BUILD AGENTS AT THE CONTEXT LAYER COMPATIBLE WITH THE CLOUD DATA WAREHOUSE YOU ALREADY OWN.

AI Agents That Act on Your Warehouse.

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

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 secure 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

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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

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Modular governance using Role-Based Access Control (RBAC)

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

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Accurate insights across your data stack

Deploy Sigma Agents across the enterprise.

Accelerate interdepartmental workflows with custom Sigma Agents for each function.

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Trusted by +2000 leading enterprises around the world

When an agent is triggered, the LLM plans which tools to call and in what order based on context.

Sigma then compiles these operations, reasoning over your business logic rather than raw base tables, and executes the best path—whether that is fetching data, performing in-browser calculations, or pushing multi-step actions down to the warehouse.

Warehouse-native reasoning

All agent processing runs on warehouse compute, grounded in existing semantic models and logic.

Inherited security

Sigma Agents inherits your existing row-level security and access policies. The agent never sees data the user shouldn't.

Auditable actions

When an agent takes an action, it writes back to the warehouse with a full audit trail.

Transparent lineage

You can see every planning step that the Sigma Agent took. Review the table, calculation, and why it said what it said to eliminate black-box AI execution.

Maximize the value of existing investments with real-time context

Sigma Agents run on business logic your team already built. Skip the vector databases and RAG pipelines and begin capturing human-in-the-loop insights directly today.

Leverage secure action frameworks in your production environment

Writeback, webhooks, API actions, input tables, and scheduled triggers are trusted, proven methods across thousands of apps. Accelerate worklflows today.

Enforce existing governance to scale agent deployment faster

Every Sigma Agent action respects existing read and write permissions so no one sees or updates the wrong data. No shadow AI or compliance surprises.

Multi-modal, interactive artifacts and data products as outputs

Stop settling for basic text markdown responses. Sigma Agents produce live AI Apps connected to your warehouse and can even help you build more Agents.

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Enterprise-Grade Scale

Sigma is built for teams that need flexibility without sacrificing governance or performance.

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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 existing row-level and column-level security policies.

OAuth Passthrough

Authenticated user identity passed directly to the warehouse execution layer. Agent access is physically constrained by database rules. It cannot reason over restricted data.

Sigma User Attributes

RLS / CLS enforced at the application layer via SCIM-synced attributes. Enables secure multi-tenant embedded AI Applications where external customers share infrastructure.

Immutable Audit Trail

Every agent-initiated read, write, and API call is logged directly in the warehouse. Compliance teams get a full audit trail of who accessed what, when, and what was changed.

Session Variables

Dynamic variable injection (e.g., region, department) into the engine at runtime allows personalization. Lightweight, high-performance filtering without per-user warehouse accounts.

Fits into the rest of your stack

Sigma connects to your warehouse, and it also plays well with the systems around it whether its catalog, transformation, monitoring, or reverse ETL.

Reuse standardized metrics and keep business logic centralized.

Let users discover governed tables and definitions where they already look.

Keep an eye on pipeline and data quality issues that impact downstream analysis.

Operationalize curated outputs from the warehouse into downstream tools.

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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.

AI Agents That Act on Your Warehouse.