_QUERY\u003C/code\u003E as the Query Text column and enable the metrics you want to measure:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EAnswer Correctness\u003C/strong\u003E — Reference the \u003Ccode\u003EGROUND_TRUTH_DATA\u003C/code\u003E column\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EClick \u003Cstrong\u003ECreate Evaluation\u003C/strong\u003E to start the run.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote\u003C/strong\u003E: Custom metrics are only available via the programmatic YAML.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-cortex-agent-evaluations/select-metrics.png?v=76dcf90b\" alt=\"Configure Metrics\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-cortex-agent-evaluations/run-in-progress.png?v=76dcf90b\" alt=\"Run in progress\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EExamine Evaluation Results\u003C/h2\u003E\n","\u003Cp\u003ENow that you've completed your first Cortex Agents Evaluation Run, you can view the results to understand how your agent is performing.\u003C/p\u003E\n","\u003Cp\u003EOn the \u003Ccode\u003EEvaluations\u003C/code\u003E page, we can view overall metrics aggregated by run. So far, we just have one run to view here.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-cortex-agent-evaluations/evaluation-runs.png?v=76dcf90b\" alt=\"Evaluation Runs\"\u003E\u003C/p\u003E\n","\u003Cp\u003EOn this page, you can see metrics including:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E/# OF RECORDS (total number of records for the run)\u003C/li\u003E\u003Cli\u003EAVG DURATION (Average time the agent executed for a single record)\u003C/li\u003E\u003Cli\u003EAC (Answer Correctness)\u003C/li\u003E\u003Cli\u003ELC (Logical Consistency)\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EIf you ran via the YAML config, you'll also see your custom metrics: \u003Cstrong\u003EGroundedness\u003C/strong\u003E and \u003Cstrong\u003EExecution Efficiency\u003C/strong\u003E.\u003C/p\u003E\n","\u003Cp\u003EThen, by clicking on the run you can view all of the records that make up the run. This allows you to see which records the agent performed well on and which ones it did not perform as well.\u003C/p\u003E\n","\u003Cp\u003EFrom here, you should select a record with low evaluation scores. We'll start by choosing the query "Generate a report for the holiday gift guide" that scored low on both Answer Correctness (AC) and our custom Tool Selection metric (T).\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-cortex-agent-evaluations/run-records.png?v=76dcf90b\" alt=\"Run Records\"\u003E\u003C/p\u003E\n","\u003Cp\u003EOn this page you will see three columns:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EOn the left most side, you can see the evaluation results.\u003C/li\u003E\u003Cli\u003EIn the middle column, you can see the agent trace. This view is interactive and you can examine individual spans of the trace by clicking, and their attributes will display on the right.\u003C/li\u003E\u003Cli\u003EOn the right column, you can see the span information for the selected part of the trace, including fields like \u003Ccode\u003EMessages\u003C/code\u003E, \u003Ccode\u003EConversation History\u003C/code\u003E, \u003Ccode\u003EOutput\u003C/code\u003E, \u003Ccode\u003EModel Name\u003C/code\u003E, \u003Ccode\u003EToken Count\u003C/code\u003E, and more.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EReturning to the left side is how we can examine the metrics. By expanding the custom metric Tool Selection results, we can see in the \u003Cem\u003EWeaknesses\u003C/em\u003E section that the \u003Ccode\u003Equery_performance_metrics\u003C/code\u003E tool call was missing in the agent trace.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-cortex-agent-evaluations/tsa.png?v=76dcf90b\" alt=\"Tool Selection\"\u003E\u003C/p\u003E\n","\u003Cp\u003EBy missing this tool call, the final answer lost the comprehensive performance data and detailed metrics expected in the response.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-cortex-agent-evaluations/ac.png?v=76dcf90b\" alt=\"Answer Correctness\"\u003E\u003C/p\u003E\n","\u003Ch3\u003ERetrieving Results Programmatically\u003C/h3\u003E\n","\u003Cp\u003EYou can also retrieve evaluation results via SQL, which is useful for automation and deeper analysis.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EGet evaluation data for a run:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT * FROM TABLE(SNOWFLAKE.LOCAL.GET_AI_EVALUATION_DATA(\n  'MARKETING_CAMPAIGNS_DB',\n  'AGENTS',\n  'MARKETING_CAMPAIGNS_AGENT',\n  'cortex agent',\n  'BASELINE_MARKETING_AGENT_EVAL_RUN'\n));\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EGet the trace for a specific record\u003C/strong\u003E (using a \u003Ccode\u003Erecord_id\u003C/code\u003E from the evaluation data):\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT * FROM TABLE(SNOWFLAKE.LOCAL.GET_AI_RECORD_TRACE(\n  'MARKETING_CAMPAIGNS_DB',\n  'AGENTS',\n  'MARKETING_CAMPAIGNS_AGENT',\n  'cortex agent',\n  '<record_id>'\n));\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EGet error and warning logs for a run:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT *\nFROM TABLE(SNOWFLAKE.LOCAL.GET_AI_OBSERVABILITY_LOGS(\n  'MARKETING_CAMPAIGNS_DB',\n  'AGENTS',\n  'MARKETING_CAMPAIGNS_AGENT',\n  'cortex agent'\n))\nWHERE record:"severity_text" IN ('ERROR', 'WARN')\n  AND record_attributes:"snow.ai.observability.run.name" = 'BASELINE_MARKETING_AGENT_EVAL_RUN';\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EImproving the Agent\u003C/h2\u003E\n","\u003Cp\u003EOne of the most powerful features of Cortex Agent Evaluations is the ability to compare different agent configurations to identify improvements.\u003C/p\u003E\n","\u003Cp\u003ECommon improvements to test include:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EMore detailed orchestration instructions\u003C/li\u003E\u003Cli\u003EAdditional context in response instructions\u003C/li\u003E\u003Cli\u003EDifferent tool configurations\u003C/li\u003E\u003Cli\u003EModified semantic models\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EFor this example, we'll improve the agent by updating its specification with detailed orchestration instructions, response formatting guidelines, and richer tool descriptions.\u003C/p\u003E\n","\u003Ch3\u003EUpdate the Agent Specification\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EUsing SQL\u003C/strong\u003E, run the following \u003Ccode\u003EALTER AGENT\u003C/code\u003E statement to update the live agent with improved instructions and tool descriptions:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EALTER AGENT MARKETING_CAMPAIGNS_AGENT\nMODIFY LIVE VERSION SET SPECIFICATION = $$\n{\n  "models": {\n    "orchestration": "auto"\n  },\n  "instructions": {\n    "orchestration": "You are the Marketing Campaigns Analyst, a specialized assistant for analyzing marketing campaign performance, customer feedback, and generating comprehensive reports.\\n\\n## Tool Selection Guidelines\\n\\n**For QUANTITATIVE questions (metrics, numbers, trends, comparisons):**\\nUse `query_performance_metrics` FIRST.\\nExamples: ROI, revenue, conversions, impressions, clicks, engagement rates, cost metrics, time series trends, campaign comparisons by numbers.\\n\\n**For QUALITATIVE questions (content, feedback, strategy, themes):**\\nUse `search_campaign_content` FIRST.\\nExamples: Customer feedback, marketing copy, A/B test results, campaign descriptions, recommendations, strategy insights.