About Sigma agents (Beta)

You can add Sigma agents to workbooks to provide AI interfaces to users in Sigma. You can add agents specific to business tasks, like forecasting different sales promotions, or summarizing the status of an incident in a debugging app. Users can interact with agents that you build using natural language in a conversational chat interface.

Sigma agents are specific to a workbook, based on the predefined context of the data elements in the workbook and the access granted on the workbook. Instructions and data sources provide the context, while actions and agents configured in your data platform provide the tools for an agent to take action or enrich insights.

An AI agent is not a large language model (LLM), but rather uses LLMs in combination with context and tools like other warehouse agents, MCP servers, and actions in Sigma. AI models generate probabilistic outputs, but you can ensure consistent execution of critical tasks by providing deterministic action tools to your agent. Action tools run pre-defined actions, ensuring that the actions run consistently and predictably every time. The agent uses probability-based reasoning to decide when to use a tool.

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While Sigma agents are in beta, anyone in your organization with access to a workbook can use agents. A user can chat with an agent if a chat element is added to the workbook. Users with edit access to a workbook can schedule an agent to run automatically. Agents can only use tools, such as actions or MCP tools, that the user has access to or permission to use.

Decide which AI tool to use in Sigma

When choosing whether to build a Sigma agent, use a warehouse agent in Sigma, or use Sigma Assistant, consider the following:

• How much customization or setup do you want to do?
• Do you want to build powerful task-specific interaction or perform general data analysis?
• Where is your data modeled and secured?

Example use cases

For example, you might build an agent to address one of the following use cases:

• Automated forecasting and review workflow: An agent can run forecasting calculations based on the contents of an input table and informed by policy documents in an external system, then present results in a structured format for review and approval.
• Personalized sales copilot: An agent can review pipeline opportunities that a specific user is working on, flagging customers with upcoming renewal dates and drafting Slack message follow-ups that the user can send to specific channels.
• Analyze key drivers of customer satisfaction: Based on customer satisfaction survey data, an agent can identify, explain, and rank the drivers that contribute to low or high customer satisfaction scores. For specific customers represented in the survey data, the agent can send Slack or Microsoft Teams messages, such as to notify customer success managers about accounts that might be a churn risk.
• Detect anomalies in purchasing behavior: An agent can review sales data across customer segments and identify unusual transaction patterns, based on both deterministic guidelines that you provide and probabilistic reasoning for newly identified patterns. After detecting an anomaly, the agent can provide a summary of the impact of the anomalous behavior and next steps to take. For details, see Example: Notify a user when an anomaly is detected.
• Cluster and segment prospective customers: An agent can review metadata for prospective customers and identify meaningful clusters. For each cluster, the agent can summarize why each segment is different, then suggest specific actions to take for each group. For example, accelerate prospective customer outreach by prompting the agent to draft an email message and send it to a list of relevant recipients in each cluster, then call an API to update an internal outreach tracker.
• Sophisticated analysis of complex datasets: An agent can perform sophisticated analysis such as "Identify unused servers by analyzing the currently running applications and server inventory" by reasoning across multiple related data sources, even if those sources are in different connections.
• Perform scenario analysis for strategic planning: An agent can analyze possible scenarios that could affect the product supply chain by generating what-if scenarios based on user inputs and data, running scenarios using external models defined in Python code or stored procedures. When performing scenario analysis, the agent can state assumptions and uncertainty, and evaluate the output of a possible scenario compared with real outcomes provided in an input table.

For more end-to-end examples, see Example use cases for Sigma agents (Beta).