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and improve the agent's instructions, and then put the agent in the hands of business users with Snowflake CoWork.\u003C/p\u003E\n","\u003Cp\u003EThe agent answers questions about a fictional company's marketing campaigns. It combines:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ECortex Analyst\u003C/strong\u003E over a semantic view for campaign performance metrics (ROI, revenue, clicks, conversions)\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ECortex Search\u003C/strong\u003E over campaign content, A/B test notes and customer feedback\u003C/li\u003E\u003Cli\u003EAn \u003Cstrong\u003Eagent skill\u003C/strong\u003E (a \u003Ccode\u003ESKILL.md\u003C/code\u003E file on a stage) that generates HTML campaign reports\u003C/li\u003E\u003Cli\u003EThe \u003Cstrong\u003Ecode execution\u003C/strong\u003E sandbox, used by the skill to render reports\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EWeb search\u003C/strong\u003E, for external context such as industry reports and regulatory guidance\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to give a Cortex Agent an agent skill, code execution and web search\u003C/li\u003E\u003Cli\u003EHow to run Cortex Agent Evaluations with built-in and custom LLM-judge metrics\u003C/li\u003E\u003Cli\u003EHow to read evaluation traces, scores and judge explanations to find failure patterns\u003C/li\u003E\u003Cli\u003EHow to use CoCo to improve agent instructions and commit them as a new agent version\u003C/li\u003E\u003Cli\u003EHow to compare evaluation runs across agent versions\u003C/li\u003E\u003Cli\u003EHow business users interact with the agent in Snowflake CoWork, including Artifacts and Automations\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Need\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA Snowflake account with access to the \u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E role (the setup script creates a role, database, warehouse and an API integration, and sets an account parameter)\u003C/li\u003E\u003Cli\u003EAccess to Cortex Agents, Agent Evaluations, CoCo and Snowflake CoWork in your account's region\u003C/li\u003E\u003Cli\u003EBasic familiarity with SQL and Snowsight\u003C/li\u003E\u003Cli\u003EA browser for Snowsight and CoCo in Snowsight, or optionally the \u003Ca href=\"https://docs.snowflake.com/en/user-guide/cortex-code/cortex-code-cli\"\u003ECoCo CLI\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EAccount-level change:\u003C/strong\u003E The agent uses the web search tool, which requires \u003Ccode\u003EENABLE_CORTEX_WEBSEARCH = TRUE\u003C/code\u003E on the \u003Cstrong\u003Eentire account\u003C/strong\u003E. This lets any Cortex Agent, CoWork or CoCo user in the account with web search configured send queries to an external web search provider. Review this with your security and governance stakeholders before running the setup script. The script explains how to skip this step and how to revert it.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EWhat You'll Build\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA \u003Ccode\u003EMARKETING_AGENT\u003C/code\u003E Cortex Agent backed by a semantic view, a Cortex Search service and a campaign report skill\u003C/li\u003E\u003Cli\u003EA baseline evaluation run and an optimized agent version with a second evaluation run to compare against\u003C/li\u003E\u003Cli\u003ESaved Artifacts and a scheduled Automation in Snowflake CoWork\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ERun the Setup Script\u003C/h2\u003E\n","\u003Cp\u003EDownload \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-build-evaluate-and-deploy-ai-agents-with-snowflake-cowork/blob/main/SETUP.sql\"\u003ESETUP.sql\u003C/a\u003E and open it in a new SQL worksheet or workspace file in Snowsight. Review it, then run the entire script starting as \u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E. It takes roughly 5-10 minutes, including the first evaluation run.\u003C/p\u003E\n","\u003Cp\u003EThe script does the following:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ESection\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EWhat it creates\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E1. Database and schema\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESets the web search account parameter, then creates \u003Ccode\u003ECOWORK_AI_HOL_DB.AGENTS\u003C/code\u003E and the \u003Ccode\u003ECOWORK_AI_HOL_WH\u003C/code\u003E warehouse\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E2. Role configuration\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECreates \u003Ccode\u003ECOWORK_AI_HOL_ROLE\u003C/code\u003E, grants it to your user, and grants the privileges needed for Cortex, CoCo, datasets, tasks and evaluations\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E3. Git integration\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECreates an API integration and a Git repository object pointing at the lab's public GitHub repo\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E4-5. Tables\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ELoads campaigns, daily performance, content, feedback and the evaluation dataset from the repo's CSV files, then validates row counts\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E6. Semantic view\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EMARKETING_PERFORMANCE_ANALYST\u003C/code\u003E, used