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Instead of pre-defined dashboards or static queries, agents reason through tasks, choose the right tools, and deliver results in natural language or take actions on your behalf.\u003C/p\u003E\n","\u003Cp\u003EYou can create, update, and deploy these high-quality agents directly inside your Snowflake environment. Snowflake Agents integrate directly with \u003Ca href=\"https://ai.snowflake.com/\"\u003ESnowflake CoWork\u003C/a\u003E with governance, observability, and performance built in.\u003C/p\u003E\n","\u003Cp\u003EThis guide is your map to building high-quality Cortex Agents for use with Snowflake CoWork, from idea to production, including links to deeper resources, examples, and tutorials along the way.\u003C/p\u003E\n","\u003Ch3\u003EWhat you'll learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow Snowflake CoWork and Cortex Agents work together.\u003C/li\u003E\u003Cli\u003EHow to define agent purpose and scope.\u003C/li\u003E\u003Cli\u003EHow to configure orchestration and response instructions.\u003C/li\u003E\u003Cli\u003EHow to design effective tools for Cortex Agents.\u003C/li\u003E\u003Cli\u003EHow to use agent versioning to manage your deployment lifecycle.\u003C/li\u003E\u003Cli\u003EHow to evaluate and monitor agent performance.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EImportant:\u003C/strong\u003E Before building Cortex Agents, \u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/snowflake-intelligence#set-up-sf-intelligence\"\u003Econfigure your permissions\u003C/a\u003E and make sure that you have \u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/snowflake-intelligence#supported-models-and-regions\"\u003Eaccess to the right models\u003C/a\u003E.\u003C/p\u003E\n","\u003Ch2\u003EHow Snowflake CoWork works\u003C/h2\u003E\n","\u003Cp\u003ECortex Agents power the reasoning behind Snowflake CoWork, turning natural language into governed actions and answers.\u003C/p\u003E\n","\u003Cp\u003ECortex Agents combine reasoning from large language models with Snowflake’s governance, data access, and observability layers to deliver accurate, explainable answers. When a user asks a question in Snowflake CoWork, it uses \u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents\"\u003ECortex Agents\u003C/a\u003E under the hood with the following stages.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\u003Cstrong\u003EUser input:\u003C/strong\u003E A user submits a natural-language question. For example, \u003Cem\u003E“How are Q4 sales trending?”\u003C/em\u003E.\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents-rest-api\"\u003E\u003Cstrong\u003ECortex Agent API\u003C/strong\u003E\u003C/a\u003E: The question is routed to the \u003Cstrong\u003ECortex Agent API\u003C/strong\u003E, which powers Snowflake CoWork.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EOrchestration:\u003C/strong\u003E The orchestrator (an LLM) interprets intent, selects the right tools, and plans the sequence of actions. It may use one tool, chain several together, or decide that the question is out of scope.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ETool execution:\u003C/strong\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-analyst\"\u003ECortex Analyst\u003C/a\u003E: Write and run SQL on your semantic views for structured data.\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-search/cortex-search-overview\"\u003ECortex Search\u003C/a\u003E: Retrieve relevant document text for unstructured data.\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/LIMITEDACCESS/cortex-agents-code-interpreter\"\u003ECode Execution\u003C/a\u003E: Generate and run Python code in a sandboxed environment.\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents#web-search\"\u003EWeb Search\u003C/a\u003E: Query the web for real-time information.\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/LIMITEDACCESS/snowflake-cortex/mcp-connectors\"\u003EMCP Connectors\u003C/a\u003E: Connect to external SaaS tools via the Model Context Protocol.\u003C/li\u003E\u003Cli\u003ECustom Tools: Execute user-defined functions or stored procedures for actions.\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EReflection & response:\u003C/strong\u003E The orchestrator reviews results, refines if needed, and generates the final answer (including summaries, tables, or charts) shown in the Snowflake CoWork UI.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EThe following image describes this structure of Snowflake CoWork.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/best-practices-to-building-cortex-agents/snowflake-cowork-architecture.png?v=9baa5992\" alt=\"Snowflake CoWork\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cem\u003E👉 Read the blog to learn more about \u003Ca href=\"https://www.snowflake.com/en/engineering-blog/inside-snowflake-intelligence-enterprise-agentic-ai/\"\u003Ehow Snowflake CoWork orchestration works\u003C/a\u003E\u003C/em\u003E\u003C/p\u003E\n","\u003Ch2\u003EBuilding Cortex Agents\u003C/h2\u003E\n","\u003Cp\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents\"\u003ECortex Agents\u003C/a\u003E are configurable reasoning systems that combine Snowflake’s built-in intelligence with your domain context.\u003C/p\u003E\n","\u003Cp\u003EYou can build and run agents in several ways:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\u003Cstrong\u003EAgent UI in Snowsight:\u003C/strong\u003E An interactive interface that handles identity, access control, and monitoring out of the box.\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents-rest-api\"\u003E\u003Cstrong\u003ECortex Agent API\u003C/strong\u003E\u003C/a\u003E: A REST API for integrating agents into your own applications (like Streamlit apps or custom apps).\u003C/li\u003E\u003C/ol\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPro tip:\u003C/strong\u003E Build agents using natural language with Snowflake's AI coding agent \u003Ca href=\"https://www.snowflake.com/en/developers/guides/best-practices-cortex-code-cli/#production-ready-cortex-agents\"\u003E\u003Cstrong\u003ECortex Code\u003C/strong\u003E\u003C/a\u003E.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EConsider the following when building an agent.\u003C/p\u003E\n","\u003Ch3\u003EDefine your agent's purpose\u003C/h3\u003E\n","\u003Cp\u003EEvery great agent starts with a clear purpose. Before adding tools or writing instructions, define why the agent exists, who it serves, and what specific questions it should answer. This step shapes everything that follows, from tool selection to performance and trust.\u003C/p\u003E\n","\u003Cp\u003EStart with an end user, and think through what they would actually want: \u003Cem\u003Ewhat specific job is this agent meant to do, and for whom? If they had 24/7 access to a data analyst who reads incredibly quickly and has single-digit minute response times, what would they ask of them?\u003C/em\u003E\u003C/p\u003E\n","\u003Ch3\u003EFavor narrowly-scoped specialized agents\u003C/h3\u003E\n","\u003Cp\u003EDon’t boil the ocean with a generalist agent. Start narrow with a specific, high-value use case. After an agent proves reliable in one area, you can replicate the pattern for others.\u003C/p\u003E\n","\u003Cp\u003EFor example, you could have the following agents:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EOne agent that analyzes your Shopify store’s recent sales and marketing data.\u003C/li\u003E\u003Cli\u003EOne agent that sales can use to recommend the best SKUs to pitch to the retailer.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cem\u003E👉\u003C/em\u003E\u003Ca href=\"https://medium.com/@JamesChaEarley/356b8566d114\"\u003E\u003Cem\u003ERead more about why single agents yield the best\nresults\u003C/em\u003E\u003C/a\u003E\u003C/p\u003E\n","\u003Ch3\u003EMap key use cases to tools\u003C/h3\u003E\n","\u003Cp\u003ETo get high-value, narrow use cases, partner with business stakeholders to identify the top 20 most important questions that they need answered. Use these questions as the initial scope for your agent.\u003C/p\u003E\n","\u003Cp\u003EIf you were to answer that question using a set of documents or data, what would you use? Would you use the sales table, read a few Google docs (which ones?), or look up support tickets?\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EHow many tools should a single agent have?\u003C/strong\u003E\nAn agent should have access to exactly as many tools as it needs to fulfill its predefined, targeted purpose. In the previous guidance, you wrote down exactly what you needed to answer each question. This becomes the list of tools your agent needs access to.\u003C/p\u003E\n","\u003Cp\u003EFor example, if you needed to write one set of SQL statements about your Shopify data, then read a Google doc, and finally read some support tickets when answering your question, your agent needs at least 3 separate components:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EA semantic view for your Shopify data\u003C/li\u003E\u003Cli\u003EA Cortex Search service to read your Google docs\u003C/li\u003E\u003Cli\u003EA Cortex Search service to read your support tickets\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cem\u003E👉\u003Ca href=\"https://medium.com/snowflake/how-to-make-useful-data-science-agents-dbacbf1643b8\"\u003ELessons learned building agents from our data science\nteam\u003C/a\u003E\u003C/em\u003E\u003C/p\u003E\n","\u003Ch2\u003EImportance of Cortex Agent instructions\u003C/h2\u003E\n","\u003Cp\u003EA well-written agent will run efficiently and reliably: calling the right tools, producing explainable results, and reflecting your business logic. Bad or incomplete instructions lead to missteps in reasoning, incorrect data retrieval, and wasted compute cost.\u003C/p\u003E\n","\u003Cp\u003EEvery Cortex Agent combines your custom instructions with Snowflake’s built-in base system instructions. These base instructions inform general workflows for tool usage, data analysis patterns, validation, visualization, citation, and safety guardrails.\u003C/p\u003E\n","\u003Cp\u003EYou \u003Cstrong\u003Ewon’t\u003C/strong\u003E need to further instruct the agent on this base functionality. For example:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E❌ DON'T include:\n"When you receive a question, first analyze it carefully, then\nselect appropriate tools, call them in sequence, and format results properly..."\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYour custom agent instructions are configured in 4 key layers, each playing a specific role to define how the agent reasons and responds with domain-specific context, rules, and workflows.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/views-semantic/overview#why-use-semantic-views\"\u003E\u003Cstrong\u003ESemantic views\u003C/strong\u003E\u003C/a\u003E are configured inside your data layer. They act as translators, or “cheat sheets” between your raw, structured data and how humans or AI interpret it.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EOrchestration instructions\u003C/strong\u003E are configured high-level business logic, rules, and multi-step workflows. These instruct the agent on how to approach answering a question.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EResponse instructions\u003C/strong\u003E control the final output format, tone, and communication style of the agent.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ETool descriptions\u003C/strong\u003E explain precisely what a tool does, which data it accesses, when to use it, and when \u003Cem\u003Enot\u003C/em\u003E to use it. \u003Cem\u003EThis is the most critical factor for accurate tool selection.\u003C/em\u003E\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EWe’ll go into more detail for each instruction layer in the following sections.\u003C/p\u003E\n","\u003Ch2\u003ESemantic views (data level)\u003C/h2\u003E\n","\u003Cp\u003EEach \u003Ca href=\"https://docs.snowflake.com/en/user-guide/views-semantic/overview\"\u003Esemantic view\u003C/a\u003E should cover a similar set of tables, and is instructions that tell the agent how to query or interpret the data. This is where you want to set data specific defaults, such as always adding a date filter for the past three months if not specified or always excluding internal accounts.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EResources for semantic views:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E🎥 \u003Ca href=\"https://www.snowflake.com/en/webinars/virtual-hands-on-lab/from-sql-to-agentic-analytics-building-a-semantic-layer-with-ai-to-empower-snowflake-intelligence-2025-11-20/\"\u003EWatch the hands-on lab\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E📖 \u003Ca href=\"https://medium.com/snowflake/getting-started-with-snowflake-semantic-view-7eced29abe6f\"\u003EGet started with semantic views\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch2\u003EOrchestration instructions (agent level)\u003C/h2\u003E\n","\u003Cp\u003EOrchestration instructions could include:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EYour agent’s identity and narrow scope\u003C/strong\u003E prevents scope creep and helps the agent stay focused on its intended purpose.\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode\u003E✅ ORCHESTRATION INSTRUCTION\n\nYour Role: You are "SalesBot", a sales intelligence assistant for the Snowflake sales team. Your Scope: You answer questions about customer accounts, pipeline opportunities, deal history, and product usage. You help sales professionals prepare for customer meetings and track account health. \n\nYour Users: Account Executives (AEs), Solution Engineers (SEs), and Sales Leaders who need quick access to customer data and insights. \n\u003C/code\u003E\u003C/pre\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EDomain context\u003C/strong\u003E helps the agent interpret questions correctly and use appropriate terminology.\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode\u003E✅ ORCHESTRATION INSTRUCTION\n\nDomain Context \n- Snowflake uses a "consumption-based" pricing model where customers pay for compute (measured in credits) and storage separately.