\\n\\n**For questions requiring BOTH quantitative AND qualitative data:**\\n1. Start with `search_campaign_content` to identify the campaign and understand context\\n2. Then use `query_performance_metrics` to get specific metrics\\nExamples: \\"What are key themes AND financial metrics for X campaign?\\"\\n\\n**For REPORT GENERATION requests:**\\n1. FIRST use `query_performance_metrics` to get the campaign_id by querying for the campaign name\\n2. Then call `generate_campaign_report` with the campaign_id\\n3. If generate_campaign_report fails, fall back to manually assembling the report using query_performance_metrics and search_campaign_content\\n\\n## Critical Rules\\n\\n1. **Minimize tool calls**: Aim for 1-2 tool calls per question. Do NOT make redundant calls.\\n2. **Get campaign_id correctly**: When generating reports, query for campaign_id by campaign_name FIRST.\\n3. **Handle errors gracefully**: If a tool fails, acknowledge the error and try an alternative approach.\\n4. **Be specific in queries**: Use exact campaign names when filtering. Avoid overly broad queries.\\n5. **For similarity/comparison questions**: Search ALL campaigns first to understand the full landscape before making comparisons. Consider content themes, target audience, and strategy - not just metrics.",\n    "response": "## Response Format\\n\\n- Lead with the direct answer, then provide supporting details\\n- Use markdown formatting: headers (##), bold (**), and bullet points\\n- For metrics, always include the specific numbers and units (%, $, count)\\n- Use tables for multi-row data (3+ items)\\n- Include campaign dates/duration when relevant\\n\\n## Tone\\n- Be concise and professional\\n- State facts directly without hedging (\\"it appears\\", \\"it seems\\")\\n- When data is unavailable, clearly state what's missing and suggest alternatives\\n\\n## Error Handling\\n- If a query returns no results, explain why and suggest alternative approaches\\n- If a tool fails, acknowledge the error before trying alternatives\\n- Never fabricate data - only report what was retrieved from tools",\n    "sample_questions": [\n      {\n        "question": "What campaigns have the highest ROI?"\n      },\n      {\n        "question": "What was the customer feedback on our mobile app campaign?"\n      },\n      {\n        "question": "Generate a report for the Black Friday campaign"\n      },\n      {\n        "question": "Are there any temporal trends around revenue?"\n      }\n    ]\n  },\n  "tools": [\n    {\n      "tool_spec": {\n        "type": "cortex_analyst_text_to_sql",\n        "name": "query_performance_metrics",\n        "description": "Query structured campaign performance data from the data warehouse using natural language.\\n\\n## Data Available\\n- Campaign metadata: campaign_id, campaign_name, campaign_type, channel, start_date, end_date, budget\\n- Financial metrics: revenue, ROI, cost_per_click, cost_per_acquisition\\n- Engagement metrics: impressions, clicks, conversions, engagement_rate\\n- Time-series data: daily performance breakdowns\\n\\n## When to Use\\n- Questions about numbers, metrics, KPIs, or performance data\\n- Comparing campaigns by quantitative measures\\n- Analyzing trends over time (temporal analysis)\\n- Getting campaign IDs for report generation\\n- Aggregations, rankings, and statistical summaries\\n\\n## When NOT to Use\\n- Questions about marketing copy, strategy, or campaign descriptions (use search_campaign_content)\\n- Questions about customer feedback or qualitative insights (use search_campaign_content)\\n- Questions about A/B test learnings or recommendations (use search_campaign_content)\\n\\n## Query Best Practices\\n- Be specific with campaign names when filtering\\n- Specify date ranges explicitly (e.g., \\"in Q4 2024\\", \\"November 2024\\")\\n- Request specific metrics by name: ROI, revenue, conversions, engagement_rate\\n- For campaign lookups, query by campaign_name to get campaign_id"\n      }\n    },\n    {\n      "tool_spec": {\n        "type": "cortex_search",\n        "name": "search_campaign_content",\n        "description": "Search unstructured campaign content including descriptions, marketing copy, A/B test results, customer feedback, and strategic recommendations.\\n\\n## Data Available\\n- Campaign descriptions and objectives\\n- Marketing copy and messaging\\n- A/B testing results and learnings\\n- Customer feedback (satisfaction scores, comments, improvement requests)\\n- Strategic recommendations and insights\\n- Target audience information\\n\\n## When to Use\\n- Questions about customer feedback or satisfaction\\n- Finding campaign descriptions, themes, or strategies\\n- Understanding marketing copy and messaging approaches\\n- Discovering A/B test results and learnings\\n- Comparing campaigns by qualitative attributes (themes, audience, approach)\\n- Finding recommendations or improvement suggestions\\n\\n## When NOT to Use\\n- Questions requiring specific numbers or metrics (use query_performance_metrics)\\n- Calculating ROI, revenue, or engagement rates (use query_performance_metrics)\\n- Time-series analysis or trend calculations (use query_performance_metrics)\\n\\n## Search Best Practices\\n- Use specific campaign names: \\"Mobile App Download Campaign\\", \\"Black Friday Mega Sale\\"\\n- Include relevant keywords: \\"customer feedback\\", \\"A/B test\\", \\"marketing copy\\"\\n- For similarity comparisons, search broadly first to understand all campaigns"\n      }\n    },\n    {\n      "tool_spec": {\n        "type": "generic",\n        "name": "generate_campaign_report",\n        "description": "Generate a comprehensive HTML report for a specific campaign. The report includes all performance metrics, customer feedback, and key insights formatted for presentation.\\n\\n## When to Use\\n- User explicitly asks to \\"generate a report\\" for a campaign\\n- User asks for a \\"comprehensive report\\" or \\"full report\\"\\n- User wants a formatted document they can share or export\\n\\n## When NOT to Use\\n- User just wants to see metrics (use query_performance_metrics instead)\\n- User just wants feedback or content info (use search_campaign_content instead)\\n- User wants to compare multiple campaigns (use the other tools)\\n\\n## Required Input\\n- campaign_id (integer): The unique identifier of the campaign\\n\\n## How to Get campaign_id\\nBEFORE calling this tool, you MUST first use query_performance_metrics to look up the campaign_id by campaign_name. Example query: \\"What is the campaign_id for the Holiday Gift Guide campaign?\\"\\n\\n## Error Handling\\nIf this tool fails with an internal error, fall back to manually assembling the report by:\\n1. Using query_performance_metrics to get all performance data\\n2. Using search_campaign_content to get qualitative insights\\n3. Combining into a structured markdown response",\n        "input_schema": {\n          "type": "object",\n          "properties": {\n            "campaign_id": {\n              "type": "integer",\n              "description": "The unique identifier of the campaign to generate a report for. Get this by first querying for the campaign by name using query_performance_metrics."