Example use cases for Sigma agents (Beta)

You can use Sigma agents to perform actions and help you accomplish tasks. Refer to the following example implementations:

• Example: Add conversational memory as an action tool
• Example: Notify a user when an anomaly is detected
• Example: Run a forecasting model
• Example: Retrieve details from a third-party API
• Example: Nightly usage summary agent

Example: Add conversational memory as an action tool

If you want to help the agent remember important context from conversations, add an input table to your workbook and create an action tool for the agent to use.

1. Add an input table to the workbook titled Conversation memory. Add one date column called Date, and two text columns called Conversation summary and Key detail.

2. Select an agent and follow the steps to add an action tool:

   1. For Tools, click + (Add tool), then select Action.

   2. For Name, enter Remember details.

   3. For Instructions, enter Keep track of important details in a conversation when prompted, adding a short summary and the key detail to remember.

   4. For Steps, click Add step (+).

   5. For the step, select a Step type of Run an action.

   6. For Action, select Insert row, then select the Conversation memory input table.

   7. For Map with values, do the following:

      • For the Date column, select Formula and enter Now() to record the date when the key detail was added to the table.
      • For the Conversation summary column, select Agent input. Replace the placeholder text with Conversation summary.
      • For the Key detail column, select Agent input and replace the placeholder text with Key detail to remember.

   8. Select Close (x) to return to editing the agent.

3. Update the agent instructions with guidance for using the new action tool, using an @-mention to reference the tool. Add a sentence like When asked to remember a key detail, use the Remember details tool.

4. (Optional) If you want the agent to reference the key details stored in the Conversation memory input table as context, add the input table as a data source.

5. Click Save to save the agent.

6. Add a chat element to interact with the agent.

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Using this method creates conversation memory that is shared with all users of the workbook. If you want the agent to store key details specific to each user, add a Created by system column from the Row edit history, then follow the steps to set up row-level security using the user email address.

Example: Notify a user when an anomaly is detected

For example, if you want to monitor anomalous page visits to your website, you might have a workbook with your website analytics data.

You can configure an alert that sends a Slack message when page views exceed a known threshold, but you can also use a Sigma agent to review non-deterministic anomalies and send an alert.

You can recreate this example with the Sigma Sample Database Google Analytics Events data.

1. In a workbook, add the Sigma Sample Database Google Analytics Events table and a text area control titled Message body.

2. Create a Sigma agent and name it Anomaly Detection Agent.

3. For the agent, add a data element with the website analytics data table. If you use complex calculations to identify page views or unique users, use a table with metrics for those calculations defined.

4. Provide instructions to the agent with guidance like the following:

   Text

   You are a website analytics power agent. Review the provided data sources and identify anomalous activity, such as a spike in the number of page views compared to previous days or previous hours, an unexpected increase in unique users early in the morning for a Pacific time zone, or a much lower number of engaged sessions than usual.
   
   Refer to the company marketing calendar stored in Microsoft SharePoint for details about launch announcements or social media activity to help explain anomalies.
   
   After identifying an anomaly, do the following:
   
   1. Summarize details about the anomaly and write an explanation about why it occurred.
   2. Generate simple HTML to style the details and explanation as a message, then use the **Stage message contents** action. Never use placeholder values.
   3. When the message is ready to send, call the **Notify about anomalies** tool to notify a human about the anomalous traffic.

5. To store the message contents, follow the steps to add an action tool to the agent to update the text area control:

   1. For Tools, click + (Add tool), then select Action.
   2. For Name, enter Stage message contents.
   3. For Instructions, enter Write simple valid HTML to provide as a Microsoft Teams message. Refer to https://learn.microsoft.com/en-us/microsoftteams/platform/bots/how-to/format-your-bot-messages for supported syntax.
   4. For Steps, click Add step (+).
   5. For the step, select a Step type of Run an action.
   6. For Action, select Set control value.
   7. For Update control, choose the Message body text area control.
   8. For Set value as, select Agent input.
   9. Select Close (x) to return to editing the agent.

6. To send a notification about identified anomalies, follow the steps to add an action tool to the agent:

   1. For Tools, click + (Add tool), then select Action.
   2. For Name, enter Notify about anomalies.
   3. For Instructions, enter When you identify an anomaly, notify a human in Microsoft Teams.
   4. For Steps, click Add step (+).
   5. For the step, select a Step type of Run an action.
   6. For Action, select Notify and export, then select a Destination of Microsoft Teams.
   7. For To, choose Specific channels and enter the URL to the relevant alert Microsoft Teams channel.
   8. For Message, press =, then type [message-body] to reference the text area control updated by the agent.
   9. (Optional) Turn off the Link to workbook toggle.
   10. (Optional) Turn off the Attachment toggle.
   11. Select Close (x) to return to editing the agent.