\n- An "opportunity" represents a potential deal tracked in Salesforce with stages: Prospecting → Qualification → Proof of Value → Negotiation → Closed Won/Lost\n- "ARR" (Annual Recurring Revenue) is the key metric for subscription value\n- Our fiscal year runs Feb 1 - Jan 31\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EExplicit tool selection logic\u003C/strong\u003E to prevent the agent from choosing the wrong tool and improve consistency.\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode\u003E✅ ORCHESTRATION INSTRUCTION\n\nTool Selection Guidelines:\n- For questions about CURRENT customer data (accounts, usage, credits): Use the "CustomerData" tool.\n    For example: "What's Acme Corp's credit usage?", "Show me active accounts"\n- For questions about HISTORICAL trends and analytics: Use the "Analytics" tool.\n    For example: "How has consumption grown over time?", "Compare Q1 vs Q2"\n- For questions about sales pipeline and opportunities: Use the "SalesforcePipeline" tool.\n    For example: "What deals are closing this quarter?", "Show me open opportunities"\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EBoundaries and limitations\u003C/strong\u003E prevent hallucinations and inappropriate responses. Users will inevitably ask questions outside your agent's scope.\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode\u003E✅ ORCHESTRATION INSTRUCTION\n\nLimitations and Boundaries:\n- You do NOT have access to customer contracts or legal agreements.\n    If asked, respond: "I don't have access to contract details. Please contact Legal."\n- You do NOT have real-time data. Your data is refreshed daily at 2 AM UTC.\n    If asked about "right now", clarify: "My data is current as of this morning's refresh."\n- Do NOT calculate financial forecasts or make predictions about future revenue.\n    You can show historical trends but should not extrapolate future values.\n- Do NOT provide customer contact information (emails, phone numbers) for privacy reasons.\n\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EBusiness rules and conditional logic\u003C/strong\u003E to ensure consistent handling of common scenarios, edge cases, and error conditions.\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode\u003E✅ ORCHESTRATION INSTRUCTION\n\nBusiness Rules:\n- When a user asks about a customer by name (not ID), ALWAYS use\nCustomerLookup tool first to get the customer_id before calling other tools\n\n- If a query result returns more than 100 rows, ALWAYS aggregate or\nfilter the data before presenting. Do NOT display all rows.\n\n- For any consumption questions about dates within the last 7 days,\nremind users that data has a 24-hour delay and today's data is not yet\navailable\n\n- When multiple regions match a query, ALWAYS ask for clarification\nrather than assuming which region the user meant\n\n- If a tool returns an error code "INSUFFICIENT_PERMISSIONS", respond\nwith: "You don't have access to this data. Please contact your Snowflake admin to request access."\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EDomain-specific workflows\u003C/strong\u003E to deliver consistency and reduce the need for users to ask complex multi-part questions.\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode\u003E✅ ORCHESTRATION INSTRUCTION\n\nAccount Summary Workflow:\n\nWhen a user asks to "summarize my accounts" or "give me a book of\nbusiness update":\n\n1. Use CustomerData tool to get the user's assigned accounts list\n2. Use Analytics tool to show each account's':\n    - Last 90-day consumption and growth rate\n    - Total ARR and change from last quarter\n3. Use SalesforcePipeline tool to show:\n    - Top 5 open opportunities by value\n    - Any opportunities closing in next 30 days\n4. Use SupportTickets tool to flag any critical severity tickets in last 7 days\n\nPresent results in tables with clear sections.\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003EResponse instructions (agent level)\u003C/h2\u003E\n","\u003Cp\u003EThese instructions control the final output format, tone, and communication style of the agent. Examples include:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ETone and communication style:\u003C/strong\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode\u003E✅ RESPONSE INSTRUCTION\n\nResponse Style:\n    - Be concise and professional - sales teams are busy\n    - Lead with the direct answer, then provide supporting details\n    - Be direct with data. Avoid hedging language like "it seems" or "it appears"\n    - Use active voice and clear statements\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EData presentation formats\u003C/strong\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode\u003E✅ RESPONSE INSTRUCTION\n\nData Presentation:\n    - Use tables for multi-row data (\\>3 items)\n    - Use charts for comparisons, trends, and rankings\n    - For single values, state them directly without tables\n    - Always include units (credits, dollars, %) with numbers\n    - Include data freshness timestamp in responses\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EResponse structure templates\u003C/strong\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode\u003E✅ RESPONSE INSTRUCTION\n\nResponse Structure:\n\nFor "What is X?" questions:\n    - Lead with direct answer\n    - Follow with supporting context if relevant\n\n    Response example: "Acme Corp used 12,450 credits last month (up 8% from September)."\n\nFor "Show me X" questions:\n    - Brief summary sentence\n    - Table or chart with data\n    - Key insights or highlights\n\n    Response example: "You have $2.4M in open Q4 pipeline across 12 opportunities. \\[table\\]"\n\nFor "Compare X and Y" questions:\n    - Summary of comparison result\n    - Chart showing comparison visually\n    - Notable differences highlighted\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EError and edge case messaging\u003C/strong\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode\u003E✅ RESPONSE INSTRUCTION\n\nError Handling:\n- When data is unavailable: "I don't have access to \\[data type\\]. You can find this information in \\[alternative source\\] or contact \\[team\\]."\n- When query is ambiguous: "To provide accurate data, I need clarification: \\[specific question\\]. Did you mean \\[option A\\] or \\[option B\\]?"\n- When results are empty: "No results found for \\[criteria\\]. This could mean \\[possible reason\\]. Would you like to try \\[alternative approach\\]?"\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003EBest practices between orchestration and response instructions\u003C/h2\u003E\n","\u003Cp\u003EIt’s important to separate orchestration (what to do, which tools) and response (how to format, tone) into distinct instruction settings. Don’t combine tool selection logic with response formatting in the same section.\u003C/p\u003E\n","\u003Cp\u003ETo help categorize where instructions should live, ask yourself:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EDoes this instruction affect...\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EPut it in...\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EExample\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EWhich tool to select\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EOrchestration\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E"Use CustomerData for current metrics"\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EWhat data to retrieve\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EOrchestration\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E"Include last 90 days of usage data"\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EHow to interpret user intent\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EOrchestration\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E"When user says 'recent', use last 30 days"\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EHow to sequence tool calls\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EOrchestration\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E"Always call CustomerLookup before CustomerMetrics"\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EConditional logic and rules\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EOrchestration\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E"If result > 100 rows, aggregate before displaying"\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EWhat to do in specific scenarios\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EOrchestration\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E"When error code X occurs, try alternative tool Y"\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EHow to format the answer\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EResponse\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E"Use tables for multi-row results"\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EWhat tone to use\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EResponse\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E"Be concise and professional"\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EHow to structure text\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EResponse\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E"Lead with direct answer, then details"\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EWhat to say when errors occur\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EResponse\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E"Explain limitation and suggest alternatives"\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Ch2\u003ETool descriptions (agent level)\u003C/h2\u003E\n","\u003Cp\u003EThese describe to the agent what types of things the tool (Semantic View, Search Service, or Custom Tool) can do, so it can infer when it would be best to call it.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETool descriptions are often the culprit for most agent quality problems.\u003C/strong\u003E Agents choose tools based on name and description context, so make them obvious. Bad tool descriptions create cascading failures, and can lead to downstream hallucinations.\u003C/p\u003E\n","\u003Cp\u003EWhile instructions set the agent's identity and scope, tool descriptions directly govern:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\u003Cstrong\u003ETool selection accuracy\u003C/strong\u003E: Whether the agent picks the right tool for each question.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EParameter usage\u003C/strong\u003E: Whether the agent provides correct inputs to tools.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EError prevention\u003C/strong\u003E: Whether the agent avoids misusing tools or making invalid calls.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EConsistency\u003C/strong\u003E: Whether the agent behaves predictably across similar questions.\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003EStep 1: Start with a clear, specific tool name\u003C/h3\u003E\n","\u003Cp\u003ETool names are loaded into the agent's context and influence selection.\u003C/p\u003E\n","\u003Cp\u003ETip: Combine a \u003Cem\u003Edomain\u003C/em\u003E (“Customer”, “Sales”) with a \u003Cem\u003Efunction\u003C/em\u003E (“Analytics”, “Search”) to make each tool unambiguous.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E✅ GOOD: "CustomerConsumptionAnalytics"\n❌ BAD: "DataTool" or "Tool1"\n\n✅ GOOD: "SalesforcePipelineQuery"\n❌ BAD: "Query" or "SalesData"\n\n✅ GOOD: "ProductDocumentationSearch"\n❌ BAD: "Search" or "Docs"\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStep 2: Write a purpose-driven tool description\u003C/h3\u003E\n","\u003Cp\u003EA strong description tells the agent:\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E[What the tool does] + [What data it accesses] + [When to use it] + [When NOT to use it]\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003E[What data it accesses] refers to what’s in your \u003Ca href=\"#semantic-views-data-level\"\u003Esemantic view\u003C/a\u003E.\u003C/strong\u003E Include a concise summary of what’s in your semantic view. The agent first chooses tools based on their descriptions, not by inspecting your full data model.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003E"When NOT to Use" is critical.\u003C/strong\u003E Without it, agents will try to use tools for everything remotely related. "When NOT to Use" creates clear boundaries and redirects the agent to appropriate alternatives.\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode\u003E✅ GOOD EXAMPLE\n\nName: CustomerConsumptionAnalytics\n\nDescription: Analyzes Snowflake consumption metrics for customer accounts including credit usage,compute hours, and storage.\n\nData Coverage: Daily aggregated consumption data for all commercial\ncustomers, updated nightly. Includes data from the past 2 years.\n\nWhen to Use:\n    - Questions about customer usage patterns, trends, or growth\n    - Queries about specific customers' consumption (e.g., "How much did Acme use?")\n    - Comparisons between time periods (e.g., "Compare Q1 vs Q2 usage")\n\nWhen NOT to Use:\n    - Do NOT use for real-time/current-hour data (data is daily batch, not real-time)\n    - Do NOT use for trial or non-commercial accounts (not included in dataset)\n    - Do NOT use for individual query performance (use QueryHistory tool instead)\n\nKey Parameters:\n    - customer_name: Exact customer name (case-sensitive).\n        Use CustomerList tool first if unsure of exact spelling.\n    - date_range: ISO format dates (YYYY-MM-DD). Required.\n        Use specific dates, not relative terms like "last month".\n    - metric: One of: 'credits', 'compute_hours', 'storage_tb'\n\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cpre\u003E\u003Ccode\u003E❌ BAD EXAMPLE:\nName: ConsumptionTool\nDescription: Gets consumption data.\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStep 3: Be explicit about tool inputs\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EThis is where most tool descriptions fail.