\n            }\n          },\n          "required": [\n            "campaign_id"\n          ]\n        }\n      }\n    }\n  ],\n  "tool_resources": {\n    "query_performance_metrics": {\n      "execution_environment": {\n        "query_timeout": 299,\n        "type": "warehouse",\n        "warehouse": "COMPUTE_WH"\n      },\n      "semantic_view": "MARKETING_CAMPAIGNS_DB.AGENTS.MARKETING_PERFORMANCE_ANALYST"\n    },\n    "search_campaign_content": {\n      "execution_environment": {\n        "query_timeout": 299,\n        "type": "warehouse",\n        "warehouse": "COMPUTE_WH"\n      },\n      "search_service": "MARKETING_CAMPAIGNS_DB.AGENTS.MARKETING_CAMPAIGNS_SEARCH"\n    },\n    "generate_campaign_report": {\n      "type": "procedure",\n      "identifier": "MARKETING_CAMPAIGNS_DB.AGENTS.GENERATE_CAMPAIGN_REPORT_HTML",\n      "execution_environment": {\n        "type": "warehouse",\n        "warehouse": "COMPUTE_WH",\n        "query_timeout": 300\n      }\n    }\n  }\n}\n$$;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EUsing the UI:\u003C/strong\u003E Navigate to your agent in \u003Cstrong\u003EAI & ML > Agents\u003C/strong\u003E and click \u003Cstrong\u003EEdit\u003C/strong\u003E. Update the orchestration and response instructions fields with the content from the specification above, then save.\u003C/p\u003E\n","\u003Ch3\u003ERun Evaluation on the Improved Agent\u003C/h3\u003E\n","\u003Cp\u003EKick off a new evaluation run against the same dataset using the same YAML config:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECALL EXECUTE_AI_EVALUATION(\n  'START',\n  OBJECT_CONSTRUCT('run_name', 'OPTIMIZED_MARKETING_AGENT_EVAL_RUN'),\n  '@MARKETING_CAMPAIGNS_DB.AGENTS.EVAL_CONFIG_STAGE/marketing_campaign_eval_config.yaml'\n);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ECheck the status:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECALL EXECUTE_AI_EVALUATION(\n  'STATUS',\n  OBJECT_CONSTRUCT('run_name', 'OPTIMIZED_MARKETING_AGENT_EVAL_RUN'),\n  '@MARKETING_CAMPAIGNS_DB.AGENTS.EVAL_CONFIG_STAGE/marketing_campaign_eval_config.yaml'\n);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EUsing the UI:\u003C/strong\u003E Navigate to the \u003Cstrong\u003EEvaluations\u003C/strong\u003E tab on your agent, click \u003Cstrong\u003ECreate New Evaluation\u003C/strong\u003E, name it (e.g., "marketing-campaign-agent-improved-instructions"), select \u003Cstrong\u003EExisting Dataset\u003C/strong\u003E and reuse the same dataset, then enable the same metrics for an apples-to-apples comparison.\u003C/p\u003E\n","\u003Ch3\u003EAnalyze Comparison Results\u003C/h3\u003E\n","\u003Cp\u003EOnce both evaluations complete, you can compare results:\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E1.\u003C/strong\u003E View side-by-side metrics for both agent configurations\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E2.\u003C/strong\u003E Identify specific queries where the improved agent performed better\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E3.\u003C/strong\u003E Investigate cases where added orchestration and response instructions led to:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EMore correct answers\u003C/li\u003E\u003Cli\u003EBetter logical consistency\u003C/li\u003E\u003Cli\u003EImproved custom metric scores (groundedness, execution efficiency, tool selection)\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EKey Questions to Answer\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EWhich queries improved the most with the new configuration?\u003C/li\u003E\u003Cli\u003EAre there patterns in the queries that still fail?\u003C/li\u003E\u003Cli\u003EDid any queries regress with the changes?\u003C/li\u003E\u003Cli\u003EWhat's the overall improvement in each metric?\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EBest Practices\u003C/h2\u003E\n","\u003Ch3\u003EEvaluation Dataset Design\u003C/h3\u003E\n","\u003Ch4\u003EDiverse Query Coverage\u003C/h4\u003E\n\u003Cul\u003E\u003Cli\u003EInclude queries across all expected use cases\u003C/li\u003E\u003Cli\u003ETest edge cases and boundary conditions\u003C/li\u003E\u003Cli\u003EInclude queries that should trigger different tools\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch4\u003EGround Truth Quality\u003C/h4\u003E\n\u003Cul\u003E\u003Cli\u003EEnsure expected outputs are accurate and complete\u003C/li\u003E\u003Cli\u003EReview tool invocation expectations carefully\u003C/li\u003E\u003Cli\u003EUpdate ground truth as requirements evolve\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch4\u003EDataset Size\u003C/h4\u003E\n\u003Cul\u003E\u003Cli\u003EStart with 20-50 representative queries\u003C/li\u003E\u003Cli\u003EExpand coverage based on failure analysis\u003C/li\u003E\u003Cli\u003EBalance breadth and depth\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ECustom Metrics Design\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EStart with the built-in metrics and add custom metrics for domain-specific concerns\u003C/li\u003E\u003Cli\u003EKeep scoring rubrics clear and unambiguous — the LLM judge needs concrete criteria\u003C/li\u003E\u003Cli\u003EUse the \u003Ccode\u003Escore_ranges\u003C/code\u003E field to define meaningful thresholds (e.g., min/median/max)\u003C/li\u003E\u003Cli\u003ETest your custom metric prompts on a few examples before running full evaluations\u003C/li\u003E\u003Cli\u003ELeverage template variables to give the judge relevant context — \u003Ccode\u003E{{input}}\u003C/code\u003E, \u003Ccode\u003E{{output}}\u003C/code\u003E, and \u003Ccode\u003E{{ground_truth}}\u003C/code\u003E for content comparison; \u003Ccode\u003E{{tool_info}}\u003C/code\u003E, \u003Ccode\u003E{{duration}}\u003C/code\u003E, \u003Ccode\u003E{{llm_model}}\u003C/code\u003E for execution details\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EIterative Improvement\u003C/h3\u003E\n","\u003Ch4\u003E1. Establish Baseline\u003C/h4\u003E\n\u003Cul\u003E\u003Cli\u003ERun initial evaluation on your current agent\u003C/li\u003E\u003Cli\u003EDocument baseline metrics across all dimensions\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch4\u003E2. Identify Weaknesses\u003C/h4\u003E\n\u003Cul\u003E\u003Cli\u003EAnalyze queries with low scores\u003C/li\u003E\u003Cli\u003ELook for patterns in failures\u003C/li\u003E\u003Cli\u003EPrioritize high-impact improvements\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch4\u003E3. Make Targeted Changes\u003C/h4\u003E\n\u003Cul\u003E\u003Cli\u003EModify one aspect at a time\u003C/li\u003E\u003Cli\u003EUpdate orchestration or response instructions\u003C/li\u003E\u003Cli\u003EAdjust tool configurations as needed\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch4\u003E4. Measure Impact\u003C/h4\u003E\n\u003Cul\u003E\u003Cli\u003ERe-run evaluation with same dataset\u003C/li\u003E\u003Cli\u003ECompare metrics to baseline\u003C/li\u003E\u003Cli\u003EDocument what worked and what didn't\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch4\u003E5. Iterate\u003C/h4\u003E\n\u003Cul\u003E\u003Cli\u003ERepeat the process with new improvements\u003C/li\u003E\u003Cli\u003ETrack progress over time\u003C/li\u003E\u003Cli\u003EBuild a history of evaluation runs\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ECommon Pitfalls to Avoid\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EOverfitting to evaluation data\u003C/strong\u003E: Ensure your agent generalizes beyond test queries\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EIgnoring regressions\u003C/strong\u003E: Watch for queries that get worse with changes\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EInconsistent ground truth\u003C/strong\u003E: Maintain quality standards across your dataset\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESkipping baseline\u003C/strong\u003E: Always establish a comparison point before making changes\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion and Resources\u003C/h2\u003E\n","\u003Cp\u003ECongratulations! You've successfully set up and run Cortex Agent Evaluations. You now have the tools to systematically measure and improve your AI agents.