7. Click Save to save the agent.

8. Schedule the agent to run on a regular cadence.

Example: Run a forecasting model

If you want to combine deterministic forecasting models with agent-informed forecasting, you can add a Sigma agent to a workbook with an existing Python code element that you use to run a forecasting model.

1. In a workbook, add a Python element with the code for the forecasting model. Include the sigma.output() method to make the code available as a child element, and create a table with the code output. Add another table with relevant inputs for the forecasting model, such as a historical product inventory table.

2. Create a Sigma agent.

3. Provide instructions to the agent with guidance like the following. Use an @-mention to reference the specific table:

   Text

   You are an expert at forecasting inventory. If you need to retrieve deterministic inventory forecasts, run the inventory forecasting model and review the results. The output is available in the Inventory Output table.

4. For Data sources, add both the historical product inventory table and the table containing the output.

5. To make the agent capable of running the forecasting model, add an action tool to the agent to run the Python element:

   1. For Tools, click + (Add tool), then select Action.
   2. For Name, enter Run forecasting model.
   3. For Instructions, enter Retrieve deterministic inventory forecasting using a trend projection technique.
   4. For Steps, click Add step (+).
   5. For the step, select a Step type of Run an action.
   6. For Action, select Run Python element.
   7. For Element, choose the Python element that contains the inventory forecasting model.
   8. Select Close (x) to return to editing the agent.

6. Click Save to save the agent.

7. Add a chat element to interact with the agent, or schedule the agent to run on a regular cadence.

Example: Retrieve details from a third-party API

A Sigma agent can retrieve details from a third-party API, summarize the response, and use the response to take further action in Sigma.

For example, if you want to build a Sigma agent to help search for an apartment in New York City, NY, USA, you might want to inform your search with data from 311 to identify whether and what type of incidents are commonly reported for the apartment building address.

1. In your Sigma organization, configure API credentials and connectors for the NYC Open Data API.

2. In your workbook, add a text input to use for entering relevant apartment addresses, and an input table named Recommendations to store recommendations from the Sigma agent.

3. Create a Sigma agent.

4. Provide instructions to the agent with guidance like the following, using an @-mention to refer to the exact tool:

   Help me look for an apartment! Review information available to you on the web and also in 311 to make recommendations about specific apartment building addresses. If I ask you to look at 311 data, use the "311 incidents" tool.

5. For Data sources, add the Recommendations input table.

6. To make the agent capable of calling an API with 311 incident data in NYC, add an action tool to call an API action:

   1. For Tools, click + (Add tool), then select Action.

   2. For Name, enter Retrieve 311 incidents.

   3. For Instructions, enter Call 311 with the specified address and summarize the response.

   4. For Steps, click Add step (+).

   5. For the step, select a Step type of Run an action.

   6. For Action, select Call API.

   7. For Select an API connector, choose the NYC Open Data connector.

   8. For Map with values, if any API parameters are dynamically set, such as the incident address, choose Control and specify the text input control.

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      To let the agent provide the input, select Agent input instead of Control.

   9. Select Close (x) to return to editing the agent.

7. Click Save to save the agent.

8. Add a chat element to interact with the agent, or schedule the agent to run on a regular cadence.

Example: Nightly usage summary agent

In this example, an agent runs every night to review the last 24 hours of usage data across multiple sources and send an email with the summary.

The agent is configured with the following:

• Multiple sources of usage data in the workbook for data from the last 24 hours.

• Instructions to provide a concise summary for a business systems analyst:

  You are a usage summary agent. Review the last 24 hours of usage data for outliers, patterns, and key insights. Be concise and action-oriented.

To set up this workflow:

1. Create an automated action sequence with the following actions:

   1. Call agent action with the following configuration:

      1. Select the agent.

      2. For Prompt, enter guidance to provide a concise summary of the latest usage data to send via email, with an action-oriented email subject. For example, Provide a 3 sentence summary of usage data patterns for a business systems analyst and write an email subject that uses a call to action.

      3. For Output, add the following action variables:

         • email-subject with a data type of Text.
         • email-body with a data type of Text.

   2. Notify and export action with the following configuration:

      1. Select a destination of Email, then choose recipients.
      2. For Subject, press =, then type [email-subject] to reference the email subject action variable from the Call agent action.
      3. For Message, press =, then type [email-body] to reference the email body action variable from the Call agent action.
      4. Complete the remaining configuration options.

2. After configuring the action sequence, follow the steps to run the action sequence automatically at your preferred frequency, such as every morning at 6 AM.

3. Publish the workbook to activate the schedule.