\u003C/strong\u003E Ambiguous inputs to your tools lead to incorrect tool calls and errors, whether Cortex Analyst, Cortex Search, or custom tools.\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003ECommon pitfalls\u003C/strong\u003E\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003ERecommendation\u003C/strong\u003E\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EUsing a generic name\u003C/strong\u003E<br>user vs user_id vs username\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EBe specific\u003C/strong\u003E<br>salesforce_user_id (18-char ID) vs user_email (email string)\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EUnclear data formats\u003C/strong\u003E<br>"date" vs "ISO 8601 date (YYYY-MM-DD)"\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003ESpecify data format\u003C/strong\u003E<br>Agents may pass "last month", "Q3", or other invalid formats\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003ENot explaining how to obtain IDs\u003C/strong\u003E<br>"Provide customer_id"\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EProvide clear data instructions\u003C/strong\u003E<br>"Customer ID from CustomerLookup tool, or directly from user if known"\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EUnclear optionality\u003C/strong\u003E<br>"region (optional)"\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EProvide default guidance\u003C/strong\u003E<br>"region (optional, defaults to 'ALL', returns data for all regions)"\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EUsing inconsistent terminology\u003C/strong\u003E<br>Pick one term and use it consistently everywhere.<br>Instructions say "customers" but tool descriptions say "accounts"\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EUse consistent terminology\u003C/strong\u003E<br>If your domain has multiple terms for the same concept, define them explicitly:<br>"Account (also called 'customer' in billing context): A business entity that..."\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Ch2\u003EUsing Tools\u003C/h2\u003E\n","\u003Cp\u003ECortex Agents support a rich set of built-in tools: \u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-analyst\"\u003ECortex Analyst\u003C/a\u003E for text-to-SQL, \u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-search/cortex-search-overview\"\u003ECortex Search\u003C/a\u003E for document retrieval, \u003Ca href=\"https://docs.snowflake.com/en/LIMITEDACCESS/cortex-agents-code-interpreter\"\u003Ecode execution\u003C/a\u003E for sandboxed Python, \u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents#web-search\"\u003Eweb search\u003C/a\u003E for real-time information, and \u003Ca href=\"https://docs.snowflake.com/en/LIMITEDACCESS/snowflake-cortex/mcp-connectors\"\u003EMCP connectors\u003C/a\u003E for integrating with external SaaS tools.\u003C/p\u003E\n","\u003Ch3\u003ECortex Analyst (Text-to-SQL)\u003C/h3\u003E\n","\u003Cp\u003ECortex Analyst accepts natural language queries and converts them to SQL. Your description must guide the agent on how to phrase queries effectively.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EStart with "Generate with Cortex"\u003C/strong\u003E in the Admin UI to automatically generate a tool description based on your semantic model. This provides a solid baseline that already includes key information about your data.\u003C/p\u003E\n","\u003Cp\u003E<img src="assets/semantic-view-generate-with-cortex.png" />\u003C/p\u003E\n","\u003Cp\u003EThen, enhance the auto-generated description by following the previously described principles.\u003C/p\u003E\n","\u003Ch3\u003ECortex Search\u003C/h3\u003E\n","\u003Cp\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-search/cortex-search-overview\"\u003ECortex Search\u003C/a\u003E services retrieve relevant documents and records using semantic search. The two primary use cases for Cortex Search are retrieval augmented generation (RAG) and enterprise search.\u003C/p\u003E\n","\u003Cp\u003EFor example, one of the first demo agents built inside of Snowflake used the following Cortex Search Service to answer questions about internal product documentation and architecture.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E✅ GOOD EXAMPLE\n\nName: ProductDocumentationSearch\n\nType: Cortex Search Service\n\nDescription:\nSearches internal product documentation, feature announcements,\ntechnical guides, and release notes to answer "what" and "how" questions\nabout Snowflake products. Uses semantic search to find relevant\ndocuments even when exact keywords don't match.\n\nData Sources:\n    - Product documentation (updated weekly)\n    - Feature release notes (updated with each release)\n    - Technical architecture guides (updated quarterly)\n    - Best practice documents (updated monthly)\n    - Last indexed: Timestamp included in each search result\n\nWhen to Use:\n    - Questions about product features, capabilities, or specifications\n    - "How to" questions and configuration instructions\n    - Feature availability and compatibility questions\n    - Troubleshooting guidance and best practices\n\nWhen NOT to Use:\n    - Customer-specific data or usage (use CustomerMetrics instead)\n    - Sales/pipeline information (use SalesforcePipeline instead)\n    - Real-time system status (use HealthMonitor instead)\n    - Questions requiring computation or data aggregation (use Cortex Analyst tools)\n\nSearch Query Best Practices:\n    1. Use specific product names:\n        ✅ "Snowflake Streams change data capture"\n        ❌ "streams" (too generic)\n    \n    2. Include multiple related keywords:\n        ✅ "security authentication SSO SAML configuration"\n        ❌ "security" (too broad)\n\n    3. Use technical terms when appropriate:\n        ✅ "materialized view incremental refresh performance"\n        ❌ "fast views" (colloquial)\n\n    4. If first search returns low relevance, rephrase: Try synonyms, expand acronyms, add context.\n    \nExample usage:\n\nScenario 1: Feature explanation\n    - User Question: "How do Snowflake Streams work?"\n    - Search Query: "Snowflake Streams change data capture CDC functionality"\n    - Expected Results: 3-5 relevant docs about Streams\n\nScenario 2: Configuration question\n    - User Question: "How do I configure SSO with Okta?"\n    - Search Query: "SSO single sign-on Okta SAML configuration setup"\n    - Expected Results: Step-by-step guides, configuration docs\n\nScenario 3: Low relevance handling\n    - Initial Query: "table optimization"\n    - Results: Low relevance scores (\\<0.5)\n    - Action: Rephrase search: "table clustering performance optimization best practices".\n        Then provide results from improved search\n\nScenario 4: No relevant results\n    - User Query: "Snowflake integration with \\[obscure system\\]"\n    - Results: No results with relevance \\>0.3\n    - Response: "I couldn't find documentation about this integration.\n        This feature may not be supported or documented yet.\n        Please contact Support for specific integration questions."\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EIf you have essential parameters in your Cortex Search service, it is\nespecially important for you to include:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EType and format\u003C/strong\u003E (include examples)\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ERequired vs. optional\u003C/strong\u003E (with default values)\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EValid values or constraints\u003C/strong\u003E (enums, ranges, formats)\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ERelationship to other parameters\u003C/strong\u003E (dependencies, conflicts)\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EHow to obtain the value\u003C/strong\u003E (especially for IDs)\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EOne good example is if you have a service where you often need to filter for specific accounts, or start or end dates of contracts, the following description would help your agent in using this search service.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E✅ GOOD EXAMPLE\n\nParameters:\n\naccount_id:\nType: string\nRequired: Yes\nDescription: Unique Salesforce account ID (18-character alphanumeric)\nFormat: Starts with "001" followed by 15 alphanumeric characters\nExample: "001XX000003DHW3QAO"\nHow to obtain: Use AccountLookup tool first if you only have account\nname\n\nstart_date:\nType: string (ISO 8601 date)\nRequired: Yes\nFormat: "YYYY-MM-DD"\nExample: "2024-01-01"\nConstraints: Must not be more than 2 years in the past, must be before\nend_date\n\nend_date:\nType: string (ISO 8601 date)\nRequired: No (defaults to today)\nFormat: "YYYY-MM-DD"\nExample: "2024-12-31"\nConstraints: Must be after start_date, cannot be in the future\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ECode Execution\u003C/h3\u003E\n","\u003Cp\u003EThe \u003Ca href=\"https://docs.snowflake.com/en/LIMITEDACCESS/cortex-agents-code-interpreter\"\u003Ecode execution tool\u003C/a\u003E enables your agent to generate and run Python code in a sandboxed environment during a conversation. This is useful for complex calculations, data transformations, and generating visualizations that go beyond what SQL can express.\u003C/p\u003E\n","\u003Cp\u003ETo enable code execution, add the tool spec and resource to your agent specification:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-yaml\"\u003Etools:\n  - tool_spec:\n      type: code_execution\n      name: code_execution\n\ntool_resources:\n  code_execution: {}\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EBest practices for code execution:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EScope access carefully.\u003C/strong\u003E The code execution tool inherits the agent owner's role privileges. Make sure the owner role is appropriately scoped.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EGrant PyPI access only when needed.\u003C/strong\u003E You can allow PyPI package installation via \u003Ccode\u003Eartifact_repositories\u003C/code\u003E, but this gives the tool access to any public package. Only enable it when your use case requires external libraries.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EUse external access integrations sparingly.\u003C/strong\u003E If the code execution tool needs to reach external endpoints, create narrowly scoped network rules that allow only the specific domains required.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EDesign for single-session scope.\u003C/strong\u003E The sandbox persists within a session but not across sessions. If you need to persist results, write them to a Snowflake table that the tool has access to.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAdd orchestration instructions for when to use code execution vs. other tools.\u003C/strong\u003E For example: \u003Cem\u003E"Use the code execution tool for statistical analysis, visualizations, or multi-step calculations. Use Cortex Analyst for direct data retrieval."\u003C/em\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWeb Search\u003C/h3\u003E\n","\u003Cp\u003EThe \u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents#web-search\"\u003Eweb search tool\u003C/a\u003E lets your agent query the web via the Brave Search API to retrieve real-time information during a conversation. This is useful for questions about current events, public benchmarks, or any context that your internal data doesn't cover.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPrerequisites:\u003C/strong\u003E An ACCOUNTADMIN must enable web search at the account level in Snowsight under \u003Cstrong\u003EAI & ML → Agents → Settings\u003C/strong\u003E before it can be used in any agent.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EBest practices for web search:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EUse web search for real-time information your internal data doesn't cover.\u003C/strong\u003E If users ask about industry trends, competitor news, or current events, web search fills the gap.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAdd explicit orchestration instructions for when to use web search vs. internal tools.\u003C/strong\u003E For example: \u003Cem\u003E"Use web search only for questions about external market data or current events. For all customer and sales data, use CustomerAnalytics."\u003C/em\u003E Without this guidance, the agent may default to web search for questions your internal tools can answer better.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EKnow the privacy model.\u003C/strong\u003E Snowflake has enabled zero data retention (ZDR) with Brave — no search queries or results are stored by Brave. However, queries and results do traverse the public internet.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ECombine with Cortex Search for hybrid scenarios.\u003C/strong\u003E Web search provides breadth (the open web), while Cortex Search provides depth (your proprietary documents). Use orchestration instructions to tell the agent when each is appropriate.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EMCP Connectors\u003C/h3\u003E\n","\u003Cp\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents-mcp-connectors\"\u003EMCP Connectors\u003C/a\u003E connect your agents to external SaaS tools via the Model Context Protocol (MCP). Supported connectors include Atlassian (Jira & Confluence), GitHub, Glean, Google Workspace, Linear, Salesforce, and Slack, and you can build custom connectors for any MCP-compatible endpoint.\u003C/p\u003E\n","\u003Cp\u003EThe setup flow for MCP connectors is:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\u003Cstrong\u003EProvider setup:\u003C/strong\u003E Create an OAuth app on the provider's dashboard and obtain credentials.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAPI integration:\u003C/strong\u003E Create an API integration in Snowflake that stores the OAuth configuration.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EExternal MCP server:\u003C/strong\u003E Create an external MCP server object that references the API integration.