\u003C/p\u003E\n","\u003Ch3\u003EWhat You Learned\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EEnvironment Setup\u003C/strong\u003E: How to configure sample agents and data for evaluation\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EEvaluation Creation\u003C/strong\u003E: How to build datasets and run evaluations via the SQL API and Snowsight UI\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ECustom Metrics\u003C/strong\u003E: How to define custom evaluation metrics via YAML with LLM-as-judge prompts\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EMetrics Interpretation\u003C/strong\u003E: Understanding answer correctness, logical consistency, and custom metrics\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAgent Comparison\u003C/strong\u003E: How to compare configurations and identify improvements\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EDataset Building\u003C/strong\u003E: Using the Evalset Generator app for custom datasets\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ENext Steps\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EEvaluate your own production agents\u003C/li\u003E\u003Cli\u003EBuild comprehensive evaluation datasets covering your specific use cases\u003C/li\u003E\u003Cli\u003EDesign custom metrics tailored to your domain and quality requirements\u003C/li\u003E\u003Cli\u003EEstablish an evaluation workflow as part of your agent development process\u003C/li\u003E\u003Cli\u003ESet up automated evaluation runs for continuous monitoring\u003C/li\u003E\u003C/ul\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/user-guide/snowflake-cortex/cortex-agents\"\u003ECortex Agents Guide\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/developers/guides/getting-started-with-cortex-agents/\"\u003EGetting Started with Cortex Agents\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/engineering-blog/ai-agent-evaluation-gpa-framework/\"\u003EWhat's Your Agent's GPA? A Framework for Evaluating AI Agent Reliability\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ERepository\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-getting-started-with-cortex-agent-evaluations\"\u003Esfguide-getting-started-with-cortex-agent-evaluations on GitHub\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E"],"description":"Get started with Cortex Agent Evaluations","title":"Getting Started with Cortex Agent Evaluations",":type":"snowflake-site/components/contentfragment",":items":{},":itemsOrder":[],"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],"elements":{"quickstartArticleBody":{"dataType":"string","value":"## Overview\n\nBuilding AI agents is just the beginning—understanding how well they perform is critical for delivering reliable, production-ready applications. Snowflake Cortex Agent Evaluations provides a comprehensive framework for measuring agent performance across multiple dimensions, helping you identify issues and iterate toward better outcomes.\n\nThis quickstart guides you through setting up and running evaluations on Cortex Agents, building evaluation datasets, defining custom metrics via YAML, and comparing agent configurations to optimize performance.\n\nIf you are building external agents, such as in python or a framework like LangGraph, please check out [Getting Started with AI Observability](https://www.snowflake.com/en/developers/guides/getting-started-with-ai-observability/) instead. This guide is entirely focused on evaluation of Cortex Agents.\n\n### What is Cortex Agent Evaluations?\n\nCortex Agent Evaluations is a feature within Snowflake Cortex that enables you to systematically measure and improve your AI agents. It provides built-in metrics and support for custom metrics:\n\n**Built-in Metrics:**\n\n- **Answer Correctness**: Assesses the quality and accuracy of the agent's final response\n- **Logical Consistency**: Assesses the agent trace for redundancies, superfluous tool calls or inefficient use of resources\n\n**Custom Metrics via YAML:**\n\n- Define your own evaluation criteria with custom prompts and scoring rubrics\n- Examples included in this guide are **Groundedness** (is the response supported by tool outputs?), **Execution Efficiency** (are the agent's actions streamlined?), and **Tool Selection** (did the agent call the right tools at the right time?)\n\n### What You'll Learn\n\n- How to set up sample marketing data and agents for evaluation\n- How to create evaluation datasets with ground truth data\n- How to define built-in and custom metrics via a YAML configuration file\n- How to run evaluations programmatically using the SQL API\n- How to run evaluations using the Snowsight UI\n- How to compare agent configurations to identify improvements\n- How to use the Evalset Generator app to build custom evaluation datasets\n\n### What You'll Build\n\nA complete evaluation workflow that enables you to:\n\n- Create evaluation datasets from agent logs or manual input\n- Configure built-in and custom evaluation metrics via YAML\n- Run evaluations programmatically with `EXECUTE_AI_EVALUATION` or through the Snowsight UI\n- Compare different agent configurations\n- Iterate on agent design based on evaluation insights\n\n### Prerequisites\n\n- A [Snowflake account](https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides) with access to Cortex Agent Evaluations\n- A role with privileges to create databases, schemas, tables, and stages\n- [Cross-region inference](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-suite-cross-region) enabled for your account (required for evaluation LLM judge models — see Model Availability below)\n- Python 3.8+ (for optional local Streamlit app)\n- Basic familiarity with Snowflake SQL and Cortex Agents\n\n### Model Availability\n\nCortex Agent Evaluations uses **claude-4-sonnet** or **claude-3-5-sonnet** as the LLM judge for computing metrics. The system automatically selects from these models based on your account settings.\n\n**Cross-region inference** is required:\n\n- **claude-4-sonnet**: Requires cross-region inference enabled for at least one of: Any Region, AWS US, AWS US Commercial Gov, AWS EU, or AWS APJ\n- **claude-3-5-sonnet**: Requires cross-region inference for AWS US, or your account must be in an AWS cluster\n\n\u003C!-- ------------------------ --\u003E\n\n## Setup Environment\n\n### Clone the Repository\n\nFirst, clone the quickstart repository to get all the necessary files:\n\n```bash\ngit clone https://github.com/Snowflake-Labs/sfguide-getting-started-with-cortex-agent-evaluations.git\ncd sfguide-getting-started-with-cortex-agent-evaluations\n```\n\n### What's in the Repository\n\n| File | Description |\n|------|-------------|\n| `SETUP.sql` | SQL script to create sample database, tables, agents, and services |\n| `marketing_campaign_eval_config.yaml` | YAML configuration for evaluation metrics (built-in + custom) |\n| `agent_evalset_generator.py` | Streamlit app for building evaluation datasets |\n| `requirements.txt` | Python dependencies for the Streamlit app |\n| `README.md` | Repository documentation |\n\n### Run the Setup Script\n\n**Step 1.** Open Snowsight and create a new SQL Worksheet.\n\n**Step 2.** Copy and paste the contents of `SETUP.sql` into the worksheet.\n\n**Step 3.