by the agent's Cortex Analyst tool\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E7. Cortex Search\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EMARKETING_CAMPAIGNS_SEARCH\u003C/code\u003E over campaign content and customer feedback\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E8. Agent skill\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EThe \u003Ccode\u003EAGENT_SKILLS\u003C/code\u003E stage containing \u003Ccode\u003Eskills/campaign-report/SKILL.md\u003C/code\u003E and a helper script\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E9. Tests\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESample queries against the semantic view and search service\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E10. Cortex Agent\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EMARKETING_AGENT\u003C/code\u003E with the Analyst, Search, code execution and web search tools plus the campaign-report skill, and its baseline version \u003Ccode\u003EVERSION$1\u003C/code\u003E (alias \u003Ccode\u003EBASELINE\u003C/code\u003E)\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E11-12. Evaluation\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAn evaluation dataset, an evaluation config on the \u003Ccode\u003EEVAL_CONFIG_STAGE\u003C/code\u003E stage, and the \u003Ccode\u003EBASELINE_MARKETING_AGENT_EVAL_RUN\u003C/code\u003E evaluation run\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003EFrom Section 3 onward the script switches to \u003Ccode\u003ECOWORK_AI_HOL_ROLE\u003C/code\u003E, which owns the agent and its tools. Use this role for the rest of the guide.\u003C/p\u003E\n","\u003Cp\u003EWhen the script completes, the final query returns a link to your agent's Evaluations tab.\u003C/p\u003E\n","\u003Ch3\u003ESet Your Default Role\u003C/h3\u003E\n","\u003Cp\u003ECoWork and other tools use your user's default role. Set it to the lab role so the agent and its tools are accessible everywhere:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESET MY_USER = CURRENT_USER();\nALTER USER IDENTIFIER($MY_USER) SET DEFAULT_ROLE = COWORK_AI_HOL_ROLE;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou can also switch roles in Snowsight from the user menu in the bottom-left corner.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EExplore Baseline Evaluations\u003C/h2\u003E\n","\u003Cp\u003EOpen the link returned at the end of the setup script, or navigate to \u003Cstrong\u003EAI & ML > Agents > MARKETING_AGENT\u003C/strong\u003E and select the \u003Cstrong\u003EEvaluations\u003C/strong\u003E tab. You should see \u003Ccode\u003EBASELINE_MARKETING_AGENT_EVAL_RUN\u003C/code\u003E with its aggregate scores.\u003C/p\u003E\n","\u003Cp\u003EThe evaluation measures six metrics:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EMetric\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EType\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EWhat it measures\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\"\u003EBuilt-in\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EHow well the final answer matches the expected answer\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ELogical consistency\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EBuilt-in\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EWhether the agent's reasoning and actions are consistent\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ETool selection accuracy\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EBuilt-in\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EWhether the agent called the expected tools, and only those\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ETool execution accuracy\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EBuilt-in\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EWhether tool calls were well-formed and returned the expected results\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EGroundedness\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECustom LLM judge\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EWhether each claim in the response is supported by tool outputs\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EExecution efficiency\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECustom LLM judge\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EWhether the agent took a minimal, non-redundant path to the answer\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003ESelect the run to see per-record scores. Your scores will differ from the example below.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-evaluate-and-deploy-ai-agents-with-snowflake-cowork/eval_runs.png?v=3951e692\" alt=\"Baseline evaluation run with per-record scores\"\u003E\u003C/p\u003E\n","\u003Cp\u003ESelect a record to see its trace alongside its scores. For each metric you can see the \u003Cstrong\u003Ecriteria\u003C/strong\u003E the judge used and the \u003Cstrong\u003Eexplanation\u003C/strong\u003E for the score. This combination of quantitative scores and qualitative reasoning is what lets you, and CoCo, find the root cause of low scores.