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAgent configuration:\u003C/strong\u003E Add the external MCP server to your agent.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EUser authentication:\u003C/strong\u003E End users connect via OAuth in Snowflake CoWork.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cstrong\u003EBest practices for MCP connectors:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EFollow least-privilege access.\u003C/strong\u003E Grant only the minimum required privileges for each role. Access to an MCP server doesn't automatically grant access to its tools.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EUse descriptive names for MCP servers.\u003C/strong\u003E The agent selects tools based on name and description context. A name like \u003Ccode\u003EJiraProjectTracker\u003C/code\u003E is better than \u003Ccode\u003EMCPServer1\u003C/code\u003E.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAdd orchestration instructions for external vs. internal tools.\u003C/strong\u003E For example: \u003Cem\u003E"Use the Jira connector for questions about open tickets and sprint progress. Use CustomerAnalytics for revenue and usage data."\u003C/em\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EDisable rather than drop integrations during maintenance.\u003C/strong\u003E Disabling preserves configuration and secrets while immediately blocking tool invocations. Dropping is permanent.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EUse hyphens, not underscores, in hostnames.\u003C/strong\u003E Hostnames containing underscores cause connection issues.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E👉 \u003Ca href=\"https://www.snowflake.com/en/developers/guides/getting-started-with-mcp-connectors/\"\u003E\u003Cem\u003EGetting Started with MCP Connectors\u003C/em\u003E\u003C/a\u003E\u003C/p\u003E\n","\u003Ch3\u003EHelp users find and use your agent effectively\u003C/h3\u003E\n","\u003Cp\u003EIn addition to a specific, descriptive agent name, add \u003Cstrong\u003Eexample questions\u003C/strong\u003E where you know your agent already performs well.\u003C/p\u003E\n","\u003Cp\u003EThese examples help users understand your agent’s purpose and how to engage with it. These example questions should be independent of each other, and connect back to your agent’s predefined purpose.\u003C/p\u003E\n","\u003Cp\u003E<img src="assets/example-questions-snowflake-intelligence.png" />\u003C/p\u003E\n","\u003Cp\u003EIn Snowflake CoWork, users can browse the \u003Cstrong\u003EAgents\u003C/strong\u003E tab to view available agents. They’ll see your agent’s description and its example questions. A well-written description makes it easy for users to recognize when to use your agent and what to expect from it.\u003C/p\u003E\n","\u003Cp\u003E<img src="assets/agent-tab-snowflake-intelligence.png" />\u003C/p\u003E\n","\u003Ch2\u003EDeploying your agent to production\u003C/h2\u003E\n","\u003Cp\u003EThe process of deploying agents is similar to developer cycles, with three key stages. Begin by clearly:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EDefining a use case and creating a prototype agent.\u003C/li\u003E\u003Cli\u003EUsing systematic tests to drive iteration and improvement.\u003C/li\u003E\u003Cli\u003EGraduating to a production agent.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E👉 \u003Cem\u003EFor a deep dive into evaluation, versioning, CI/CD, and monitoring best practices, see \u003Ca href=\"https://www.snowflake.com/en/developers/guides/best-practices-for-evaluating-cortex-agents/\"\u003EBest Practices for Evaluating Cortex Agents\u003C/a\u003E.\u003C/em\u003E\u003C/p\u003E\n","\u003Ch3\u003EUse agent versioning to structure your deployment lifecycle\u003C/h3\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPreview Feature — Private:\u003C/strong\u003E Agent versioning is available to select accounts.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003ECortex Agent versioning gives you a clean separation between development and production through three concepts:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ELive version\u003C/strong\u003E — a mutable draft where you iterate on prompts, tools, and configs.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ENamed versions\u003C/strong\u003E — immutable snapshots created from the live version that you can safely test and deploy.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAliases\u003C/strong\u003E (e.g., \u003Ccode\u003Eproduction\u003C/code\u003E, \u003Ccode\u003Estaging\u003C/code\u003E, \u003Ccode\u003Ecanary\u003C/code\u003E) — pointers that route traffic to a specific version, decoupling your client code from version numbers.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EThe core workflow:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\u003Cstrong\u003EPrototype\u003C/strong\u003E on the live version.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ECommit\u003C/strong\u003E a named version and evaluate it against your test set.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EPromote\u003C/strong\u003E by assigning the \u003Ccode\u003Eproduction\u003C/code\u003E alias to the version that passes your quality bar.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EALTER AGENT my_agent COMMIT COMMENT = 'Improved tool selection logic';\n\nALTER AGENT my_agent MODIFY VERSION VERSION$4 SET ALIAS = production;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EIf a regression is detected, roll back instantly by pointing the alias to a previous version:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EALTER AGENT my_agent MODIFY VERSION VERSION$3 SET ALIAS = production;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou can also create agent versions from a stage or git repository, list versions, and access version files via the \u003Ccode\u003Esnow://agent/\u003C/code\u003E URI scheme.\u003C/p\u003E\n","\u003Ch3\u003EStage 1: Prototype and use case development\u003C/h3\u003E\n","\u003Cp\u003EBuild the first version of your agent and smooth out obvious rough edges. At the end of this stage, it should be clear which use cases your agent targets and which it does not.\u003C/p\u003E\n","\u003Cp\u003ECreate a representative “golden” test set of questions, expected tool use, and expected answers. Work directly with trusted stakeholders or end-users to build this set — it becomes your baseline for measuring agent quality.\u003C/p\u003E\n","\u003Ch3\u003EStage 2: Iteration and evaluation\u003C/h3\u003E\n","\u003Cp\u003EUse the Snowflake Monitoring UI and \u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents#evaluation\"\u003ECortex Agent Evaluations\u003C/a\u003E (generally available) to identify which queries the agent handles incorrectly or too slowly. Agent traces show planning, tool use, and generation steps so you can pinpoint exactly where things went wrong.\u003C/p\u003E\n","\u003Cp\u003EAfter your agent performs well against your golden set, it’s ready for production.\u003C/p\u003E\n","\u003Ch3\u003EStage 3: Production\u003C/h3\u003E\n","\u003Cp\u003EMonitor production usage and collect user feedback. Run your evaluation set on a regular cadence to catch regressions from model updates, data changes, or tool configuration drift. Focus first on queries with negative feedback to build a “hard” evaluation set that drives the next round of improvement.\u003C/p\u003E\n","\u003Ch2\u003EHow to improve agent performance\u003C/h2\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003EImprove orchestration instructions and tool descriptions:\u003C/strong\u003E Use evaluation results to inform improvement. For issues with tools, focus on tool descriptions. For orchestration and planning issues, update orchestration instructions.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003EUse agent traces to identify latency bottlenecks:\u003C/strong\u003E Traces in the monitoring tab show the logical path the agent took and how long each step lasted, allowing you to pinpoint the exact bottleneck.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPre-define verified queries:\u003C/strong\u003E For common or complex analytics, pre-define and verify queries directly in your semantic views. This ensures the agent uses an optimized, predictable query path.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003EMake queries performant:\u003C/strong\u003E An ounce of data engineering is worth a pound of prompt engineering. Optimizing your underlying data models, pre-aggregating common metrics, and using clear, consistent column names can have a greater impact on performance than tweaking instructions.\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch2\u003EExample: Complete agent configuration\u003C/h2\u003E\n","\u003Cp\u003EHere's a comprehensive example bringing it all together in the Snowflake Agent UI. We're building "CarAnalytics Pro", an automotive marketplace analytics agent.\u003C/p\u003E\n","\u003Ch3\u003EAbout the agent\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode\u003EDisplay Name: CarAnalytics Pro\n\nDescription:\nCarAnalytics Pro answers questions about vehicle pricing, listing\nperformance, and market trends on AutoMarket.\n\nExample questions:\n    - What is the average Days to Sale for 2020 Honda Accord by trim in California last quarter?\n    - Which SUV segments had the largest month over month price change this year?\n    - Show listings that are priced above market for 2019 to 2021 Toyota RAV4 with mileage under 60,000.\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E<img src="assets/caranalystics-about-snowflake-intelligence.png"/>\u003C/p\u003E\n","\u003Ch3\u003EOrchestration instructions\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode\u003E\\*\\*Role:\\*\\*\n\nYou are "CarAnalytics Pro", an automotive data analytics assistant for\nAutoMarket, an online car marketplace. You help data scientists,\nanalysts, product managers, and pricing strategists gain insights from\nvehicle listings, customer behavior, market trends, and platform\nperformance data.\n\n\\*\\*Users:\\*\\*\n\nYour primary users are:\n    - Data scientists building predictive models and statistical analyses\n    - Business analysts tracking KPIs and generating reports\n    - Product managers optimizing platform features and user experience\n    - Pricing strategists developing competitive pricing recommendations\n\n    They typically need to analyze large datasets, understand market dynamics, and create data-driven recommendations for business strategy.\n\n\\*\\*Context:\\*\\*\n\nBusiness Context:\n    - AutoMarket is a leading online car marketplace in North America\n    - We facilitate both B2C (dealer) and C2C (private party) transactions\n    - Platform handles 50,000+ active vehicle listings\n    - Revenue from listing fees, transaction commissions, and premium dealer services\n    - Data refreshes: Daily at 2 AM PST\n\nKey Business Terms:\n    - Listing Velocity: Days from listing creation to sale (target: \\<30 days)\n    - Price-to-Market Ratio (PMR): Listing price ÷ market value (1.0 = fair price)\n    - Days to Sale (DTS): Time from listing to completed transaction\n    - Take Rate: Platform commission as % of transaction value (avg 3-5%)\n    - GMV: Gross Merchandise Value (total $ of all transactions)\n\nMarket Segments:\n    - Luxury: Vehicles \\>$50K (BMW, Mercedes, Audi, Lexus)\n    - Mid-Market: $15K-$50K (Honda, Toyota, Ford, Chevy)\n    - Budget: \\<$15K (older vehicles, high mileage)\n    - Electric/Hybrid: Alternative fuel vehicles (25% YoY growth)\n    - Trucks & SUVs: 40% of our GMV\n\n\\*\\*Tool Selection:\\*\\*\n\n- Use "VehicleAnalytics" for vehicle inventory, pricing, and listing performance.\n    Examples: "What's the average Days to Sale for 2020 Honda Accords?", "Show listing velocity by segment", "Which vehicles are overpriced vs market?"   \n- Use "CustomerBehavior" for buyer/seller behavior, conversion, and segmentation.\n    Examples: "What's the customer journey from search to purchase?","Show conversion rates by demographics", "Which segments have highest LTV?"  \n- Use "MarketIntelligence" for competitive analysis and market research.\n    Examples: "How do our prices compare to Carvana?", "What's our market share by region?", "Which markets have highest growth potential?"\n- Use "RevenueAnalytics" for financial metrics, GMV, take rate, and commissions.\n    Examples: "What's our take rate by transaction type?", "Show GMV trends and seasonality", "Calculate CAC by acquisition channel"\n\n\\*\\*Boundaries:\\*\\*\n- You do NOT have access to individual customer PII (names, emails, addresses, phone numbers). Only use aggregated/anonymized data per GDPR/CCPA compliance.\n- You do NOT have real-time competitor pricing beyond daily intelligence feeds. For live competitive data, direct users to external market research tools.\n- You CANNOT execute pricing changes, adjust live listings, or make binding business commitments. All recommendations are analytical only.\n- You do NOT have access to internal HR data, employee performance, or confidential strategic plans outside data analytics scope.\n- For questions about legal compliance, contracts, or regulations,respond: "I can provide data analysis but not legal advice. Please consult Legal for compliance questions."\n\n\\*\\*Business Rules:\\*\\*\n- When analyzing seasonal trends, ALWAYS apply Seasonal Adjustment Factor for vehicle types with known seasonality (convertibles, 4WD trucks, etc.)\n- If query returns \\>500 listings, aggregate by make/model/segment rather than showing individual listings\n- For price recommendations, ALWAYS include confidence intervals and sample size. Do not recommend pricing without statistical validation.\n- When comparing time periods, check for sufficient sample size (minimum 30 transactions per period). Flag low-sample warnings.\n- If VehicleAnalytics returns PMR outliers (\\>1.5 or \\<0.5), flag as potential data quality issues and recommend manual review.