** Execute all statements in order from top to bottom (through Section 10).\n\nThis script will create:\n\n- **MARKETING_CAMPAIGNS_DB**: A database containing sample marketing data\n- **AGENT_EVAL_ROLE**: A dedicated role with all permissions needed for agent evaluation\n- **Marketing data tables**: Campaign performance, feedback, and content data\n- **Semantic View**: For Cortex Analyst Service on campaign performance and feedback data\n- **Cortex Search Service**: On campaign content for semantic search capabilities\n- **Stored Procedure**: Custom agent tool for specialized operations\n- **Cortex Agent**: Pre-configured agent with the above services attached\n\n### Understanding the Role Configuration\n\nThe setup script creates a new role called `AGENT_EVAL_ROLE` with all the necessary permissions for running agent evaluations. This includes:\n\n| Permission Type | Description |\n|-----------------|-------------|\n| Database/Schema Usage | Access to the `MARKETING_CAMPAIGNS_DB` database and `AGENTS` schema |\n| Database Roles | `SNOWFLAKE.CORTEX_USER` for Cortex features and `SNOWFLAKE.AI_OBSERVABILITY_EVENTS_LOOKUP` for accessing agent traces |\n| Dataset Creation | `CREATE DATASET`, `CREATE FILE FORMAT`, `CREATE TABLE`, and `CREATE STAGE` on the schema |\n| Task Management | `CREATE TASK` on the schema and `EXECUTE TASK` on the account |\n| Agent Monitoring | `OWNERSHIP` or `MONITOR` privilege on agents in the schema |\n| Warehouse | `USAGE` on the warehouse used for evaluations |\n| Stage Access | `READ` privileges on the stage containing the YAML config |\n| Git Integration | Create API integrations and Git repositories for loading sample data |\n| Service Creation | Create semantic views, Cortex Search services, procedures, and agents |\n\nThe role is automatically granted to your current user, so you can switch to it after the setup completes:\n\n```sql\nUSE ROLE AGENT_EVAL_ROLE;\n```\n\n\u003E **Note**: The script starts with `ACCOUNTADMIN` to create the database and role, then switches to `AGENT_EVAL_ROLE` for all subsequent operations. This follows the principle of least privilege.\n\n\u003C!-- ------------------------ --\u003E\n\n## Understanding the Evaluation Schema\n\nBefore creating evaluations, it's important to understand the expected dataset schema that Cortex Agent Evaluations uses.\n\n### Evaluation Dataset Schema\n\nYour evaluation dataset should follow this structure:\n\n| Column | Type | Description |\n|--------|------|-------------|\n| `INPUT_QUERY` | VARCHAR | The natural language query to send to the agent |\n| `GROUND_TRUTH_DATA` | VARIANT | JSON object containing ground truth data |\n\n### Ground Truth Data Format\n\nThe `GROUND_TRUTH_DATA` column contains a JSON object with the following structure:\n\n```json\n{\n  \"ground_truth_invocations\": [\n    {\n      \"tool_name\": \"cortex_analyst\",\n      \"parameters\": {...}\n    }\n  ],\n  \"ground_truth_output\": \"Expected response text...\"\n}\n```\n\n### Example Dataset Row\n\n```sql\nINSERT INTO EVALS_TABLE (INPUT_QUERY, GROUND_TRUTH_DATA)\nVALUES (\n  'What was the total spend on our summer campaign?',\n  PARSE_JSON('{\n    \"ground_truth_invocations\": [\n      {\"tool_name\": \"cortex_analyst\", \"service\": \"campaign_analytics\"}\n    ],\n    \"ground_truth_output\": \"The total spend on the summer campaign was $45,000.\"\n  }')\n);\n```\n\n\u003C!-- ------------------------ --\u003E\n\n## Understanding the YAML Evaluation Config\n\nCortex Agent Evaluations uses a YAML configuration file to define the agent being evaluated, the dataset to use, and the metrics to compute — including custom metrics with LLM-as-judge prompts.\n\n### YAML Config Structure\n\nThe config file has three main sections:\n\n**1. Evaluation** — specifies the agent, run metadata, and dataset:\n\n```yaml\nevaluation:\n  agent_params:\n    agent_name: MARKETING_CAMPAIGNS_DB.AGENTS.MARKETING_CAMPAIGNS_AGENT\n    agent_type: CORTEX AGENT\n  run_params:\n    label: Marketing Campaign Agent Evaluation\n    description: Evaluating metrics for the marketing campaign analytics agent\n  source_metadata:\n    type: dataset\n    dataset_name: MARKETING_CAMPAIGNS_DB.AGENTS.MARKETING_CAMPAIGN_EVALSET\n```\n\n**2. Built-in Metrics** — referenced by name:\n\n```yaml\nmetrics:\n  - correctness\n  - logical_consistency\n  - tool_selection_accuracy\n```\n\n**3. Custom Metrics** — defined inline with a name, scoring rubric, and LLM-as-judge prompt:\n\n```yaml\nmetrics:\n  # ... built-in metrics above ...\n\n  - name: groundedness\n    score_ranges:\n      min_score: [0, 0.33]\n      median_score: [0.34, 0.66]\n      max_score: [0.67, 1]\n    prompt: |\n      You are evaluating the groundedness of an AI agent's response.\n      \n      User Query: {{input}}\n      Agent Response: {{output}}\n      Expected Answer: {{ground_truth}}\n      \n      Rate whether each statement in the response is supported\n      by the tool outputs and retrieved data in the execution trace...\n```\n\n### Prompt Template Variables\n\nCustom metric prompts can use template variables that are automatically populated from the evaluation run. These let you explicitly reference specific fields in your prompt:\n\n| Variable | Description |\n|----------|-------------|\n| `{{input}}` | User query/question sent to the agent |\n| `{{output}}` | Agent's generated response |\n| `{{ground_truth}}` | Expected answer from the dataset |\n| `{{tool_info}}` | Tool information used during execution |\n| `{{start_timestamp}}` | Start timestamp of the span |\n| `{{duration}}` | Duration in milliseconds |\n| `{{span_id}}` | Unique span identifier |\n| `{{span_type}}` | Type of the span |\n| `{{span_name}}` | Name of the span |\n| `{{llm_model}}` | LLM model used |\n| `{{error}}` | Error message (if any) |\n| `{{status}}` | Execution status |\n\n\u003E **Note**: The complete agent execution trace is always provided to the LLM judge as context, regardless of which template variables you use in your prompt. Template variables give you a way to explicitly call out specific fields in your scoring rubric, but the judge has access to the full trace — including all tool calls, tool outputs, intermediate reasoning steps, and span details — even if you don't reference them via variables.\n\n### Custom Metrics in This Quickstart\n\nThe included `marketing_campaign_eval_config.yaml` defines two custom metrics in addition to the three built-in metrics:\n\n- **Groundedness**: Evaluates whether each statement in the agent's response is supported by information from the execution trace (tool outputs, retrieved data). Scores range from 0 (fabricated content) to 1 (all claims traceable to tool outputs).\n\n- **Execution Efficiency**: Evaluates how optimally the agent uses its available tools. Considers whether the right tools were selected, whether there were redundant calls, and whether the execution path was direct. Scores range from 0 (inefficient) to 1 (optimal tool usage).\n\n\u003C!-- ------------------------ --\u003E\n\n## Run Your First Evaluation\n\nNow let's create and run your first agent evaluation. We'll cover both the programmatic SQL API and the Snowsight UI approach.\n\n### Step 1: Create an Evaluation Dataset\n\nFirst, create a dataset from the evaluation table that was populated by the setup script.