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-evaluate-and-deploy-ai-agents-with-snowflake-cowork/eval_run_detail.png?v=3951e692\" alt=\"Record details showing trace, scores and judge criteria\"\u003E\u003C/p\u003E\n","\u003Cp\u003ELook for patterns, for example:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ERecords where the agent called the same tool several times after SQL or filter errors\u003C/li\u003E\u003Cli\u003EQuestions about external sources where the agent searched internal content first\u003C/li\u003E\u003Cli\u003EQualitative questions answered with purely quantitative analysis\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EOptimize Agent with CoCo\u003C/h2\u003E\n","\u003Cp\u003ENow use CoCo to analyze the evaluation results and improve the agent. CoCo includes \u003Ccode\u003Eagent-studio\u003C/code\u003E skills for reading agent specs, saving new versions and running evaluations.\u003C/p\u003E\n","\u003Ch3\u003EChoose Where to Run CoCo\u003C/h3\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003E \u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ECoCo in Snowsight\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ECoCo CLI\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EWhere it runs\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EIn your browser\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EOn your machine\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003ESetup\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ENone, it is built into Snowsight\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EInstall the CLI and configure a connection\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EBest for\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EThe quickest path through this guide\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EUsers who already use CoCo locally\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003EChoose one and use it for the rest of the guide.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ECoCo in Snowsight:\u003C/strong\u003E Switch to \u003Ccode\u003ECOWORK_AI_HOL_ROLE\u003C/code\u003E, then select the CoCo icon in the lower-right corner of Snowsight to open the CoCo panel. See \u003Ca href=\"https://docs.snowflake.com/en/user-guide/cortex-code/cortex-code-snowsight\"\u003ECoCo in Snowsight\u003C/a\u003E.\u003C/p\u003E\n","\u003Cp\u003EWhen CoCo asks \u003Cstrong\u003ERun this command?\u003C/strong\u003E, you can select \u003Cstrong\u003EAllow all commands in this session\u003C/strong\u003E to avoid approving each step.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-evaluate-and-deploy-ai-agents-with-snowflake-cowork/always_allow_commands.png?v=3951e692\" alt=\"Allow all commands in this session\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ECoCo CLI:\u003C/strong\u003E Install the CLI and create a connection whose role is \u003Ccode\u003ECOWORK_AI_HOL_ROLE\u003C/code\u003E, following \u003Ca href=\"https://docs.snowflake.com/en/user-guide/cortex-code/cortex-code-cli\"\u003ECoCo CLI\u003C/a\u003E:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Ecurl -LsS https://ai.snowflake.com/static/cc-scripts/install.sh | sh\ncortex\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EUse the lab role for every evaluation run.\u003C/strong\u003E The agent is owned by \u003Ccode\u003ECOWORK_AI_HOL_ROLE\u003C/code\u003E. If an evaluation is started under a role that has no \u003Ccode\u003EUSAGE\u003C/code\u003E on the agent (for example, a CLI connection that defaults to \u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E), the run fails with an "agent does not exist or access is not authorized" error. If that happens, ask CoCo to start the run from a session using \u003Ccode\u003ECOWORK_AI_HOL_ROLE\u003C/code\u003E.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EAnalyze Failures and Improve Instructions\u003C/h3\u003E\n","\u003Cp\u003EPaste the following prompt into CoCo:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-text\"\u003EImportant - make sure you use the COWORK_AI_HOL_ROLE as the role for all work in this session.\n\nLook at the results from my eval run BASELINE_MARKETING_AGENT_EVAL_RUN for my agent COWORK_AI_HOL_DB.AGENTS.MARKETING_AGENT and determine common failure patterns.\nBased on the failure analysis, generate improved orchestration and response instructions that fix the identified issues.\nThe instructions should tell the agent what format to respond in, when to use multiple tools and in what order, and encourage efficient tool calling.\nOnly make updates to the instructions - do not make any updates to the tool configuration or other areas of the agent spec.\nApply the changes and commit them to a new agent version. Use the agent-studio skill and associated subskills.\n\nWhen updating the agent, each instruction change must be saved as a new incremental version (`VERSION$2`, `VERSION$3`, etc.) - never overwrite a previous version.\nVerify with `SHOW VERSIONS IN AGENT COWORK_AI_HOL_DB.AGENTS.MARKETING_AGENT` before and after saving.\nEach eval run must target the specific new version by name in the eval YAML.\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ECoCo reads the evaluation traces and judge explanations, summarizes the failure patterns it finds, drafts new orchestration and response instructions, and commits them as \u003Ccode\u003EVERSION$2\u003C/code\u003E. Review its findings and the proposed instructions before approving the change.