\n\n\\*\\*Workflows:\\*\\*\n\nPricing Strategy Analysis: When user asks "Analyze pricing for \\[segment/make/model\\]" or "Should we adjust pricing for \\[category\\]":\n\n1. Use VehicleAnalytics to get current listing data:\n    - Average prices, Days to Sale, Price-to-Market Ratios\n    - Compare vs 3-month and 12-month historical trends\n    - Segment by condition, mileage, regional variations\n\n2. Use MarketIntelligence for competitive context:\n    - Compare our prices vs competitors (Carvana, CarMax, dealers)\n    - Identify price gaps and positioning opportunities\n    - Analyze competitor inventory levels and velocity\n\n3. Use CustomerBehavior for demand signals:\n    - View-to-inquiry and inquiry-to-offer conversion rates\n    - Price sensitivity analysis by segment\n    - Historical elasticity data\n\n4. Present findings:\n    - Executive summary with specific pricing recommendation\n    - Expected impact on DTS and conversion with confidence intervals\n    - A/B testing plan and monitoring KPIs\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EResponse Instructions\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode\u003E\\*\\*Style:\\*\\*\n    - Be direct and data-driven - analysts value precision over politeness\n    - Lead with the answer, then provide supporting analysis\n    - Use statistical terminology appropriately (p-values, confidence intervals, correlation vs causation)\n    - Flag data limitations, sample size constraints, and seasonality effects\n    - Avoid hedging with business metrics - state numbers clearly\n\n\\*\\*Presentation:\\*\\*\n    - Use tables for comparisons across multiple vehicles/segments (\\>4 rows)\n    - Use line charts for time-series trends and seasonality\n    - Use bar charts for rankings and segment comparisons\n    - For single metrics, state directly: "Average DTS is 23 days (±3 days, 95% CI)"\n    - Always include data freshness, sample size, and time period in responses\n\n\\*\\*Response Structure:\\*\\*\n\n For trend analysis questions:\n "\\[Summary of trend direction\\] + \\[chart\\] + \\[statistical significance\\] + \\[context\\]"\n    \n    Example: "Luxury segment DTS decreased 15% QoQ (p\\<0.01). \\[chart showing monthly trend\\]. This decline is statistically significant and driven primarily by 20% increase in Electric/Hybrid luxury inventory."\n\nFor pricing questions: \n    "\\[Direct recommendation\\] + \\[supporting data\\] + \\[expected impact\\] + \\[caveats\\]"\n    \n    Example: "Recommend 5-8% price reduction for 2019-2020 Honda Accord listings. Current PMR is 1.12 vs market (overpriced). Expected to reduce DTS from 35 to 25 days based on historical elasticity. Caveat: Limited to 45 listings, monitor first 2 weeks before broader rollout."\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ETool: VehicleAnalytics\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003ESelect a new Cortex Analyst tool\u003C/li\u003E\u003Cli\u003ESelect “Generate with Cortex” then refine further\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode\u003EName: VehicleAnalytics\n\nDescription:\nAnalyzes vehicle inventory, pricing trends, listing performance, and market positioning metrics. Covers all active and sold listings on AutoMarket\nplatform.\n\nData Coverage:\n    - Historical: Past 3 years of listing and transaction data\n    - Active listings: All current platform inventory (\\~50K listings)\n    - Sold listings: Completed transactions with final sale price\n    - Removed listings: Listings removed without sale (expired, withdrawn)\n    - Refresh: Daily at 2 AM PST (21-hour lag from current time)\n\n    Data Sources: listings table, transactions table, vehicle_valuations table\n\nWhen to Use:\n    - Questions about vehicle pricing, inventory levels, or listing counts\n    - Listing performance metrics (Days to Sale, listing velocity, PMR)\n    - Historical price trends and seasonality analysis\n    - Vehicle-level or segment-level aggregations\n    - "Which vehicles/segments" queries (rankings, comparisons, distributions)\n\nWhen NOT to Use:\n    - Do NOT use for buyer/seller behavior or conversion funnels (use CustomerBehavior)\n    - Do NOT use for competitive pricing outside AutoMarket (use MarketIntelligence)\n    - Do NOT use for financial metrics like GMV, commissions, revenue (use RevenueAnalytics)\n    - Do NOT use for real-time data (21-hour lag, updated daily only)\n    - Do NOT use for individual customer purchase history (PII restricted)\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EConclusion\u003C/h3\u003E\n","\u003Cp\u003EBy following these best practices, you can confidently build \u003Cstrong\u003ECortex Agents\u003C/strong\u003E that are reliable, secure, and aligned with Snowflake’s data governance standards. Each agent should have a clearly defined purpose, a focused set of tools, and robust orchestration and response logic.\u003C/p\u003E\n","\u003Ch2\u003EAdditional resources\u003C/h2\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/snowflake-cowork\"\u003ESnowflake CoWork Documentation\u003C/a\u003E\u003C/li\u003E\u003Cli\u003EGuide: \u003Ca href=\"https://www.snowflake.com/en/developers/guides/best-practices-to-evaluating-cortex-agents/\"\u003EBest Practices for Evaluating Cortex Agents\u003C/a\u003E\u003C/li\u003E\u003Cli\u003EGuide: \u003Ca href=\"https://www.snowflake.com/en/developers/guides/getting-started-with-cowork/\"\u003EGetting started with Snowflake CoWork\u003C/a\u003E\u003C/li\u003E\u003Cli\u003EGuide: \u003Ca href=\"https://www.snowflake.com/en/developers/guides/getting-started-with-cowork-and-cke/\"\u003EGetting started with Snowflake CoWork and Cortex Knowledge Extensions (CKEs)\u003C/a\u003E\u003C/li\u003E\u003Cli\u003EGuide: \u003Ca href=\"https://www.snowflake.com/en/developers/guides/getting-started-with-mcp-connectors/\"\u003EGetting Started with MCP Connectors\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents-code-execution-tool\"\u003ECode execution tool documentation\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents-mcp-connectors\"\u003EMCP Connectors documentation\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/developers/guides/\"\u003EMore Snowflake Guides\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E"],"description":"","title":"Best Practices for Building Cortex Agents","isDeveloperGuidesPage":false,":type":"snowflake-site/components/contentfragment","elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],":items":{},":itemsOrder":[],"elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"### Overview\n\nAgents represent a new paradigm for how work gets done with data. Instead of pre-defined dashboards or static queries, agents reason through tasks, choose the right tools, and deliver results in natural language or take actions on your behalf.\n\nYou can create, update, and deploy these high-quality agents directly inside your Snowflake environment. Snowflake Agents integrate directly with [Snowflake CoWork](https://ai.snowflake.com/) with governance, observability, and performance built in.\n\nThis guide is your map to building high-quality Cortex Agents for use with Snowflake CoWork, from idea to production, including links to deeper resources, examples, and tutorials along the way.\n\n### What you'll learn\n- How Snowflake CoWork and Cortex Agents work together.\n- How to define agent purpose and scope.\n- How to configure orchestration and response instructions.\n- How to design effective tools for Cortex Agents.\n- How to use agent versioning to manage your deployment lifecycle.\n- How to evaluate and monitor agent performance.\n\n**Important:** Before building Cortex Agents, [configure your permissions](https://docs.snowflake.com/en/user-guide/snowflake-cortex/snowflake-intelligence#set-up-sf-intelligence) and make sure that you have [access to the right models](https://docs.snowflake.com/en/user-guide/snowflake-cortex/snowflake-intelligence#supported-models-and-regions).\n\n## How Snowflake CoWork works\n\nCortex Agents power the reasoning behind Snowflake CoWork, turning natural language into governed actions and answers.\n\nCortex Agents combine reasoning from large language models with Snowflake’s governance, data access, and observability layers to deliver accurate, explainable answers. When a user asks a question in Snowflake CoWork, it uses [Cortex Agents](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents) under the hood with the following stages.\n\n1.  **User input:** A user submits a natural-language question. For example, *“How are Q4 sales trending?”*.\n2.  [**Cortex Agent API**](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents-rest-api): The question is routed to the **Cortex Agent API**, which powers Snowflake CoWork.\n3.  **Orchestration:** The orchestrator (an LLM) interprets intent, selects the right tools, and plans the sequence of actions. It may use one tool, chain several together, or decide that the question is out of scope.\n4.  **Tool execution:**\n    -   [Cortex Analyst](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-analyst): Write and run SQL on your semantic views for structured data.\n    -   [Cortex Search](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-search/cortex-search-overview): Retrieve relevant document text for unstructured data.\n    -   [Code Execution](https://docs.snowflake.com/en/LIMITEDACCESS/cortex-agents-code-interpreter): Generate and run Python code in a sandboxed environment.\n    -   [Web Search](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents#web-search): Query the web for real-time information.\n    -   [MCP Connectors](https://docs.snowflake.com/en/LIMITEDACCESS/snowflake-cortex/mcp-connectors): Connect to external SaaS tools via the Model Context Protocol.\n    -   Custom Tools: Execute user-defined functions or stored procedures for actions.\n5.  **Reflection & response:** The orchestrator reviews results, refines if needed, and generates the final answer (including summaries, tables, or charts) shown in the Snowflake CoWork UI.\n\nThe following image describes this structure of Snowflake CoWork.\n![Snowflake CoWork](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/best-practices-to-building-cortex-agents/snowflake-cowork-architecture.png?v=9baa5992) \n\n*👉 Read the blog to learn more about [how Snowflake CoWork orchestration works](https://www.snowflake.com/en/engineering-blog/inside-snowflake-intelligence-enterprise-agentic-ai/)*\n\n\n## Building Cortex Agents\n\n[Cortex Agents](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents) are configurable reasoning systems that combine Snowflake’s built-in intelligence with your domain context.\n\nYou can build and run agents in several ways:\n\n1.  **Agent UI in Snowsight:** An interactive interface that handles identity, access control, and monitoring out of the box.\n2.  [**Cortex Agent API**](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents-rest-api): A REST API for integrating agents into your own applications (like Streamlit apps or custom apps).\n\n\u003E  **Pro tip:** Build agents using natural language with Snowflake's AI coding agent [**Cortex Code**](https://www.snowflake.com/en/developers/guides/best-practices-cortex-code-cli/#production-ready-cortex-agents). \n\nConsider the following when building an agent.\n\n### Define your agent's purpose\n\nEvery great agent starts with a clear purpose. Before adding tools or writing instructions, define why the agent exists, who it serves, and what specific questions it should answer. This step shapes everything that follows, from tool selection to performance and trust.\n\nStart with an end user, and think through what they would actually want: *what specific job is this agent meant to do, and for whom? If they had 24/7 access to a data analyst who reads incredibly quickly and has single-digit minute response times, what would they ask of them?*\n\n### Favor narrowly-scoped specialized agents\n\nDon’t boil the ocean with a generalist agent. Start narrow with a specific, high-value use case. After an agent proves reliable in one area, you can replicate the pattern for others.\n\nFor example, you could have the following agents:\n1.  One agent that analyzes your Shopify store’s recent sales and marketing data.\n2.  One agent that sales can use to recommend the best SKUs to pitch to the retailer.\n\n*👉*[*Read more about why single agents yield the best\nresults*](https://medium.com/@JamesChaEarley/356b8566d114)\n\n### Map key use cases to tools\n\nTo get high-value, narrow use cases, partner with business stakeholders to identify the top 20 most important questions that they need answered. Use these questions as the initial scope for your agent.\n\nIf you were to answer that question using a set of documents or data, what would you use? Would you use the sales table, read a few Google docs (which ones?), or look up support tickets?\n\n**How many tools should a single agent have?**\nAn agent should have access to exactly as many tools as it needs to fulfill its predefined, targeted purpose. In the previous guidance, you wrote down exactly what you needed to answer each question. This becomes the list of tools your agent needs access to.\n\nFor example, if you needed to write one set of SQL statements about your Shopify data, then read a Google doc, and finally read some support tickets when answering your question, your agent needs at least 3 separate components:\n1.  A semantic view for your Shopify data\n2.  A Cortex Search service to read your Google docs\n3.  A Cortex Search service to read your support tickets\n\n*👉[Lessons learned building agents from our data science\nteam](https://medium.com/snowflake/how-to-make-useful-data-science-agents-dbacbf1643b8)*\n\n## Importance of Cortex Agent instructions\n\nA well-written agent will run efficiently and reliably: calling the right tools, producing explainable results, and reflecting your business logic. Bad or incomplete instructions lead to missteps in reasoning, incorrect data retrieval, and wasted compute cost.\n\nEvery Cortex Agent combines your custom instructions with Snowflake’s built-in base system instructions. These base instructions inform general workflows for tool usage, data analysis patterns, validation, visualization, citation, and safety guardrails.