\n\n**Using the SQL API:**\n\n```sql\nCALL SYSTEM$CREATE_EVALUATION_DATASET(\n    'Cortex Agent',\n    'MARKETING_CAMPAIGNS_DB.AGENTS.EVALS_TABLE',\n    'MARKETING_CAMPAIGNS_DB.AGENTS.MARKETING_CAMPAIGN_EVALSET',\n    OBJECT_CONSTRUCT('query_text', 'INPUT_QUERY', 'expected_tools', 'GROUND_TRUTH_DATA'));\n```\n\nConfirm the dataset was created:\n\n```sql\nSHOW DATASETS;\n```\n\n**Using the UI:** In Snowsight, navigate to **AI & ML \u003E Agents**, click on your agent, then click the **Evaluations** tab. Click **Create New Evaluation**, enter a name (e.g., \"marketing-campaign-agent-baseline\"), and click **Next**. Select **Create New Dataset**, choose `MARKETING_CAMPAIGNS_DB.AGENTS.EVALS_TABLE` as the input table, and specify `MARKETING_CAMPAIGNS_DB.AGENTS` as the destination with dataset name `MARKETING_CAMPAIGN_EVAL_DATASET`.\n\n![Agent Evaluations Tab](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-cortex-agent-evaluations/agent-evaluations-page.png?v=76dcf90b)\n\n### Step 2: Upload the YAML Config\n\nCreate a stage to store the evaluation configuration file, then copy the YAML from the Git repository:\n\n```sql\nCREATE OR REPLACE STAGE MARKETING_CAMPAIGNS_DB.AGENTS.EVAL_CONFIG_STAGE\n  DIRECTORY = (ENABLE = TRUE)\n  COMMENT = 'Internal stage to host evaluation config files';\n\nCOPY FILES INTO @MARKETING_CAMPAIGNS_DB.AGENTS.EVAL_CONFIG_STAGE\nFROM @CORTEX_AGENT_QUICKSTART_REPO/branches/main/\nFILES = ('marketing_campaign_eval_config.yaml');\n```\n\nConfirm the YAML was uploaded:\n\n```sql\nLS @MARKETING_CAMPAIGNS_DB.AGENTS.EVAL_CONFIG_STAGE;\n```\n\n### Step 3: Start the Evaluation Run\n\n**Using the SQL API:**\n\n```sql\nCALL EXECUTE_AI_EVALUATION(\n  'START',\n  OBJECT_CONSTRUCT('run_name', 'BASELINE_MARKETING_AGENT_EVAL_RUN'),\n  '@MARKETING_CAMPAIGNS_DB.AGENTS.EVAL_CONFIG_STAGE/marketing_campaign_eval_config.yaml'\n);\n```\n\nCheck the status of the run (re-run periodically until complete):\n\n```sql\nCALL EXECUTE_AI_EVALUATION(\n  'STATUS',\n  OBJECT_CONSTRUCT('run_name', 'BASELINE_MARKETING_AGENT_EVAL_RUN'),\n  '@MARKETING_CAMPAIGNS_DB.AGENTS.EVAL_CONFIG_STAGE/marketing_campaign_eval_config.yaml'\n);\n```\n\nThe evaluation will execute your queries and compute all metrics (built-in and custom). This typically takes **3-5 minutes** depending on dataset size.\n\n**Using the UI:** After selecting your dataset in the evaluation wizard, select `INPUT_QUERY` as the Query Text column and enable the metrics you want to measure:\n\n- **Answer Correctness** — Reference the `GROUND_TRUTH_DATA` column\n\nClick **Create Evaluation** to start the run.\n\n\u003E **Note**: Custom metrics are only available via the programmatic YAML.\n\n![Configure Metrics](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-cortex-agent-evaluations/select-metrics.png?v=76dcf90b)\n\n![Run in progress](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-cortex-agent-evaluations/run-in-progress.png?v=76dcf90b)\n\n\u003C!-- ------------------------ --\u003E\n\n## Examine Evaluation Results\n\nNow that you've completed your first Cortex Agents Evaluation Run, you can view the results to understand how your agent is performing.\n\nOn the `Evaluations` page, we can view overall metrics aggregated by run. So far, we just have one run to view here.\n\n![Evaluation Runs](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-cortex-agent-evaluations/evaluation-runs.png?v=76dcf90b)\n\nOn this page, you can see metrics including:\n\n- /# OF RECORDS (total number of records for the run)\n- AVG DURATION (Average time the agent executed for a single record)\n- AC (Answer Correctness)\n- LC (Logical Consistency)\n\nIf you ran via the YAML config, you'll also see your custom metrics: **Groundedness** and **Execution Efficiency**.\n\nThen, by clicking on the run you can view all of the records that make up the run. This allows you to see which records the agent performed well on and which ones it did not perform as well.\n\nFrom here, you should select a record with low evaluation scores. We'll start by choosing the query \"Generate a report for the holiday gift guide\" that scored low on both Answer Correctness (AC) and our custom Tool Selection metric (T).\n\n![Run Records](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-cortex-agent-evaluations/run-records.png?v=76dcf90b)\n\nOn this page you will see three columns:\n\n- On the left most side, you can see the evaluation results.\n- In the middle column, you can see the agent trace. This view is interactive and you can examine individual spans of the trace by clicking, and their attributes will display on the right.\n- On the right column, you can see the span information for the selected part of the trace, including fields like `Messages`, `Conversation History`, `Output`, `Model Name`, `Token Count`, and more.\n\nReturning to the left side is how we can examine the metrics. By expanding the custom metric Tool Selection results, we can see in the *Weaknesses* section that the `query_performance_metrics` tool call was missing in the agent trace.\n\n![Tool Selection](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-cortex-agent-evaluations/tsa.png?v=76dcf90b)\n\nBy missing this tool call, the final answer lost the comprehensive performance data and detailed metrics expected in the response.\n\n![Answer Correctness](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-cortex-agent-evaluations/ac.png?v=76dcf90b)\n\n### Retrieving Results Programmatically\n\nYou can also retrieve evaluation results via SQL, which is useful for automation and deeper analysis.\n\n**Get evaluation data for a run:**\n\n```sql\nSELECT * FROM TABLE(SNOWFLAKE.LOCAL.GET_AI_EVALUATION_DATA(\n  'MARKETING_CAMPAIGNS_DB',\n  'AGENTS',\n  'MARKETING_CAMPAIGNS_AGENT',\n  'cortex agent',\n  'BASELINE_MARKETING_AGENT_EVAL_RUN'\n));\n```\n\n**Get the trace for a specific record** (using a `record_id` from the evaluation data):\n\n```sql\nSELECT * FROM TABLE(SNOWFLAKE.LOCAL.GET_AI_RECORD_TRACE(\n  'MARKETING_CAMPAIGNS_DB',\n  'AGENTS',\n  'MARKETING_CAMPAIGNS_AGENT',\n  'cortex agent',\n  '\u003Crecord_id\u003E'\n));\n```\n\n**Get error and warning logs for a run:**\n\n```sql\nSELECT *\nFROM TABLE(SNOWFLAKE.LOCAL.GET_AI_OBSERVABILITY_LOGS(\n  'MARKETING_CAMPAIGNS_DB',\n  'AGENTS',\n  'MARKETING_CAMPAIGNS_AGENT',\n  'cortex agent'\n))\nWHERE record:\"severity_text\" IN ('ERROR', 'WARN')\n  AND record_attributes:\"snow.ai.observability.run.name\" = 'BASELINE_MARKETING_AGENT_EVAL_RUN';\n```\n\n\u003C!-- ------------------------ --\u003E\n\n## Improving the Agent\n\nOne of the most powerful features of Cortex Agent Evaluations is the ability to compare different agent configurations to identify improvements. \n\nCommon improvements to test include:\n\n- More detailed orchestration instructions\n- Additional context in response instructions\n- Different tool configurations\n- Modified semantic models\n\nFor this example, we'll improve the agent by updating its specification with detailed orchestration instructions, response formatting guidelines, and richer tool descriptions.\n\n### Update the Agent Specification\n\n**Using SQL**, run the following `ALTER AGENT` statement to update the live agent with improved instructions and tool descriptions:\n\n```sql\nALTER AGENT MARKETING_CAMPAIGNS_AGENT\nMODIFY LIVE VERSION SET SPECIFICATION = $$\n{\n  \"models\": {\n    \"orchestration\": \"auto\"\n  },\n  \"instructions\": {\n    \"orchestration\": \"You are the Marketing Campaigns Analyst, a specialized assistant for analyzing marketing campaign performance, customer feedback, and generating comprehensive reports.\\n\\n## Tool Selection Guidelines\\n\\n**For QUANTITATIVE questions (metrics, numbers, trends, comparisons):**\\nUse `query_performance_metrics` FIRST.