- 概述
- UiPath 生成式 AI 活动
- Act! 365
- ActiveCampaign
- Adobe Acrobat Sign
- Adobe PDF 服务
- Amazon Bedrock
- Amazon Connect
- Amazon Polly
- 亚马逊 SES
- Amazon Transcribe
- Anthropic Claude
- Asana
- AWeber
- Azure AI 文档智能
- Azure Maps
- BambooHR
- Box
- Brevo
- Calendly
- Campaign Monitor
- Cisco Webex Teams
- Citrix ShareFile
- 清除位
- Confluence Cloud
- Constant Contact
- Coupa
- CrewAI – 预览版
- Customer.io
- Databricks Agent
- Datadog
- 深度查找
- Deputy
- Discord - 预览
- DocuSign
- 水滴
- Dropbox
- Dropbox Business
- Egnyte
- Eventbrite
- 汇率
- Expensify
- Facebook
- Freshbooks
- Freshdesk
- Freshsales
- Freshservice
- 获取响应
- GitHub
- Google Maps
- Google 语音转文本
- Google 文本转语音
- Google Vertex
- Google Vision
- GoToWebinar
- Greenhouse
- Hootsuite
- HTTP Webhook
- HubSpot CRM
- HubSpot Marketing
- Icertis
- iContact
- Insightly CRM
- Intercom
- Jina.ai
- Jira
- Keap
- Klaviyo
- LinkedIn
- Mailchimp
- Mailjet
- MailerLite
- Mailgun
- Marketo
- Microsoft Azure OpenAI
- Microsoft Azure AI Foundry
- Microsoft Dynamics CRM
- Microsoft Power Automate
- Microsoft Sentiment
- Microsoft Teams
- Microsoft Translator
- Microsoft Vision
- Miro
- 奥克塔
- OpenAI
- 符合 OpenAI V1 的 LLM
- Oracle Eloqua
- Oracle NetSuite
- PagerDuty
- Paypal
- PDFMonkey
- Perplexity
- Pinecone
- Pipedrive
- QuickBooks Online
- Quip
- Salesforce
- Salesforce Marketing Cloud
- SAP BAPI
- SAP Cloud for Customer
- SAP Concur
- SAP OData
- SendGrid
- ServiceNow
- Shopify
- Slack
- SmartRecruiters
- Smartsheet
- Snowflake
- Snowflake Cortex
- 发行说明
- About the Snowflake Cortex activities
- Interact Agent
- Stripe
- Sugar Enterprise
- Sugar Professional
- Sugar Sell
- Sugar Serve
- 探戈卡
- Todoist
- Trello
- Twilio
- IBM WatsonX
- WhatsApp Business
- WOO COMMERCE
- 可行
- Workday
- Workday REST
- X(以前称为 Twitter)
- Xero
- Youtube
- Zendesk
- Zoho Campaigns
- Zoho Desk
- Zoho Mail
- 缩放
- Zoom 信息

Integration Service 活动
This activity enables the use of Snowflake Cortex agents as participants in an automated process orchestrated by Maestro.
Snowflake provides a no-code experience to create a Cortex Agent. As soon as it is saved, it becomes available for use in Maestro. The no-code experience includes the ability to test prompts and evaluate the agent output. The Cortex agent will reply to Maestro in the same way it replies to the user prompts in the Snowflake Dash Board. In most Maestro scenarios, you will prompt the agent to generate output in the form of a JSON structure. e.g. {"sku1": "9735A45", "sku2": "1735A50"}.
To use this activity in a Maestro agentic process, follow these steps:
- Add a service task element to the canvas and open the task's Properties panel.
- Name the service task
Snowflake Hello World
. - In the Implementation section, from the Action dropdown list, select Start and wait for external agent.
- Select the Snowflake Cortex connector.
- Select an existing connection or create a new one. For more information, see Snowflake Cortex authentication.
-
From Activity, select Interact Agent.
- From Database, select a database, for example
SNOWFLAKE_INTELLIGENCE
. - From Schema, select a schema, for example
AGENTS
. - From Agent Name, select an agent previously created in Snowflake.
-
In Prompt, enter "What can you do?". Make sure to include the quotes in the prompt.
-
Connect the start event to the service task, and the service task to an end event node in the canvas.
-
Select Debug to run this process. After a successful run, review the global variables and look for the {:} response from the source: Snowflake Hello World. Take note of the structure of the reply.
For example, this is the agent's response to the prompt "What can you do?":
{ "type": "text", "text": "\nI can help you analyze and optimize your manufacturing, inventory, order fulfillment, and sales forecasting processes. Here’s what I can do:\n\n- Query and analyze your inventory, orders, production forecasts, and sales forecasts using advanced SQL queries.\n- Answer questions about current inventory levels, order statuses, and customer orders.\n- Help you determine if current or future orders can be fulfilled based on available or forecasted inventory.\n- Provide insights into upcoming production and expected sales for specific products or SKUs.\n- Generate tables and visualizations (bar, line, and pie charts) to help you understand trends and patterns in your data.\n- Assist with business analytics, SaaS metrics, and research methodology for data-driven decision-making.\n\nYou don’t need to know SQL—just ask your business questions, and I’ll use the appropriate tools to get you answers!\n" }
{ "type": "text", "text": "\nI can help you analyze and optimize your manufacturing, inventory, order fulfillment, and sales forecasting processes. Here’s what I can do:\n\n- Query and analyze your inventory, orders, production forecasts, and sales forecasts using advanced SQL queries.\n- Answer questions about current inventory levels, order statuses, and customer orders.\n- Help you determine if current or future orders can be fulfilled based on available or forecasted inventory.\n- Provide insights into upcoming production and expected sales for specific products or SKUs.\n- Generate tables and visualizations (bar, line, and pie charts) to help you understand trends and patterns in your data.\n- Assist with business analytics, SaaS metrics, and research methodology for data-driven decision-making.\n\nYou don’t need to know SQL—just ask your business questions, and I’ll use the appropriate tools to get you answers!\n" }
The agent’s output must be assigned to a process variable so it can influence the progress of the Maestro process, for example to make a decision based on a boolean evaluation, or to use the answer from a classification task.
-
In Design mode, select the agent from the design canvas.
-
Select Properties.
-
Under Output, select Add new and add a variable of type String named agent_reponse.
-
For Value, select Snowflake Hello World > Response > Agent action text (string). This represents the text component of the reply.
Beyond establishing connectivity, you should test prompts both in the Snowflake workspace as well as from Maestro. This ensures you achieve the desired output that can best be consumed by Maestro, assigned to variables, and passed on to other actors in the process.
We recommend that detailed prompts remain within the system prompts of the agent within Snowflake. The user prompt which is provided by Maestro to the agent at runtime should be brief and to the point. Its role is primarily to indicate the relevant variables needed by the agent to perform a specific tasks and generate an expected consistent output.
"What is the quantity on inventory of Order ID " + vars.orderId_1 + "respond only with a JSON object with the quantity in the key Order_Quantity. No explanations, only JSON"
"What is the quantity on inventory of Order ID " + vars.orderId_1 + "respond only with a JSON object with the quantity in the key Order_Quantity. No explanations, only JSON"
The agent will reply with:
{"Order_Quantity":"100"}
{"Order_Quantity":"100"}
string
into JSON
using the js:JSON.parse(variable of type string)
function. Pay special attention to types in your request to the agent and in the actual response. Even if the response looks like type JSON
, it may actually be of type string
.