\u003C/p\u003E\n","\u003Cp\u003EYou can confirm the new version yourself:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESHOW VERSIONS IN AGENT COWORK_AI_HOL_DB.AGENTS.MARKETING_AGENT;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Ccode\u003EVERSION$1\u003C/code\u003E (alias \u003Ccode\u003EBASELINE\u003C/code\u003E) is unchanged; agent versions are immutable once committed. See \u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents-versioning\"\u003ECortex Agent versioning\u003C/a\u003E.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECompare Evaluation Runs\u003C/h2\u003E\n","\u003Cp\u003ENext, evaluate the new version against the same dataset and metrics. Paste this prompt into CoCo:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-text\"\u003ERun the evaluation of COWORK_AI_HOL_DB.AGENTS.MARKETING_AGENT against the same dataset again.\nUse the same eval config yaml just update VERSION$1 -> VERSION$2. No other changes.\nCompare the results against the baseline - show me a side-by-side comparison of scores by metric and highlight what improved.\nUse the agent-studio skill and associated subskills.\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EPinning the evaluation to \u003Ccode\u003EVERSION$2\u003C/code\u003E by name, with the dataset and metrics unchanged, means any difference in scores comes from the instruction changes alone.\u003C/p\u003E\n","\u003Cp\u003EWhen the run completes, CoCo shows a metric-by-metric comparison. You can also compare the runs in the agent's \u003Cstrong\u003EEvaluations\u003C/strong\u003E tab. Pay particular attention to tool selection accuracy and execution efficiency, which instruction changes about tool routing and redundant calls target most directly. Results vary between runs because the agent and the judges are both LLM-based.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ETest Agent in CoWork\u003C/h2\u003E\n","\u003Cp\u003ENow try the agent the way your business users will, in Snowflake CoWork.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EMake sure your default role is \u003Ccode\u003ECOWORK_AI_HOL_ROLE\u003C/code\u003E (see Run the Setup Script).\u003C/li\u003E\u003Cli\u003EOpen Snowflake CoWork and select \u003Cstrong\u003EMARKETING_AGENT\u003C/strong\u003E.\u003C/li\u003E\u003Cli\u003EAsk questions that exercise different tools:\n\u003Cul\u003E\u003Cli\u003E"Which channels yield the highest click-through rates?" (Cortex Analyst)\u003C/li\u003E\u003Cli\u003E"What was customer feedback around our back to school campaign?" (Cortex Search)\u003C/li\u003E\u003Cli\u003E"Generate a report for the holiday gift guide campaign" (campaign-report skill and code execution)\u003C/li\u003E\u003Cli\u003E"According to the FTC's Disclosures 101 for Social Media Influencers guide, what are the best practices for disclosing paid partnerships in our social media influencer campaigns?" (web search)\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003ETo give other users access, grant them \u003Ccode\u003EUSAGE\u003C/code\u003E on the agent and on the objects its tools use. See \u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/snowflake-cowork/deploy-agents\"\u003EUser access and settings for agents\u003C/a\u003E.\u003C/p\u003E\n","\u003Ch3\u003ETry Artifacts\u003C/h3\u003E\n","\u003Cp\u003EWhen a response includes a chart or table worth keeping, select \u003Cstrong\u003ESave\u003C/strong\u003E to turn it into an artifact. Artifacts are live references: they keep the underlying query and visualization, refresh against current data, and can be shared with teammates, who see results filtered by their own data permissions.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EAsk "Show me total revenue and average ROI by campaign type as a chart" and save the chart.\u003C/li\u003E\u003Cli\u003EOpen the artifacts hub and find it under \u003Cstrong\u003ESaved\u003C/strong\u003E, then open it and ask a follow-up question.\u003C/li\u003E\u003Cli\u003EOptionally, share the conversation with a teammate through a link.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003ESee \u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/snowflake-cowork/artifacts\"\u003EArtifacts in Snowflake CoWork\u003C/a\u003E.\u003C/p\u003E\n","\u003Ch3\u003ETry Automations\u003C/h3\u003E\n","\u003Cp\u003EAutomations turn a question into a recurring report delivered to your email, re-run against the latest data each time.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EAsk "Give me a weekly summary of our top 5 campaigns by ROI with key metrics and visualizations", then reply "Send me this report every Monday at 9 AM".\u003C/li\u003E\u003Cli\u003EOr create one manually from the \u003Cstrong\u003EAutomations\u003C/strong\u003E tab with a name, instructions and frequency.\u003C/li\u003E\u003Cli\u003EReview the automation in the \u003Cstrong\u003EAutomations\u003C/strong\u003E tab, and cancel it when you are done. Automations run on your compute.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003ESee \u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/snowflake-cowork/automations\"\u003EAutomations in Snowflake CoWork\u003C/a\u003E.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EOptional Stretch Goals\u003C/h2\u003E\n","\u003Cp\u003EIf you have time left, see how far you can push the agent's quality. Commit each change as a new agent version and evaluate it by version name so you can compare runs.