\n\nYou **won’t** need to further instruct the agent on this base functionality. For example:\n\n``` \n❌ DON'T include:\n\"When you receive a question, first analyze it carefully, then\nselect appropriate tools, call them in sequence, and format results properly...\"\n```\n\nYour custom agent instructions are configured in 4 key layers, each playing a specific role to define how the agent reasons and responds with domain-specific context, rules, and workflows.\n1.  [**Semantic views**](https://docs.snowflake.com/en/user-guide/views-semantic/overview#why-use-semantic-views) are configured inside your data layer. They act as translators, or “cheat sheets” between your raw, structured data and how humans or AI interpret it.\n2.  **Orchestration instructions** are configured high-level business logic, rules, and multi-step workflows. These instruct the agent on how to approach answering a question.\n3.  **Response instructions** control the final output format, tone, and communication style of the agent.\n4.  **Tool descriptions** explain precisely what a tool does, which data it accesses, when to use it, and when *not* to use it. *This is the most critical factor for accurate tool selection.*\n\nWe’ll go into more detail for each instruction layer in the following sections.\n\n## Semantic views (data level)\n\nEach [semantic view](https://docs.snowflake.com/en/user-guide/views-semantic/overview) should cover a similar set of tables, and is instructions that tell the agent how to query or interpret the data. This is where you want to set data specific defaults, such as always adding a date filter for the past three months if not specified or always excluding internal accounts.\n\n**Resources for semantic views:**\n-   🎥 [Watch the hands-on lab](https://www.snowflake.com/en/webinars/virtual-hands-on-lab/from-sql-to-agentic-analytics-building-a-semantic-layer-with-ai-to-empower-snowflake-intelligence-2025-11-20/)\n-   📖 [Get started with semantic views](https://medium.com/snowflake/getting-started-with-snowflake-semantic-view-7eced29abe6f)\n\n## Orchestration instructions (agent level)\n\nOrchestration instructions could include:\n\n-   **Your agent’s identity and narrow scope** prevents scope creep and helps the agent stay focused on its intended purpose.\n\n```\n✅ ORCHESTRATION INSTRUCTION\n\nYour Role: You are \"SalesBot\", a sales intelligence assistant for the Snowflake sales team. Your Scope: You answer questions about customer accounts, pipeline opportunities, deal history, and product usage. You help sales professionals prepare for customer meetings and track account health. \n\nYour Users: Account Executives (AEs), Solution Engineers (SEs), and Sales Leaders who need quick access to customer data and insights. \n ```\n\n-   **Domain context** helps the agent interpret questions correctly and use appropriate terminology.\n\n```\n✅ ORCHESTRATION INSTRUCTION\n\nDomain Context \n- Snowflake uses a \"consumption-based\" pricing model where customers pay for compute (measured in credits) and storage separately.\n- An \"opportunity\" represents a potential deal tracked in Salesforce with stages: Prospecting → Qualification → Proof of Value → Negotiation → Closed Won/Lost\n- \"ARR\" (Annual Recurring Revenue) is the key metric for subscription value\n- Our fiscal year runs Feb 1 - Jan 31\n```\n\n-   **Explicit tool selection logic** to prevent the agent from choosing the wrong tool and improve consistency.\n\n```\n✅ ORCHESTRATION INSTRUCTION\n\nTool Selection Guidelines:\n- For questions about CURRENT customer data (accounts, usage, credits): Use the \"CustomerData\" tool.\n    For example: \"What's Acme Corp's credit usage?\", \"Show me active accounts\"\n- For questions about HISTORICAL trends and analytics: Use the \"Analytics\" tool.\n    For example: \"How has consumption grown over time?\", \"Compare Q1 vs Q2\"\n- For questions about sales pipeline and opportunities: Use the \"SalesforcePipeline\" tool.\n    For example: \"What deals are closing this quarter?\", \"Show me open opportunities\"\n```\n\n-   **Boundaries and limitations** prevent hallucinations and inappropriate responses. Users will inevitably ask questions outside your agent's scope.\n\n```\n✅ ORCHESTRATION INSTRUCTION\n\nLimitations and Boundaries:\n- You do NOT have access to customer contracts or legal agreements.\n    If asked, respond: \"I don't have access to contract details. Please contact Legal.\"\n- You do NOT have real-time data. Your data is refreshed daily at 2 AM UTC.\n    If asked about \"right now\", clarify: \"My data is current as of this morning's refresh.\"\n- Do NOT calculate financial forecasts or make predictions about future revenue.\n    You can show historical trends but should not extrapolate future values.\n- Do NOT provide customer contact information (emails, phone numbers) for privacy reasons.\n\n```\n\n-   **Business rules and conditional logic** to ensure consistent handling of common scenarios, edge cases, and error conditions.\n\n```\n✅ ORCHESTRATION INSTRUCTION\n\nBusiness Rules:\n- When a user asks about a customer by name (not ID), ALWAYS use\nCustomerLookup tool first to get the customer_id before calling other tools\n\n- If a query result returns more than 100 rows, ALWAYS aggregate or\nfilter the data before presenting. Do NOT display all rows.\n\n- For any consumption questions about dates within the last 7 days,\nremind users that data has a 24-hour delay and today's data is not yet\navailable\n\n- When multiple regions match a query, ALWAYS ask for clarification\nrather than assuming which region the user meant\n\n- If a tool returns an error code \"INSUFFICIENT_PERMISSIONS\", respond\nwith: \"You don't have access to this data. Please contact your Snowflake admin to request access.\"\n```\n\n-   **Domain-specific workflows** to deliver consistency and reduce the need for users to ask complex multi-part questions.\n\n```\n✅ ORCHESTRATION INSTRUCTION\n\nAccount Summary Workflow:\n\nWhen a user asks to \"summarize my accounts\" or \"give me a book of\nbusiness update\":\n\n1. Use CustomerData tool to get the user's assigned accounts list\n2. Use Analytics tool to show each account's':\n    - Last 90-day consumption and growth rate\n    - Total ARR and change from last quarter\n3. Use SalesforcePipeline tool to show:\n    - Top 5 open opportunities by value\n    - Any opportunities closing in next 30 days\n4. Use SupportTickets tool to flag any critical severity tickets in last 7 days\n\nPresent results in tables with clear sections.\n```\n\n## Response instructions (agent level)\n\nThese instructions control the final output format, tone, and communication style of the agent. Examples include:\n\n-   **Tone and communication style:**\n\n```\n✅ RESPONSE INSTRUCTION\n\nResponse Style:\n    - Be concise and professional - sales teams are busy\n    - Lead with the direct answer, then provide supporting details\n    - Be direct with data. Avoid hedging language like \"it seems\" or \"it appears\"\n    - Use active voice and clear statements\n```\n\n-   **Data presentation formats**\n\n```\n✅ RESPONSE INSTRUCTION\n\nData Presentation:\n    - Use tables for multi-row data (\\\u003E3 items)\n    - Use charts for comparisons, trends, and rankings\n    - For single values, state them directly without tables\n    - Always include units (credits, dollars, %) with numbers\n    - Include data freshness timestamp in responses\n```\n\n-   **Response structure templates**\n\n```\n✅ RESPONSE INSTRUCTION\n\nResponse Structure:\n\nFor \"What is X?\" questions:\n    - Lead with direct answer\n    - Follow with supporting context if relevant\n\n    Response example: \"Acme Corp used 12,450 credits last month (up 8% from September).\"\n\nFor \"Show me X\" questions:\n    - Brief summary sentence\n    - Table or chart with data\n    - Key insights or highlights\n\n    Response example: \"You have $2.4M in open Q4 pipeline across 12 opportunities. \\[table\\]\"\n\nFor \"Compare X and Y\" questions:\n    - Summary of comparison result\n    - Chart showing comparison visually\n    - Notable differences highlighted\n```\n\n-   **Error and edge case messaging**\n\n```\n✅ RESPONSE INSTRUCTION\n\nError Handling:\n- When data is unavailable: \"I don't have access to \\[data type\\]. You can find this information in \\[alternative source\\] or contact \\[team\\].\"\n- When query is ambiguous: \"To provide accurate data, I need clarification: \\[specific question\\]. Did you mean \\[option A\\] or \\[option B\\]?\"\n- When results are empty: \"No results found for \\[criteria\\]. This could mean \\[possible reason\\]. Would you like to try \\[alternative approach\\]?\"\n```\n\n\n## Best practices between orchestration and response instructions\n\nIt’s important to separate orchestration (what to do, which tools) and response (how to format, tone) into distinct instruction settings. Don’t combine tool selection logic with response formatting in the same section.\n\nTo help categorize where instructions should live, ask yourself:\n\n| Does this instruction affect... | Put it in... | Example                                          |\n|--------------------------------------|------------------|------------------------------------------------------|\n| Which tool to select                 | Orchestration    | \"Use CustomerData for current metrics\"               |\n| What data to retrieve                | Orchestration    | \"Include last 90 days of usage data\"                 |\n| How to interpret user intent         | Orchestration    | \"When user says 'recent', use last 30 days\"          |\n| How to sequence tool calls           | Orchestration    | \"Always call CustomerLookup before CustomerMetrics\"  |\n| Conditional logic and rules          | Orchestration    | \"If result \\\u003E 100 rows, aggregate before displaying\" |\n| What to do in specific scenarios     | Orchestration    | \"When error code X occurs, try alternative tool Y\"   |\n| How to format the answer             | Response         | \"Use tables for multi-row results\"                   |\n| What tone to use                     | Response         | \"Be concise and professional\"                        |\n| How to structure text                | Response         | \"Lead with direct answer, then details\"              |\n| What to say when errors occur        | Response         | \"Explain limitation and suggest alternatives\"        |\n\n## Tool descriptions (agent level)\n\nThese describe to the agent what types of things the tool (Semantic View, Search Service, or Custom Tool) can do, so it can infer when it would be best to call it.\n\n**Tool descriptions are often the culprit for most agent quality problems.** Agents choose tools based on name and description context, so make them obvious. Bad tool descriptions create cascading failures, and can lead to downstream hallucinations.\n\nWhile instructions set the agent's identity and scope, tool descriptions directly govern:\n1.  **Tool selection accuracy**: Whether the agent picks the right tool for each question.\n2.  **Parameter usage**: Whether the agent provides correct inputs to tools.\n3.  **Error prevention**: Whether the agent avoids misusing tools or making invalid calls.\n4.  **Consistency**: Whether the agent behaves predictably across similar questions.\n\n### Step 1: Start with a clear, specific tool name\n\nTool names are loaded into the agent's context and influence selection.\n\nTip: Combine a *domain* (“Customer”, “Sales”) with a *function* (“Analytics”, “Search”) to make each tool unambiguous.\n\n```\n✅ GOOD: \"CustomerConsumptionAnalytics\"\n❌ BAD: \"DataTool\" or \"Tool1\"\n\n✅ GOOD: \"SalesforcePipelineQuery\"\n❌ BAD: \"Query\" or \"SalesData\"\n\n✅ GOOD: \"ProductDocumentationSearch\"\n❌ BAD: \"Search\" or \"Docs\"\n```\n\n### Step 2: Write a purpose-driven tool description\n\nA strong description tells the agent:\n\n**\\[What the tool does\\] + \\[What data it accesses\\] + \\[When to use it\\] + \\[When NOT to use it\\]**\n\n- **\\[What data it accesses\\] refers to what’s in your [semantic view](#semantic-views-data-level).** Include a concise summary of what’s in your semantic view. The agent first chooses tools based on their descriptions, not by inspecting your full data model.\n\n- **\"When NOT to Use\" is critical.** Without it, agents will try to use tools for everything remotely related. \"When NOT to Use\" creates clear boundaries and redirects the agent to appropriate alternatives.\n\n```\n✅ GOOD EXAMPLE\n\nName: CustomerConsumptionAnalytics\n\nDescription: Analyzes Snowflake consumption metrics for customer accounts including credit usage,compute hours, and storage.\n\nData Coverage: Daily aggregated consumption data for all commercial\ncustomers, updated nightly. Includes data from the past 2 years.\n\nWhen to Use:\n    - Questions about customer usage patterns, trends, or growth\n    - Queries about specific customers' consumption (e.g., \"How much did Acme use?\")\n    - Comparisons between time periods (e.g., \"Compare Q1 vs Q2 usage\")\n\nWhen NOT to Use:\n    - Do NOT use for real-time/current-hour data (data is daily batch, not real-time)\n    - Do NOT use for trial or non-commercial accounts (not included in dataset)\n    - Do NOT use for individual query performance (use QueryHistory tool instead)\n\nKey Parameters:\n    - customer_name: Exact customer name (case-sensitive).\n        Use CustomerList tool first if unsure of exact spelling.\n    - date_range: ISO format dates (YYYY-MM-DD). Required.\n        Use specific dates, not relative terms like \"last month\".\n    - metric: One of: 'credits', 'compute_hours', 'storage_tb'\n\n```\n\n```\n❌ BAD EXAMPLE:\nName: ConsumptionTool\nDescription: Gets consumption data.\n```\n\n### Step 3: Be explicit about tool inputs\n\n**This is where most tool descriptions fail.** Ambiguous inputs to your tools lead to incorrect tool calls and errors, whether Cortex Analyst, Cortex Search, or custom tools.