\\nExamples: ROI, revenue, conversions, impressions, clicks, engagement rates, cost metrics, time series trends, campaign comparisons by numbers.\\n\\n**For QUALITATIVE questions (content, feedback, strategy, themes):**\\nUse `search_campaign_content` FIRST.\\nExamples: Customer feedback, marketing copy, A/B test results, campaign descriptions, recommendations, strategy insights.\\n\\n**For questions requiring BOTH quantitative AND qualitative data:**\\n1. Start with `search_campaign_content` to identify the campaign and understand context\\n2. Then use `query_performance_metrics` to get specific metrics\\nExamples: \\\"What are key themes AND financial metrics for X campaign?\\\"\\n\\n**For REPORT GENERATION requests:**\\n1. FIRST use `query_performance_metrics` to get the campaign_id by querying for the campaign name\\n2. Then call `generate_campaign_report` with the campaign_id\\n3. If generate_campaign_report fails, fall back to manually assembling the report using query_performance_metrics and search_campaign_content\\n\\n## Critical Rules\\n\\n1. **Minimize tool calls**: Aim for 1-2 tool calls per question. Do NOT make redundant calls.\\n2. **Get campaign_id correctly**: When generating reports, query for campaign_id by campaign_name FIRST.\\n3. **Handle errors gracefully**: If a tool fails, acknowledge the error and try an alternative approach.\\n4. **Be specific in queries**: Use exact campaign names when filtering. Avoid overly broad queries.\\n5. **For similarity/comparison questions**: Search ALL campaigns first to understand the full landscape before making comparisons. Consider content themes, target audience, and strategy - not just metrics.\",\n    \"response\": \"## Response Format\\n\\n- Lead with the direct answer, then provide supporting details\\n- Use markdown formatting: headers (##), bold (**), and bullet points\\n- For metrics, always include the specific numbers and units (%, $, count)\\n- Use tables for multi-row data (3+ items)\\n- Include campaign dates/duration when relevant\\n\\n## Tone\\n- Be concise and professional\\n- State facts directly without hedging (\\\"it appears\\\", \\\"it seems\\\")\\n- When data is unavailable, clearly state what's missing and suggest alternatives\\n\\n## Error Handling\\n- If a query returns no results, explain why and suggest alternative approaches\\n- If a tool fails, acknowledge the error before trying alternatives\\n- Never fabricate data - only report what was retrieved from tools\",\n    \"sample_questions\": [\n      {\n        \"question\": \"What campaigns have the highest ROI?\"\n      },\n      {\n        \"question\": \"What was the customer feedback on our mobile app campaign?\"\n      },\n      {\n        \"question\": \"Generate a report for the Black Friday campaign\"\n      },\n      {\n        \"question\": \"Are there any temporal trends around revenue?\"\n      }\n    ]\n  },\n  \"tools\": [\n    {\n      \"tool_spec\": {\n        \"type\": \"cortex_analyst_text_to_sql\",\n        \"name\": \"query_performance_metrics\",\n        \"description\": \"Query structured campaign performance data from the data warehouse using natural language.\\n\\n## Data Available\\n- Campaign metadata: campaign_id, campaign_name, campaign_type, channel, start_date, end_date, budget\\n- Financial metrics: revenue, ROI, cost_per_click, cost_per_acquisition\\n- Engagement metrics: impressions, clicks, conversions, engagement_rate\\n- Time-series data: daily performance breakdowns\\n\\n## When to Use\\n- Questions about numbers, metrics, KPIs, or performance data\\n- Comparing campaigns by quantitative measures\\n- Analyzing trends over time (temporal analysis)\\n- Getting campaign IDs for report generation\\n- Aggregations, rankings, and statistical summaries\\n\\n## When NOT to Use\\n- Questions about marketing copy, strategy, or campaign descriptions (use search_campaign_content)\\n- Questions about customer feedback or qualitative insights (use search_campaign_content)\\n- Questions about A/B test learnings or recommendations (use search_campaign_content)\\n\\n## Query Best Practices\\n- Be specific with campaign names when filtering\\n- Specify date ranges explicitly (e.g., \\\"in Q4 2024\\\", \\\"November 2024\\\")\\n- Request specific metrics by name: ROI, revenue, conversions, engagement_rate\\n- For campaign lookups, query by campaign_name to get campaign_id\"\n      }\n    },\n    {\n      \"tool_spec\": {\n        \"type\": \"cortex_search\",\n        \"name\": \"search_campaign_content\",\n        \"description\": \"Search unstructured campaign content including descriptions, marketing copy, A/B test results, customer feedback, and strategic recommendations.\\n\\n## Data Available\\n- Campaign descriptions and objectives\\n- Marketing copy and messaging\\n- A/B testing results and learnings\\n- Customer feedback (satisfaction scores, comments, improvement requests)\\n- Strategic recommendations and insights\\n- Target audience information\\n\\n## When to Use\\n- Questions about customer feedback or satisfaction\\n- Finding campaign descriptions, themes, or strategies\\n- Understanding marketing copy and messaging approaches\\n- Discovering A/B test results and learnings\\n- Comparing campaigns by qualitative attributes (themes, audience, approach)\\n- Finding recommendations or improvement suggestions\\n\\n## When NOT to Use\\n- Questions requiring specific numbers or metrics (use query_performance_metrics)\\n- Calculating ROI, revenue, or engagement rates (use query_performance_metrics)\\n- Time-series analysis or trend calculations (use query_performance_metrics)\\n\\n## Search Best Practices\\n- Use specific campaign names: \\\"Mobile App Download Campaign\\\", \\\"Black Friday Mega Sale\\\"\\n- Include relevant keywords: \\\"customer feedback\\\", \\\"A/B test\\\", \\\"marketing copy\\\"\\n- For similarity comparisons, search broadly first to understand all campaigns\"\n      }\n    },\n    {\n      \"tool_spec\": {\n        \"type\": \"generic\",\n        \"name\": \"generate_campaign_report\",\n        \"description\": \"Generate a comprehensive HTML report for a specific campaign. The report includes all performance metrics, customer feedback, and key insights formatted for presentation.\\n\\n## When to Use\\n- User explicitly asks to \\\"generate a report\\\" for a campaign\\n- User asks for a \\\"comprehensive report\\\" or \\\"full report\\\"\\n- User wants a formatted document they can share or export\\n\\n## When NOT to Use\\n- User just wants to see metrics (use query_performance_metrics instead)\\n- User just wants feedback or content info (use search_campaign_content instead)\\n- User wants to compare multiple campaigns (use the other tools)\\n\\n## Required Input\\n- campaign_id (integer): The unique identifier of the campaign\\n\\n## How to Get campaign_id\\nBEFORE calling this tool, you MUST first use query_performance_metrics to look up the campaign_id by campaign_name. Example query: \\\"What is the campaign_id for the Holiday Gift Guide campaign?\\\"\\n\\n## Error Handling\\nIf this tool fails with an internal error, fall back to manually assembling the report by:\\n1. Using query_performance_metrics to get all performance data\\n2. Using search_campaign_content to get qualitative insights\\n3. Combining into a structured markdown response\",\n        \"input_schema\": {\n          \"type\": \"object\",\n          \"properties\": {\n            \"campaign_id\": {\n              \"type\": \"integer\",\n              \"description\": \"The unique identifier of the campaign to generate a report for. Get this by first querying for the campaign by name using query_performance_metrics.