\u003C/p\u003E\n","\u003Cp\u003EA few ideas to try:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EImprove the semantic view, for example with synonyms, descriptions or verified queries\u003C/li\u003E\u003Cli\u003ETry a different orchestration model\u003C/li\u003E\u003Cli\u003EEnrich the tool descriptions so the agent routes questions more accurately\u003C/li\u003E\u003Cli\u003ERefine the campaign-report skill's instructions\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EClean Up\u003C/h2\u003E\n","\u003Cp\u003ETo remove the lab objects, run the following as \u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E. Cancel any CoWork automations you created first.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE ACCOUNTADMIN;\n-- If you set COWORK_AI_HOL_ROLE as your default role, reset it before dropping the role.\n-- Replace <your_previous_role> with the role you used before this guide.\nSET MY_USER = CURRENT_USER();\nALTER USER IDENTIFIER($MY_USER) SET DEFAULT_ROLE = <your_previous_role>;\n\nDROP DATABASE IF EXISTS COWORK_AI_HOL_DB;\nDROP WAREHOUSE IF EXISTS COWORK_AI_HOL_WH;\nDROP API INTEGRATION IF EXISTS GIT_API_INTEGRATION_COWORK_AI_HOL;\nDROP ROLE IF EXISTS COWORK_AI_HOL_ROLE;\n\n-- Web search is an ACCOUNT-WIDE setting. Uncomment ONLY if it was not enabled\n-- before this guide and nothing else in the account relies on it.\n-- ALTER ACCOUNT UNSET ENABLE_CORTEX_WEBSEARCH;\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion And Resources\u003C/h2\u003E\n","\u003Cp\u003EYou built a Cortex Agent that combines structured analytics, unstructured search, an agent skill, code execution and web search. You measured it with Cortex Agent Evaluations, used CoCo to turn evaluation evidence into better instructions committed as a new agent version, verified the improvement with a second evaluation run, and deployed the agent to business users in Snowflake CoWork.\u003C/p\u003E\n","\u003Ch3\u003EWhat You Learned\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to package a repeatable workflow as an agent skill and attach it to a Cortex Agent\u003C/li\u003E\u003Cli\u003EHow to enable code execution and web search for an agent, and what the account-level web search setting implies\u003C/li\u003E\u003Cli\u003EHow to read evaluation scores, traces and judge explanations to find failure patterns\u003C/li\u003E\u003Cli\u003EHow to use CoCo and agent versioning to make and measure targeted instruction changes\u003C/li\u003E\u003Cli\u003EHow CoWork Artifacts and Automations extend an agent into everyday business workflows\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ERelated Resources\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-build-evaluate-and-deploy-ai-agents-with-snowflake-cowork\"\u003ELab code and data on GitHub\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents\"\u003ECortex Agents\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents-evaluations\"\u003ECortex Agent evaluations\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents-skills\"\u003EAgent skills\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents-code-execution-tool\"\u003ECortex Agent code execution tool\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents-manage\"\u003ECreate and manage agents\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/cortex-code/cortex-code\"\u003ECoCo\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/self-improving-agents-with-cortex-code/\"\u003ESelf-Improving Agents with CoCo\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E"],"description":"","title":"Build, Evaluate and Deploy AI Agents with Snowflake CoWork",":type":"snowflake-site/components/contentfragment",":items":{},":itemsOrder":[],"elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\u003C!-- ------------------------ --\u003E\n## Overview\n\nIn this guide you will build a marketing analytics agent on Snowflake, measure its quality with Cortex Agent Evaluations, use CoCo (Cortex Code) to analyze the evaluation results and improve the agent's instructions, and then put the agent in the hands of business users with Snowflake CoWork.\n\nThe agent answers questions about a fictional company's marketing campaigns. It combines:\n- **Cortex Analyst** over a semantic view for campaign performance metrics (ROI, revenue, clicks, conversions)\n- **Cortex Search** over campaign content, A/B test notes and customer feedback\n- An **agent skill** (a `SKILL.md` file on a stage) that generates HTML campaign reports\n- The **code execution** sandbox, used by the skill to render reports\n- **Web search**, for external context such as industry reports and regulatory guidance\n\n### What You'll Learn\n- How to give a Cortex Agent an agent skill, code execution and web search\n- How to run Cortex Agent Evaluations with built-in and custom LLM-judge metrics\n- How to read evaluation traces, scores and judge explanations to find failure patterns\n- How to use CoCo to improve agent instructions and commit them as a new agent version\n- How to compare evaluation runs across agent versions\n- How business users interact with the agent in Snowflake CoWork, including Artifacts and Automations\n\n### What You'll Need\n- A Snowflake account with access to the `ACCOUNTADMIN` role (the setup script creates a role, database, warehouse and an API integration, and sets an account parameter)\n- Access to Cortex Agents, Agent Evaluations, CoCo and Snowflake CoWork in your account's region\n- Basic familiarity with SQL and Snowsight\n- A browser for Snowsight and CoCo in Snowsight, or optionally the [CoCo CLI](https://docs.snowflake.com/en/user-guide/cortex-code/cortex-code-cli)\n\n\u003E **Account-level change:** The agent uses the web search tool, which requires `ENABLE_CORTEX_WEBSEARCH = TRUE` on the **entire account**. This lets any Cortex Agent, CoWork or CoCo user in the account with web search configured send queries to an external web search provider. Review this with your security and governance stakeholders before running the setup script. The script explains how to skip this step and how to revert it.