\n\n| **Common pitfalls** | **Recommendation** |\n|---------------------|-------------------|\n| **Using a generic name**\u003Cbr\u003Euser vs user_id vs username | **Be specific**\u003Cbr\u003Esalesforce_user_id (18-char ID) vs user_email (email string) |\n| **Unclear data formats**\u003Cbr\u003E\"date\" vs \"ISO 8601 date (YYYY-MM-DD)\" | **Specify data format**\u003Cbr\u003EAgents may pass \"last month\", \"Q3\", or other invalid formats |\n| **Not explaining how to obtain IDs**\u003Cbr\u003E\"Provide customer_id\" | **Provide clear data instructions**\u003Cbr\u003E\"Customer ID from CustomerLookup tool, or directly from user if known\" |\n| **Unclear optionality**\u003Cbr\u003E\"region (optional)\" | **Provide default guidance**\u003Cbr\u003E\"region (optional, defaults to 'ALL', returns data for all regions)\" |\n| **Using inconsistent terminology**\u003Cbr\u003EPick one term and use it consistently everywhere.\u003Cbr\u003EInstructions say \"customers\" but tool descriptions say \"accounts\" | **Use consistent terminology**\u003Cbr\u003EIf your domain has multiple terms for the same concept, define them explicitly:\u003Cbr\u003E\"Account (also called 'customer' in billing context): A business entity that...\" |\n\n## Using Tools\n\nCortex Agents support a rich set of built-in tools: [Cortex Analyst](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-analyst) for text-to-SQL, [Cortex Search](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-search/cortex-search-overview) for document retrieval, [code execution](https://docs.snowflake.com/en/LIMITEDACCESS/cortex-agents-code-interpreter) for sandboxed Python, [web search](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents#web-search) for real-time information, and [MCP connectors](https://docs.snowflake.com/en/LIMITEDACCESS/snowflake-cortex/mcp-connectors) for integrating with external SaaS tools.\n\n### Cortex Analyst (Text-to-SQL)\n\nCortex Analyst accepts natural language queries and converts them to SQL. Your description must guide the agent on how to phrase queries effectively.\n\n**Start with \"Generate with Cortex\"** in the Admin UI to automatically generate a tool description based on your semantic model. This provides a solid baseline that already includes key information about your data.\n\n\u003Cimg src=\"assets/semantic-view-generate-with-cortex.png\" /\u003E\n\nThen, enhance the auto-generated description by following the previously described principles.\n\n### Cortex Search\n\n[Cortex Search](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-search/cortex-search-overview) services retrieve relevant documents and records using semantic search. The two primary use cases for Cortex Search are retrieval augmented generation (RAG) and enterprise search.\n\nFor example, one of the first demo agents built inside of Snowflake used the following Cortex Search Service to answer questions about internal product documentation and architecture.\n\n```\n✅ GOOD EXAMPLE\n\nName: ProductDocumentationSearch\n\nType: Cortex Search Service\n\nDescription:\nSearches internal product documentation, feature announcements,\ntechnical guides, and release notes to answer \"what\" and \"how\" questions\nabout Snowflake products. Uses semantic search to find relevant\ndocuments even when exact keywords don't match.\n\nData Sources:\n    - Product documentation (updated weekly)\n    - Feature release notes (updated with each release)\n    - Technical architecture guides (updated quarterly)\n    - Best practice documents (updated monthly)\n    - Last indexed: Timestamp included in each search result\n\nWhen to Use:\n    - Questions about product features, capabilities, or specifications\n    - \"How to\" questions and configuration instructions\n    - Feature availability and compatibility questions\n    - Troubleshooting guidance and best practices\n\nWhen NOT to Use:\n    - Customer-specific data or usage (use CustomerMetrics instead)\n    - Sales/pipeline information (use SalesforcePipeline instead)\n    - Real-time system status (use HealthMonitor instead)\n    - Questions requiring computation or data aggregation (use Cortex Analyst tools)\n\nSearch Query Best Practices:\n    1. Use specific product names:\n        ✅ \"Snowflake Streams change data capture\"\n        ❌ \"streams\" (too generic)\n    \n    2. Include multiple related keywords:\n        ✅ \"security authentication SSO SAML configuration\"\n        ❌ \"security\" (too broad)\n\n    3. Use technical terms when appropriate:\n        ✅ \"materialized view incremental refresh performance\"\n        ❌ \"fast views\" (colloquial)\n\n    4. If first search returns low relevance, rephrase: Try synonyms, expand acronyms, add context.\n    \nExample usage:\n\nScenario 1: Feature explanation\n    - User Question: \"How do Snowflake Streams work?\"\n    - Search Query: \"Snowflake Streams change data capture CDC functionality\"\n    - Expected Results: 3-5 relevant docs about Streams\n\nScenario 2: Configuration question\n    - User Question: \"How do I configure SSO with Okta?\"\n    - Search Query: \"SSO single sign-on Okta SAML configuration setup\"\n    - Expected Results: Step-by-step guides, configuration docs\n\nScenario 3: Low relevance handling\n    - Initial Query: \"table optimization\"\n    - Results: Low relevance scores (\\\u003C0.5)\n    - Action: Rephrase search: \"table clustering performance optimization best practices\".\n        Then provide results from improved search\n\nScenario 4: No relevant results\n    - User Query: \"Snowflake integration with \\[obscure system\\]\"\n    - Results: No results with relevance \\\u003E0.3\n    - Response: \"I couldn't find documentation about this integration.\n        This feature may not be supported or documented yet.\n        Please contact Support for specific integration questions.\"\n\n```\n\nIf you have essential parameters in your Cortex Search service, it is\nespecially important for you to include:\n\n-   **Type and format** (include examples)\n-   **Required vs. optional** (with default values)\n-   **Valid values or constraints** (enums, ranges, formats)\n-   **Relationship to other parameters** (dependencies, conflicts)\n-   **How to obtain the value** (especially for IDs)\n\nOne good example is if you have a service where you often need to filter for specific accounts, or start or end dates of contracts, the following description would help your agent in using this search service.\n\n```\n✅ GOOD EXAMPLE\n\nParameters:\n\naccount_id:\nType: string\nRequired: Yes\nDescription: Unique Salesforce account ID (18-character alphanumeric)\nFormat: Starts with \"001\" followed by 15 alphanumeric characters\nExample: \"001XX000003DHW3QAO\"\nHow to obtain: Use AccountLookup tool first if you only have account\nname\n\nstart_date:\nType: string (ISO 8601 date)\nRequired: Yes\nFormat: \"YYYY-MM-DD\"\nExample: \"2024-01-01\"\nConstraints: Must not be more than 2 years in the past, must be before\nend_date\n\nend_date:\nType: string (ISO 8601 date)\nRequired: No (defaults to today)\nFormat: \"YYYY-MM-DD\"\nExample: \"2024-12-31\"\nConstraints: Must be after start_date, cannot be in the future\n\n```\n\n### Code Execution\n\nThe [code execution tool](https://docs.snowflake.com/en/LIMITEDACCESS/cortex-agents-code-interpreter) enables your agent to generate and run Python code in a sandboxed environment during a conversation. This is useful for complex calculations, data transformations, and generating visualizations that go beyond what SQL can express.\n\nTo enable code execution, add the tool spec and resource to your agent specification:\n\n```yaml\ntools:\n  - tool_spec:\n      type: code_execution\n      name: code_execution\n\ntool_resources:\n  code_execution: {}\n```\n\n**Best practices for code execution:**\n-   **Scope access carefully.** The code execution tool inherits the agent owner's role privileges. Make sure the owner role is appropriately scoped.\n-   **Grant PyPI access only when needed.** You can allow PyPI package installation via `artifact_repositories`, but this gives the tool access to any public package. Only enable it when your use case requires external libraries.\n-   **Use external access integrations sparingly.** If the code execution tool needs to reach external endpoints, create narrowly scoped network rules that allow only the specific domains required.\n-   **Design for single-session scope.** The sandbox persists within a session but not across sessions. If you need to persist results, write them to a Snowflake table that the tool has access to.\n-   **Add orchestration instructions for when to use code execution vs. other tools.** For example: *\"Use the code execution tool for statistical analysis, visualizations, or multi-step calculations. Use Cortex Analyst for direct data retrieval.\"*\n\n### Web Search\n\nThe [web search tool](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents#web-search) lets your agent query the web via the Brave Search API to retrieve real-time information during a conversation. This is useful for questions about current events, public benchmarks, or any context that your internal data doesn't cover.\n\n**Prerequisites:** An ACCOUNTADMIN must enable web search at the account level in Snowsight under **AI & ML → Agents → Settings** before it can be used in any agent.\n\n**Best practices for web search:**\n-   **Use web search for real-time information your internal data doesn't cover.** If users ask about industry trends, competitor news, or current events, web search fills the gap.\n-   **Add explicit orchestration instructions for when to use web search vs. internal tools.** For example: *\"Use web search only for questions about external market data or current events. For all customer and sales data, use CustomerAnalytics.\"* Without this guidance, the agent may default to web search for questions your internal tools can answer better.\n-   **Know the privacy model.** Snowflake has enabled zero data retention (ZDR) with Brave — no search queries or results are stored by Brave. However, queries and results do traverse the public internet.\n-   **Combine with Cortex Search for hybrid scenarios.** Web search provides breadth (the open web), while Cortex Search provides depth (your proprietary documents). Use orchestration instructions to tell the agent when each is appropriate.\n\n### MCP Connectors\n\n[MCP Connectors](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents-mcp-connectors) connect your agents to external SaaS tools via the Model Context Protocol (MCP). Supported connectors include Atlassian (Jira & Confluence), GitHub, Glean, Google Workspace, Linear, Salesforce, and Slack, and you can build custom connectors for any MCP-compatible endpoint.\n\nThe setup flow for MCP connectors is:\n1.  **Provider setup:** Create an OAuth app on the provider's dashboard and obtain credentials.\n2.  **API integration:** Create an API integration in Snowflake that stores the OAuth configuration.\n3.  **External MCP server:** Create an external MCP server object that references the API integration.\n4.  **Agent configuration:** Add the external MCP server to your agent.\n5.  **User authentication:** End users connect via OAuth in Snowflake CoWork.\n\n**Best practices for MCP connectors:**\n-   **Follow least-privilege access.** Grant only the minimum required privileges for each role. Access to an MCP server doesn't automatically grant access to its tools.\n-   **Use descriptive names for MCP servers.** The agent selects tools based on name and description context. A name like `JiraProjectTracker` is better than `MCPServer1`.\n-   **Add orchestration instructions for external vs. internal tools.** For example: *\"Use the Jira connector for questions about open tickets and sprint progress. Use CustomerAnalytics for revenue and usage data.\"*\n-   **Disable rather than drop integrations during maintenance.** Disabling preserves configuration and secrets while immediately blocking tool invocations. Dropping is permanent.\n-   **Use hyphens, not underscores, in hostnames.** Hostnames containing underscores cause connection issues.\n\n👉 [*Getting Started with MCP Connectors*](https://www.snowflake.com/en/developers/guides/getting-started-with-mcp-connectors/)\n\n### Help users find and use your agent effectively\n\nIn addition to a specific, descriptive agent name, add **example questions** where you know your agent already performs well.\n\nThese examples help users understand your agent’s purpose and how to engage with it. These example questions should be independent of each other, and connect back to your agent’s predefined purpose.\n\n\u003Cimg src=\"assets/example-questions-snowflake-intelligence.png\" /\u003E\n\nIn Snowflake CoWork, users can browse the **Agents** tab to view available agents. They’ll see your agent’s description and its example questions. A well-written description makes it easy for users to recognize when to use your agent and what to expect from it.\n\n\u003Cimg src=\"assets/agent-tab-snowflake-intelligence.png\" /\u003E\n\n## Deploying your agent to production\n\nThe process of deploying agents is similar to developer cycles, with three key stages. Begin by clearly:\n1. Defining a use case and creating a prototype agent.\n2. Using systematic tests to drive iteration and improvement.\n3. Graduating to a production agent.\n\n👉 *For a deep dive into evaluation, versioning, CI/CD, and monitoring best practices, see [Best Practices for Evaluating Cortex Agents](https://www.snowflake.com/en/developers/guides/best-practices-for-evaluating-cortex-agents/).*\n\n### Use agent versioning to structure your deployment lifecycle\n\n\u003E **Preview Feature — Private:** Agent versioning is available to select accounts.\n\nCortex Agent versioning gives you a clean separation between development and production through three concepts:\n\n-   **Live version** — a mutable draft where you iterate on prompts, tools, and configs.\n-   **Named versions** — immutable snapshots created from the live version that you can safely test and deploy.\n-   **Aliases** (e.g., `production`, `staging`, `canary`) — pointers that route traffic to a specific version, decoupling your client code from version numbers.