\"\n            }\n          },\n          \"required\": [\n            \"campaign_id\"\n          ]\n        }\n      }\n    }\n  ],\n  \"tool_resources\": {\n    \"query_performance_metrics\": {\n      \"execution_environment\": {\n        \"query_timeout\": 299,\n        \"type\": \"warehouse\",\n        \"warehouse\": \"COMPUTE_WH\"\n      },\n      \"semantic_view\": \"MARKETING_CAMPAIGNS_DB.AGENTS.MARKETING_PERFORMANCE_ANALYST\"\n    },\n    \"search_campaign_content\": {\n      \"execution_environment\": {\n        \"query_timeout\": 299,\n        \"type\": \"warehouse\",\n        \"warehouse\": \"COMPUTE_WH\"\n      },\n      \"search_service\": \"MARKETING_CAMPAIGNS_DB.AGENTS.MARKETING_CAMPAIGNS_SEARCH\"\n    },\n    \"generate_campaign_report\": {\n      \"type\": \"procedure\",\n      \"identifier\": \"MARKETING_CAMPAIGNS_DB.AGENTS.GENERATE_CAMPAIGN_REPORT_HTML\",\n      \"execution_environment\": {\n        \"type\": \"warehouse\",\n        \"warehouse\": \"COMPUTE_WH\",\n        \"query_timeout\": 300\n      }\n    }\n  }\n}\n$$;\n```\n\n**Using the UI:** Navigate to your agent in **AI & ML \u003E Agents** and click **Edit**. Update the orchestration and response instructions fields with the content from the specification above, then save.\n\n### Run Evaluation on the Improved Agent\n\nKick off a new evaluation run against the same dataset using the same YAML config:\n\n```sql\nCALL EXECUTE_AI_EVALUATION(\n  'START',\n  OBJECT_CONSTRUCT('run_name', 'OPTIMIZED_MARKETING_AGENT_EVAL_RUN'),\n  '@MARKETING_CAMPAIGNS_DB.AGENTS.EVAL_CONFIG_STAGE/marketing_campaign_eval_config.yaml'\n);\n```\n\nCheck the status:\n\n```sql\nCALL EXECUTE_AI_EVALUATION(\n  'STATUS',\n  OBJECT_CONSTRUCT('run_name', 'OPTIMIZED_MARKETING_AGENT_EVAL_RUN'),\n  '@MARKETING_CAMPAIGNS_DB.AGENTS.EVAL_CONFIG_STAGE/marketing_campaign_eval_config.yaml'\n);\n```\n\n**Using the UI:** Navigate to the **Evaluations** tab on your agent, click **Create New Evaluation**, name it (e.g., \"marketing-campaign-agent-improved-instructions\"), select **Existing Dataset** and reuse the same dataset, then enable the same metrics for an apples-to-apples comparison.\n\n### Analyze Comparison Results\n\nOnce both evaluations complete, you can compare results:\n\n**1.** View side-by-side metrics for both agent configurations\n\n**2.** Identify specific queries where the improved agent performed better\n\n**3.** Investigate cases where added orchestration and response instructions led to:\n\n- More correct answers\n- Better logical consistency\n- Improved custom metric scores (groundedness, execution efficiency, tool selection)\n\n### Key Questions to Answer\n\n- Which queries improved the most with the new configuration?\n- Are there patterns in the queries that still fail?\n- Did any queries regress with the changes?\n- What's the overall improvement in each metric?\n\n\u003C!-- ------------------------ --\u003E\n\n## Best Practices\n\n### Evaluation Dataset Design\n\n#### Diverse Query Coverage\n\n- Include queries across all expected use cases\n- Test edge cases and boundary conditions\n- Include queries that should trigger different tools\n\n#### Ground Truth Quality\n\n- Ensure expected outputs are accurate and complete\n- Review tool invocation expectations carefully\n- Update ground truth as requirements evolve\n\n#### Dataset Size\n\n- Start with 20-50 representative queries\n- Expand coverage based on failure analysis\n- Balance breadth and depth\n\n### Custom Metrics Design\n\n- Start with the built-in metrics and add custom metrics for domain-specific concerns\n- Keep scoring rubrics clear and unambiguous — the LLM judge needs concrete criteria\n- Use the `score_ranges` field to define meaningful thresholds (e.g., min/median/max)\n- Test your custom metric prompts on a few examples before running full evaluations\n- Leverage template variables to give the judge relevant context — `{{input}}`, `{{output}}`, and `{{ground_truth}}` for content comparison; `{{tool_info}}`, `{{duration}}`, `{{llm_model}}` for execution details\n\n### Iterative Improvement\n\n#### 1. Establish Baseline\n\n- Run initial evaluation on your current agent\n- Document baseline metrics across all dimensions\n\n#### 2. Identify Weaknesses\n\n- Analyze queries with low scores\n- Look for patterns in failures\n- Prioritize high-impact improvements\n\n#### 3. Make Targeted Changes\n\n- Modify one aspect at a time\n- Update orchestration or response instructions\n- Adjust tool configurations as needed\n\n#### 4. Measure Impact\n\n- Re-run evaluation with same dataset\n- Compare metrics to baseline\n- Document what worked and what didn't\n\n#### 5. Iterate\n\n- Repeat the process with new improvements\n- Track progress over time\n- Build a history of evaluation runs\n\n### Common Pitfalls to Avoid\n\n- **Overfitting to evaluation data**: Ensure your agent generalizes beyond test queries\n- **Ignoring regressions**: Watch for queries that get worse with changes\n- **Inconsistent ground truth**: Maintain quality standards across your dataset\n- **Skipping baseline**: Always establish a comparison point before making changes\n\n\u003C!-- ------------------------ --\u003E\n\n## Conclusion and Resources\n\nCongratulations! You've successfully set up and run Cortex Agent Evaluations. You now have the tools to systematically measure and improve your AI agents.\n\n### What You Learned\n\n- **Environment Setup**: How to configure sample agents and data for evaluation\n- **Evaluation Creation**: How to build datasets and run evaluations via the SQL API and Snowsight UI\n- **Custom Metrics**: How to define custom evaluation metrics via YAML with LLM-as-judge prompts\n- **Metrics Interpretation**: Understanding answer correctness, logical consistency, and custom metrics\n- **Agent Comparison**: How to compare configurations and identify improvements\n- **Dataset Building**: Using the Evalset Generator app for custom datasets\n\n### Next Steps\n\n- Evaluate your own production agents\n- Build comprehensive evaluation datasets covering your specific use cases\n- Design custom metrics tailored to your domain and quality requirements\n- Establish an evaluation workflow as part of your agent development process\n- Set up automated evaluation runs for continuous monitoring\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 Guide](https://docs.snowflake.com/user-guide/snowflake-cortex/cortex-agents)\n- [Getting Started with Cortex Agents](https://www.snowflake.com/en/developers/guides/getting-started-with-cortex-agents/)\n- [What's Your Agent's GPA? A Framework for Evaluating AI Agent Reliability](https://www.snowflake.com/en/engineering-blog/ai-agent-evaluation-gpa-framework/)\n\n### Repository\n\n- [sfguide-getting-started-with-cortex-agent-evaluations on GitHub](https://github.com/Snowflake-Labs/sfguide-getting-started-with-cortex-agent-evaluations)\n","title":"Quickstart Article Body","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"isDeveloperGuidesPage":false,"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-fbc64f1cd5","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":{"id":"container-65bae4ee32","layout":"SIMPLE",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-95e672f73a","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2026-03-17",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-eb5f71d389","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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