\n\n### What You'll Build\n- A `MARKETING_AGENT` Cortex Agent backed by a semantic view, a Cortex Search service and a campaign report skill\n- A baseline evaluation run and an optimized agent version with a second evaluation run to compare against\n- Saved Artifacts and a scheduled Automation in Snowflake CoWork\n\n\u003C!-- ------------------------ --\u003E\n## Run the Setup Script\n\nDownload [SETUP.sql](https://github.com/Snowflake-Labs/sfguide-build-evaluate-and-deploy-ai-agents-with-snowflake-cowork/blob/main/SETUP.sql) and open it in a new SQL worksheet or workspace file in Snowsight. Review it, then run the entire script starting as `ACCOUNTADMIN`. It takes roughly 5-10 minutes, including the first evaluation run.\n\nThe script does the following:\n\n| Section | What it creates |\n|---|---|\n| 1. Database and schema | Sets the web search account parameter, then creates `COWORK_AI_HOL_DB.AGENTS` and the `COWORK_AI_HOL_WH` warehouse |\n| 2. Role configuration | Creates `COWORK_AI_HOL_ROLE`, grants it to your user, and grants the privileges needed for Cortex, CoCo, datasets, tasks and evaluations |\n| 3. Git integration | Creates an API integration and a Git repository object pointing at the lab's public GitHub repo |\n| 4-5. Tables | Loads campaigns, daily performance, content, feedback and the evaluation dataset from the repo's CSV files, then validates row counts |\n| 6. Semantic view | `MARKETING_PERFORMANCE_ANALYST`, used by the agent's Cortex Analyst tool |\n| 7. Cortex Search | `MARKETING_CAMPAIGNS_SEARCH` over campaign content and customer feedback |\n| 8. Agent skill | The `AGENT_SKILLS` stage containing `skills/campaign-report/SKILL.md` and a helper script |\n| 9. Tests | Sample queries against the semantic view and search service |\n| 10. Cortex Agent | `MARKETING_AGENT` with the Analyst, Search, code execution and web search tools plus the campaign-report skill, and its baseline version `VERSION$1` (alias `BASELINE`) |\n| 11-12. Evaluation | An evaluation dataset, an evaluation config on the `EVAL_CONFIG_STAGE` stage, and the `BASELINE_MARKETING_AGENT_EVAL_RUN` evaluation run |\n\nFrom Section 3 onward the script switches to `COWORK_AI_HOL_ROLE`, which owns the agent and its tools. Use this role for the rest of the guide.\n\nWhen the script completes, the final query returns a link to your agent's Evaluations tab.\n\n### Set Your Default Role\n\nCoWork and other tools use your user's default role. Set it to the lab role so the agent and its tools are accessible everywhere:\n\n```sql\nSET MY_USER = CURRENT_USER();\nALTER USER IDENTIFIER($MY_USER) SET DEFAULT_ROLE = COWORK_AI_HOL_ROLE;\n```\n\nYou can also switch roles in Snowsight from the user menu in the bottom-left corner.\n\n\u003C!-- ------------------------ --\u003E\n## Explore Baseline Evaluations\n\nOpen the link returned at the end of the setup script, or navigate to **AI & ML \u003E Agents \u003E MARKETING_AGENT** and select the **Evaluations** tab. You should see `BASELINE_MARKETING_AGENT_EVAL_RUN` with its aggregate scores.\n\nThe evaluation measures six metrics:\n\n| Metric | Type | What it measures |\n|---|---|---|\n| Answer correctness | Built-in | How well the final answer matches the expected answer |\n| Logical consistency | Built-in | Whether the agent's reasoning and actions are consistent |\n| Tool selection accuracy | Built-in | Whether the agent called the expected tools, and only those |\n| Tool execution accuracy | Built-in | Whether tool calls were well-formed and returned the expected results |\n| Groundedness | Custom LLM judge | Whether each claim in the response is supported by tool outputs |\n| Execution efficiency | Custom LLM judge | Whether the agent took a minimal, non-redundant path to the answer |\n\nSelect the run to see per-record scores. Your scores will differ from the example below.\n\n![Baseline evaluation run with per-record scores](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-evaluate-and-deploy-ai-agents-with-snowflake-cowork/eval_runs.png?v=3951e692)\n\nSelect a record to see its trace alongside its scores. For each metric you can see the **criteria** the judge used and the **explanation** for the score. This combination of quantitative scores and qualitative reasoning is what lets you, and CoCo, find the root cause of low scores.\n\n![Record details showing trace, scores and judge criteria](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-evaluate-and-deploy-ai-agents-with-snowflake-cowork/eval_run_detail.png?v=3951e692)\n\nLook for patterns, for example:\n- Records where the agent called the same tool several times after SQL or filter errors\n- Questions about external sources where the agent searched internal content first\n- Qualitative questions answered with purely quantitative analysis\n\n\u003C!-- ------------------------ --\u003E\n## Optimize Agent with CoCo\n\nNow use CoCo to analyze the evaluation results and improve the agent. CoCo includes `agent-studio` skills for reading agent specs, saving new versions and running evaluations.