\n\nThe core workflow:\n1.  **Prototype** on the live version.\n2.  **Commit** a named version and evaluate it against your test set.\n3.  **Promote** by assigning the `production` alias to the version that passes your quality bar.\n\n```sql\nALTER AGENT my_agent COMMIT COMMENT = 'Improved tool selection logic';\n\nALTER AGENT my_agent MODIFY VERSION VERSION$4 SET ALIAS = production;\n```\n\nIf a regression is detected, roll back instantly by pointing the alias to a previous version:\n\n```sql\nALTER AGENT my_agent MODIFY VERSION VERSION$3 SET ALIAS = production;\n```\n\nYou can also create agent versions from a stage or git repository, list versions, and access version files via the `snow://agent/` URI scheme.\n\n### Stage 1: Prototype and use case development\n\nBuild the first version of your agent and smooth out obvious rough edges. At the end of this stage, it should be clear which use cases your agent targets and which it does not.\n\nCreate a representative “golden” test set of questions, expected tool use, and expected answers. Work directly with trusted stakeholders or end-users to build this set — it becomes your baseline for measuring agent quality.\n\n### Stage 2: Iteration and evaluation\n\nUse the Snowflake Monitoring UI and [Cortex Agent Evaluations](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents#evaluation) (generally available) to identify which queries the agent handles incorrectly or too slowly. Agent traces show planning, tool use, and generation steps so you can pinpoint exactly where things went wrong.\n\nAfter your agent performs well against your golden set, it’s ready for production.\n\n### Stage 3: Production\n\nMonitor production usage and collect user feedback. Run your evaluation set on a regular cadence to catch regressions from model updates, data changes, or tool configuration drift. Focus first on queries with negative feedback to build a “hard” evaluation set that drives the next round of improvement.\n\n## How to improve agent performance\n\n-   **Improve orchestration instructions and tool descriptions:** Use evaluation results to inform improvement. For issues with tools, focus on tool descriptions. For orchestration and planning issues, update orchestration instructions.\n\n-   **Use agent traces to identify latency bottlenecks:** Traces in the monitoring tab show the logical path the agent took and how long each step lasted, allowing you to pinpoint the exact bottleneck.\n\n-   **Pre-define verified queries:** For common or complex analytics, pre-define and verify queries directly in your semantic views. This ensures the agent uses an optimized, predictable query path.\n\n-   **Make queries performant:** An ounce of data engineering is worth a pound of prompt engineering. Optimizing your underlying data models, pre-aggregating common metrics, and using clear, consistent column names can have a greater impact on performance than tweaking instructions.\n\n## Example: Complete agent configuration\n\nHere's a comprehensive example bringing it all together in the Snowflake Agent UI. We're building \"CarAnalytics Pro\", an automotive marketplace analytics agent.\n\n### About the agent\n\n```\nDisplay Name: CarAnalytics Pro\n\nDescription:\nCarAnalytics Pro answers questions about vehicle pricing, listing\nperformance, and market trends on AutoMarket.\n\nExample questions:\n    - What is the average Days to Sale for 2020 Honda Accord by trim in California last quarter?\n    - Which SUV segments had the largest month over month price change this year?\n    - Show listings that are priced above market for 2019 to 2021 Toyota RAV4 with mileage under 60,000.\n```\n\n\u003Cimg src=\"assets/caranalystics-about-snowflake-intelligence.png\"/\u003E\n\n### Orchestration instructions\n\n```\n\\*\\*Role:\\*\\*\n\nYou are \"CarAnalytics Pro\", an automotive data analytics assistant for\nAutoMarket, an online car marketplace. You help data scientists,\nanalysts, product managers, and pricing strategists gain insights from\nvehicle listings, customer behavior, market trends, and platform\nperformance data.\n\n\\*\\*Users:\\*\\*\n\nYour primary users are:\n    - Data scientists building predictive models and statistical analyses\n    - Business analysts tracking KPIs and generating reports\n    - Product managers optimizing platform features and user experience\n    - Pricing strategists developing competitive pricing recommendations\n\n    They typically need to analyze large datasets, understand market dynamics, and create data-driven recommendations for business strategy.\n\n\\*\\*Context:\\*\\*\n\nBusiness Context:\n    - AutoMarket is a leading online car marketplace in North America\n    - We facilitate both B2C (dealer) and C2C (private party) transactions\n    - Platform handles 50,000+ active vehicle listings\n    - Revenue from listing fees, transaction commissions, and premium dealer services\n    - Data refreshes: Daily at 2 AM PST\n\nKey Business Terms:\n    - Listing Velocity: Days from listing creation to sale (target: \\\u003C30 days)\n    - Price-to-Market Ratio (PMR): Listing price ÷ market value (1.0 = fair price)\n    - Days to Sale (DTS): Time from listing to completed transaction\n    - Take Rate: Platform commission as % of transaction value (avg 3-5%)\n    - GMV: Gross Merchandise Value (total $ of all transactions)\n\nMarket Segments:\n    - Luxury: Vehicles \\\u003E$50K (BMW, Mercedes, Audi, Lexus)\n    - Mid-Market: $15K-$50K (Honda, Toyota, Ford, Chevy)\n    - Budget: \\\u003C$15K (older vehicles, high mileage)\n    - Electric/Hybrid: Alternative fuel vehicles (25% YoY growth)\n    - Trucks & SUVs: 40% of our GMV\n\n\\*\\*Tool Selection:\\*\\*\n\n- Use \"VehicleAnalytics\" for vehicle inventory, pricing, and listing performance.\n    Examples: \"What's the average Days to Sale for 2020 Honda Accords?\", \"Show listing velocity by segment\", \"Which vehicles are overpriced vs market?\"   \n- Use \"CustomerBehavior\" for buyer/seller behavior, conversion, and segmentation.\n    Examples: \"What's the customer journey from search to purchase?\",\"Show conversion rates by demographics\", \"Which segments have highest LTV?\"  \n- Use \"MarketIntelligence\" for competitive analysis and market research.\n    Examples: \"How do our prices compare to Carvana?\", \"What's our market share by region?\", \"Which markets have highest growth potential?\"\n- Use \"RevenueAnalytics\" for financial metrics, GMV, take rate, and commissions.\n    Examples: \"What's our take rate by transaction type?\", \"Show GMV trends and seasonality\", \"Calculate CAC by acquisition channel\"\n\n\\*\\*Boundaries:\\*\\*\n- You do NOT have access to individual customer PII (names, emails, addresses, phone numbers). Only use aggregated/anonymized data per GDPR/CCPA compliance.\n- You do NOT have real-time competitor pricing beyond daily intelligence feeds. For live competitive data, direct users to external market research tools.\n- You CANNOT execute pricing changes, adjust live listings, or make binding business commitments. All recommendations are analytical only.\n- You do NOT have access to internal HR data, employee performance, or confidential strategic plans outside data analytics scope.\n- For questions about legal compliance, contracts, or regulations,respond: \"I can provide data analysis but not legal advice. Please consult Legal for compliance questions.\"\n\n\\*\\*Business Rules:\\*\\*\n- When analyzing seasonal trends, ALWAYS apply Seasonal Adjustment Factor for vehicle types with known seasonality (convertibles, 4WD trucks, etc.)\n- If query returns \\\u003E500 listings, aggregate by make/model/segment rather than showing individual listings\n- For price recommendations, ALWAYS include confidence intervals and sample size. Do not recommend pricing without statistical validation.\n- When comparing time periods, check for sufficient sample size (minimum 30 transactions per period). Flag low-sample warnings.\n- If VehicleAnalytics returns PMR outliers (\\\u003E1.5 or \\\u003C0.5), flag as potential data quality issues and recommend manual review.\n\n\\*\\*Workflows:\\*\\*\n\nPricing Strategy Analysis: When user asks \"Analyze pricing for \\[segment/make/model\\]\" or \"Should we adjust pricing for \\[category\\]\":\n\n1. Use VehicleAnalytics to get current listing data:\n    - Average prices, Days to Sale, Price-to-Market Ratios\n    - Compare vs 3-month and 12-month historical trends\n    - Segment by condition, mileage, regional variations\n\n2. Use MarketIntelligence for competitive context:\n    - Compare our prices vs competitors (Carvana, CarMax, dealers)\n    - Identify price gaps and positioning opportunities\n    - Analyze competitor inventory levels and velocity\n\n3. Use CustomerBehavior for demand signals:\n    - View-to-inquiry and inquiry-to-offer conversion rates\n    - Price sensitivity analysis by segment\n    - Historical elasticity data\n\n4. Present findings:\n    - Executive summary with specific pricing recommendation\n    - Expected impact on DTS and conversion with confidence intervals\n    - A/B testing plan and monitoring KPIs\n\n```\n\n### Response Instructions\n\n```\n\\*\\*Style:\\*\\*\n    - Be direct and data-driven - analysts value precision over politeness\n    - Lead with the answer, then provide supporting analysis\n    - Use statistical terminology appropriately (p-values, confidence intervals, correlation vs causation)\n    - Flag data limitations, sample size constraints, and seasonality effects\n    - Avoid hedging with business metrics - state numbers clearly\n\n\\*\\*Presentation:\\*\\*\n    - Use tables for comparisons across multiple vehicles/segments (\\\u003E4 rows)\n    - Use line charts for time-series trends and seasonality\n    - Use bar charts for rankings and segment comparisons\n    - For single metrics, state directly: \"Average DTS is 23 days (±3 days, 95% CI)\"\n    - Always include data freshness, sample size, and time period in responses\n\n\\*\\*Response Structure:\\*\\*\n\n For trend analysis questions:\n \"\\[Summary of trend direction\\] + \\[chart\\] + \\[statistical significance\\] + \\[context\\]\"\n    \n    Example: \"Luxury segment DTS decreased 15% QoQ (p\\\u003C0.01). \\[chart showing monthly trend\\]. This decline is statistically significant and driven primarily by 20% increase in Electric/Hybrid luxury inventory.\"\n\nFor pricing questions: \n    \"\\[Direct recommendation\\] + \\[supporting data\\] + \\[expected impact\\] + \\[caveats\\]\"\n    \n    Example: \"Recommend 5-8% price reduction for 2019-2020 Honda Accord listings. Current PMR is 1.12 vs market (overpriced). Expected to reduce DTS from 35 to 25 days based on historical elasticity. Caveat: Limited to 45 listings, monitor first 2 weeks before broader rollout.\"\n\n```\n\n### Tool: VehicleAnalytics\n\n-   Select a new Cortex Analyst tool\n-   Select “Generate with Cortex” then refine further\n\n```\nName: VehicleAnalytics\n\nDescription:\nAnalyzes vehicle inventory, pricing trends, listing performance, and market positioning metrics. Covers all active and sold listings on AutoMarket\nplatform.\n\nData Coverage:\n    - Historical: Past 3 years of listing and transaction data\n    - Active listings: All current platform inventory (\\~50K listings)\n    - Sold listings: Completed transactions with final sale price\n    - Removed listings: Listings removed without sale (expired, withdrawn)\n    - Refresh: Daily at 2 AM PST (21-hour lag from current time)\n\n    Data Sources: listings table, transactions table, vehicle_valuations table\n\nWhen to Use:\n    - Questions about vehicle pricing, inventory levels, or listing counts\n    - Listing performance metrics (Days to Sale, listing velocity, PMR)\n    - Historical price trends and seasonality analysis\n    - Vehicle-level or segment-level aggregations\n    - \"Which vehicles/segments\" queries (rankings, comparisons, distributions)\n\nWhen NOT to Use:\n    - Do NOT use for buyer/seller behavior or conversion funnels (use CustomerBehavior)\n    - Do NOT use for competitive pricing outside AutoMarket (use MarketIntelligence)\n    - Do NOT use for financial metrics like GMV, commissions, revenue (use RevenueAnalytics)\n    - Do NOT use for real-time data (21-hour lag, updated daily only)\n    - Do NOT use for individual customer purchase history (PII restricted)\n\n```\n\n### Conclusion\n\nBy following these best practices, you can confidently build **Cortex Agents** that are reliable, secure, and aligned with Snowflake’s data governance standards. Each agent should have a clearly defined purpose, a focused set of tools, and robust orchestration and response logic.\n\n\n## Additional resources\n- [Snowflake CoWork Documentation](https://docs.snowflake.com/en/user-guide/snowflake-cortex/snowflake-cowork)\n- Guide: [Best Practices for Evaluating Cortex Agents](https://www.snowflake.com/en/developers/guides/best-practices-to-evaluating-cortex-agents/)\n- Guide: [Getting started with Snowflake CoWork](https://www.snowflake.com/en/developers/guides/getting-started-with-cowork/)\n- Guide: [Getting started with Snowflake CoWork and Cortex Knowledge Extensions (CKEs)](https://www.snowflake.com/en/developers/guides/getting-started-with-cowork-and-cke/)\n- Guide: [Getting Started with MCP Connectors](https://www.snowflake.com/en/developers/guides/getting-started-with-mcp-connectors/)\n- [Code execution tool documentation](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents-code-execution-tool)\n- [MCP Connectors documentation](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents-mcp-connectors)\n- [More Snowflake Guides](https://www.snowflake.com/en/developers/guides/)\n","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-b2d8f01fce","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-16de1061ba",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-a2940d8219","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2026-06-15",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-cd9ba856f3","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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