Deploy Sigma Agents. Secure by default.

Sigma Agents inherit your warehouse's row-level security and role-based access so that your team can begin securely orchestrating action across the enterprise on day one.

Trusted by +2000 leading enterprises around the world
Agents where your data lives
Every agent action compiles to SQL and executes on your warehouse. No extraction, no stale copies, no separate vector databases.
Everyone is a builder
Define agent behavior, conditions, and thresholds in a spreadsheet interface. No Python, SQL, or engineering tickets required.
Insight to action, instantly
Agents write to the warehouse, trigger opportunities, update Jira tickets, or fire Slack alerts. The insight-to-action gap disappears.

Same platform. Same governance.

No dead ends or new integrations. Sigma Agents take governed action across your stack.

Agents that can detect, reason, and act

Agents run on a schedule, monitoring billions of rows of live warehouse data. When critical conditions are met — inventory depletion, contract over-utilization, anomalous spend — the agent executes actions or dispatches notifications automatically.
Schedule-driven execution on live warehouse data
Threshold and anomaly detection across billions of rows
Automatic notifications via Slack, email, or webhooks
Full audit trail of every detection and action
GET STARTED WITH ALERT AUTOMATION

Transparent chat-driven exploration

Users query the agent in natural language. The agent responds with complete visibility into its logic including its planning process and every table queried, every calculation performed. No black boxes. The user can ask the agent to take a subsequent action based on the findings.
Natural language conversation with the agent
Transparent chain-of-thought reasoning
Human-in-the-loop approval before execution
Frictionless progression from insight to action
LEARN MORE ABOUT SIGMA AI APPS

Close the insight-to-action loop

Realize the value of your technology investments today. Using the Sigma Actions framework, agents execute REST API calls to external tools. Create a Salesforce opportunity, update a Jira ticket, dispatch a Slack alert, or trigger a stored procedure to transform analytical signal into operational execution.
API calls to CRMs, ticketing, and messaging tools
Webhook triggers for custom integrations
Stored procedure execution in your warehouse
Governed writeback to warehouse tables
GET STARTED WITH SIGMA API ACTIONS

Sigma takes you from insight to action with agents

01

Reuse existing logic

Maximize the business logic your team already built. Skip the RAG pipelines and vector databases to realize value immediately.

02

Production-ready action

Accelerate agentic enterprise workflows with proven methods like writeback, webhooks, API actions, and scheduled triggers.

03

Inherited governance

Eliminate shadow AI, new integrations, and compliance surprises. Sigma Agents respect security and permissions by default.

04

Explorable AI Apps

Evolve beyond basic text responses to live data products. Sigma Agents produce live AI Apps so your users can take action immediately.

When an agent is triggered, the LLM plans which tools to call and in what order based on context.
Sigma then compiles these operations and reasons over your business logic, executing the best path via data fetches, in-browser calculations, or warehouse pushdowns.
Warehouse-native reasoning
All agent processing runs on warehouse compute, grounded in existing semantic models and logic.
Auditable actions
When an agent takes an action, it writes back to the warehouse with a full audit trail.
Inherited governance
Sigma Agents pass the identity or role to the warehouse. Agents never see data the user shouldn't.
Transparent lineage
See every planning step that Sigma Agents take. Review the table, calculation, and its reasoning.

Enterprise-Grade Scale

Sigma is built for teams that need flexibility without sacrificing governance or performance.
Warehouse RBAC
Passes the user's explicit Snowflake or Databricks role during query execution. Leverages your existing, mature role hierarchies without manual mapping.
Secure Writeback
Writeback is executed natively within the warehouse using the Sigma Actions framework. All operations inherit row-level and column-level security policies.
OAuth Passthrough
Authenticated user identity passed directly to the warehouse execution layer. Agent access is constrained by database rules.
Sigma User Attributes
RLS / CLS enforced at the apps layer via SCIM-synced attributes enables secure multi-tenant embeddings where external customers share infrastructure.
Immutable Audit Trail
Every agent-initiated read, write, and API call is logged directly in the warehouse. Compliance gets a full audit trail of who accessed, when, and what was changed.
Session Variables
Dynamic variable injection into the engine at runtime allows personalization. Lightweight, high-performance filtering without per-user warehouse accounts.

Sigma Agents FAQ

The questions we think every enterprise should ask when evaluating AI Agents.

When an AI agent takes an action, is the change written back to the warehouse securely and fully auditable?

Yes. Every write from a Sigma Agent is executed natively within the warehouse using the Sigma Actions framework. All operations inherit existing row-level and column-level security policies. Every action is logged in the warehouse's audit trail, giving compliance teams an immutable record of who accessed what, when, and what was changed.

Do agents inherit our existing warehouse security, or do we need to configure permissions separately?

Security is entirely inherited. Sigma passes the authenticated user's identity directly to your warehouse via OAuth Passthrough. The agent physically cannot reason over data the executing user is restricted from viewing. There are zero duplicate permission models to maintain.

What happens when the underlying data model changes — who owns the rework?

Because Sigma compiles every agent action to SQL against your warehouse's semantic layer, changes to your data model are reflected automatically. Unlike custom Python agents or LangChain scripts, there is no brittle code to rewrite when a table schema evolves.

Can we start with human-in-the-loop and graduate to full autonomy?

Absolutely. Sigma Agents operate across three modes: Interactive (chat-driven with human approval), Autonomous (scheduled monitoring and execution), and External Actions (API calls to third-party systems). You can start fully supervised and scale autonomy as institutional trust grows.

How is this different from a BI copilot that answers questions?

A copilot answers questions — an agent takes action. Sigma Agents don't just surface insights; they execute writes, trigger REST API calls, fire webhooks, and interface with external systems like Salesforce, Jira, and Slack. The critical distinction: Agents act, everything else informs.

Does data leave the warehouse when agents process it?

No. All agent processing runs on your warehouse compute. Sigma compiles agent operations into SQL or native platform functions and pushes them to the warehouse. No data is sent to external AI services unless you explicitly configure an external function.

How does Sigma handle the MCP endpoint sprawl problem?

Sigma will operate as both an MCP client and an MCP server. As a client, agents pull context from external CRMs and document stores. As a server, Sigma exposes governed data, dbt semantic models, and tested workflows to external AI tools to act as a single governed endpoint.

What if our organization is not ready for autonomous agents?

Most aren't. Build confidence by identifying and beginning to iteratively automate your enterprise's most high-value workflows. Enhance and accelerate them with AI to guide where building Sigma Agents could add the most value. Leverage the business logic already built and integrate agents that detect anomalies, trigger API actions, and fire webhooks depending on the use case to complete your agentic enterprise evolution when ready.