\n\n### Choose Where to Run CoCo\n\n| | CoCo in Snowsight | CoCo CLI |\n|---|---|---|\n| **Where it runs** | In your browser | On your machine |\n| **Setup** | None, it is built into Snowsight | Install the CLI and configure a connection |\n| **Best for** | The quickest path through this guide | Users who already use CoCo locally |\n\nChoose one and use it for the rest of the guide.\n\n**CoCo in Snowsight:** Switch to `COWORK_AI_HOL_ROLE`, then select the CoCo icon in the lower-right corner of Snowsight to open the CoCo panel. See [CoCo in Snowsight](https://docs.snowflake.com/en/user-guide/cortex-code/cortex-code-snowsight).\n\nWhen CoCo asks **Run this command?**, you can select **Allow all commands in this session** to avoid approving each step.\n\n![Allow all commands in this session](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-evaluate-and-deploy-ai-agents-with-snowflake-cowork/always_allow_commands.png?v=3951e692)\n\n**CoCo CLI:** Install the CLI and create a connection whose role is `COWORK_AI_HOL_ROLE`, following [CoCo CLI](https://docs.snowflake.com/en/user-guide/cortex-code/cortex-code-cli):\n\n```bash\ncurl -LsS https://ai.snowflake.com/static/cc-scripts/install.sh | sh\ncortex\n```\n\n\u003E **Use the lab role for every evaluation run.** The agent is owned by `COWORK_AI_HOL_ROLE`. If an evaluation is started under a role that has no `USAGE` on the agent (for example, a CLI connection that defaults to `ACCOUNTADMIN`), the run fails with an \"agent does not exist or access is not authorized\" error. If that happens, ask CoCo to start the run from a session using `COWORK_AI_HOL_ROLE`.\n\n### Analyze Failures and Improve Instructions\n\nPaste the following prompt into CoCo:\n\n```text\nImportant - make sure you use the COWORK_AI_HOL_ROLE as the role for all work in this session.\n\nLook at the results from my eval run BASELINE_MARKETING_AGENT_EVAL_RUN for my agent COWORK_AI_HOL_DB.AGENTS.MARKETING_AGENT and determine common failure patterns.\nBased on the failure analysis, generate improved orchestration and response instructions that fix the identified issues.\nThe instructions should tell the agent what format to respond in, when to use multiple tools and in what order, and encourage efficient tool calling.\nOnly make updates to the instructions - do not make any updates to the tool configuration or other areas of the agent spec.\nApply the changes and commit them to a new agent version. Use the agent-studio skill and associated subskills.\n\nWhen updating the agent, each instruction change must be saved as a new incremental version (`VERSION$2`, `VERSION$3`, etc.) - never overwrite a previous version.\nVerify with `SHOW VERSIONS IN AGENT COWORK_AI_HOL_DB.AGENTS.MARKETING_AGENT` before and after saving.\nEach eval run must target the specific new version by name in the eval YAML.\n```\n\nCoCo reads the evaluation traces and judge explanations, summarizes the failure patterns it finds, drafts new orchestration and response instructions, and commits them as `VERSION$2`. Review its findings and the proposed instructions before approving the change.\n\nYou can confirm the new version yourself:\n\n```sql\nSHOW VERSIONS IN AGENT COWORK_AI_HOL_DB.AGENTS.MARKETING_AGENT;\n```\n\n`VERSION$1` (alias `BASELINE`) is unchanged; agent versions are immutable once committed. See [Cortex Agent versioning](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents-versioning).\n\n\u003C!-- ------------------------ --\u003E\n## Compare Evaluation Runs\n\nNext, evaluate the new version against the same dataset and metrics. Paste this prompt into CoCo:\n\n```text\nRun the evaluation of COWORK_AI_HOL_DB.AGENTS.MARKETING_AGENT against the same dataset again.\nUse the same eval config yaml just update VERSION$1 -\u003E VERSION$2. No other changes.\nCompare the results against the baseline - show me a side-by-side comparison of scores by metric and highlight what improved.\nUse the agent-studio skill and associated subskills.\n```\n\nPinning the evaluation to `VERSION$2` by name, with the dataset and metrics unchanged, means any difference in scores comes from the instruction changes alone.\n\nWhen the run completes, CoCo shows a metric-by-metric comparison. You can also compare the runs in the agent's **Evaluations** tab. Pay particular attention to tool selection accuracy and execution efficiency, which instruction changes about tool routing and redundant calls target most directly. Results vary between runs because the agent and the judges are both LLM-based.\n\n\u003C!-- ------------------------ --\u003E\n## Test Agent in CoWork\n\nNow try the agent the way your business users will, in Snowflake CoWork.\n\n1. Make sure your default role is `COWORK_AI_HOL_ROLE` (see Run the Setup Script).\n2. Open Snowflake CoWork and select **MARKETING_AGENT**.\n3. Ask questions that exercise different tools:\n    - \"Which channels yield the highest click-through rates?\" (Cortex Analyst)\n    - \"What was customer feedback around our back to school campaign?\" (Cortex Search)\n    - \"Generate a report for the holiday gift guide campaign\" (campaign-report skill and code execution)\n    - \"According to the FTC's Disclosures 101 for Social Media Influencers guide, what are the best practices for disclosing paid partnerships in our social media influencer campaigns?\" (web search)\n\nTo give other users access, grant them `USAGE` on the agent and on the objects its tools use. See [User access and settings for agents](https://docs.snowflake.com/en/user-guide/snowflake-cortex/snowflake-cowork/deploy-agents).\n\n### Try Artifacts\n\nWhen a response includes a chart or table worth keeping, select **Save** to turn it into an artifact. Artifacts are live references: they keep the underlying query and visualization, refresh against current data, and can be shared with teammates, who see results filtered by their own data permissions.\n\n- Ask \"Show me total revenue and average ROI by campaign type as a chart\" and save the chart.\n- Open the artifacts hub and find it under **Saved**, then open it and ask a follow-up question.\n- Optionally, share the conversation with a teammate through a link.\n\nSee [Artifacts in Snowflake CoWork](https://docs.snowflake.com/en/user-guide/snowflake-cortex/snowflake-cowork/artifacts).\n\n### Try Automations\n\nAutomations turn a question into a recurring report delivered to your email, re-run against the latest data each time.\n\n- Ask \"Give me a weekly summary of our top 5 campaigns by ROI with key metrics and visualizations\", then reply \"Send me this report every Monday at 9 AM\".\n- Or create one manually from the **Automations** tab with a name, instructions and frequency.\n- Review the automation in the **Automations** tab, and cancel it when you are done. Automations run on your compute.\n\nSee [Automations in Snowflake CoWork](https://docs.snowflake.com/en/user-guide/snowflake-cortex/snowflake-cowork/automations).\n\n\u003C!-- ------------------------ --\u003E\n## Optional Stretch Goals\n\nIf you have time left, see how far you can push the agent's quality. Commit each change as a new agent version and evaluate it by version name so you can compare runs.\n\nA few ideas to try:\n- Improve the semantic view, for example with synonyms, descriptions or verified queries\n- Try a different orchestration model\n- Enrich the tool descriptions so the agent routes questions more accurately\n- Refine the campaign-report skill's instructions\n\n\u003C!-- ------------------------ --\u003E\n## Clean Up\n\nTo remove the lab objects, run the following as `ACCOUNTADMIN`. Cancel any CoWork automations you created first.\n\n```sql\nUSE ROLE ACCOUNTADMIN;\n-- If you set COWORK_AI_HOL_ROLE as your default role, reset it before dropping the role.\n-- Replace \u003Cyour_previous_role\u003E with the role you used before this guide.\nSET MY_USER = CURRENT_USER();\nALTER USER IDENTIFIER($MY_USER) SET DEFAULT_ROLE = \u003Cyour_previous_role\u003E;\n\nDROP DATABASE IF EXISTS COWORK_AI_HOL_DB;\nDROP WAREHOUSE IF EXISTS COWORK_AI_HOL_WH;\nDROP API INTEGRATION IF EXISTS GIT_API_INTEGRATION_COWORK_AI_HOL;\nDROP ROLE IF EXISTS COWORK_AI_HOL_ROLE;\n\n-- Web search is an ACCOUNT-WIDE setting. Uncomment ONLY if it was not enabled\n-- before this guide and nothing else in the account relies on it.\n-- ALTER ACCOUNT UNSET ENABLE_CORTEX_WEBSEARCH;\n```\n\n\u003C!-- ------------------------ --\u003E\n## Conclusion And Resources\n\nYou built a Cortex Agent that combines structured analytics, unstructured search, an agent skill, code execution and web search. You measured it with Cortex Agent Evaluations, used CoCo to turn evaluation evidence into better instructions committed as a new agent version, verified the improvement with a second evaluation run, and deployed the agent to business users in Snowflake CoWork.\n\n### What You Learned\n- How to package a repeatable workflow as an agent skill and attach it to a Cortex Agent\n- How to enable code execution and web search for an agent, and what the account-level web search setting implies\n- How to read evaluation scores, traces and judge explanations to find failure patterns\n- How to use CoCo and agent versioning to make and measure targeted instruction changes\n- How CoWork Artifacts and Automations extend an agent into everyday business workflows\n\n### Related Resources\n- [Lab code and data on GitHub](https://github.com/Snowflake-Labs/sfguide-build-evaluate-and-deploy-ai-agents-with-snowflake-cowork)\n- [Cortex Agents](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents)\n- [Cortex Agent evaluations](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents-evaluations)\n- [Agent skills](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents-skills)\n- [Cortex Agent code execution tool](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents-code-execution-tool)\n- [Create and manage agents](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents-manage)\n- [CoCo](https://docs.snowflake.com/en/user-guide/cortex-code/cortex-code)\n- [Getting Started with Cortex Agent Evaluations](https://www.snowflake.com/en/developers/guides/getting-started-with-cortex-agent-evaluations/)\n- [Self-Improving Agents with CoCo](https://www.snowflake.com/en/developers/guides/self-improving-agents-with-cortex-code/)\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-1dcbf51abb","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-25fc7f4c44",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-3299e27c4a","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2026-10-23",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-ae0a274178","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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