Snowflake's Agentic Control Plane

Snowflake and Accenture unveil an agentic control plane designed to transform enterprise data into governed, actionable AI-driven decisions at scale.

Diagram illustrating the components of the agentic enterprise: Enterprise Data + Context, AI Models, SaaS + Applications, and the Agentic Control Plane.
The four key components enabling the agentic enterprise, including Snowflake's data foundation and the agentic control plane.· Snowflake
Visual TL;DR
Fragmented Agent ActionsDriver
From the articleHowever, current deployments often lead to fragmented agent actions, creating conflicting decisions between different business functions.
Lack of Unified ContextDriver
From the articleThis challenge stems from a lack of unified enterprise context, which dictates industry semantics, key performance indicators, and policy guardrails.
Agentic Enterprise ArchitectureContext
centers on data, context, AI models, SaaS apps, and control plane
From the article 4 mentionsThis is the core promise of the emerging 'agentic enterprise,' where intelligent agents operate across data, models, and applications to drive business outcomes at scale.
Snowflake Agentic Control PlaneCore
orchestrates elements, enforces governance, and trans-forms data into decisions
From the article 9 mentionsSnowflake and Accenture are collaborating on a solution that centers on four key components: enterprise data and context, AI models, SaaS applications, and crucially, the agentic control plane.
Accenture Context GraphCore
provides enterprise context for decision intelligence and AI model applications
From the article 4 mentionsAccenture's Context Graph aims to transform this enterprise context into an industry-aware decision substrate.
Governed DecisionsEffect
AI-driven decisions are unified, consistent, and adhere to enterprise policies
From the article 9+ mentionsBeyond AI as the new user interface for data, the next frontier is turning insights into actionable, governed decisions.
Operationalized AIOutcome
turning enterprise data into actionable, governed business outcomes at scale
Contents(5)

The enterprise AI conversation is evolving. Beyond AI as the new user interface for data, the next frontier is turning insights into actionable, governed decisions. This is the core promise of the emerging 'agentic enterprise,' where intelligent agents operate across data, models, and applications to drive business outcomes at scale.

However, current deployments often lead to fragmented agent actions, creating conflicting decisions between different business functions. This challenge stems from a lack of unified enterprise context, which dictates industry semantics, key performance indicators, and policy guardrails.

The Agentic Enterprise Architecture

Realizing this vision requires a robust architecture. Snowflake and Accenture are collaborating on a solution that centers on four key components: enterprise data and context, AI models, SaaS applications, and crucially, the agentic control plane. This control plane is designed to orchestrate these elements, enforce governance, and translate business intent into governed agentic actions.

At the heart of this is a governed, shared foundation of data and business semantics. Snowflake itself serves as this substrate for over 13,600 organizations, converging governed data, policies, and business logic.

Accenture's Context Graph for Decision Intelligence

Accenture's Context Graph aims to transform this enterprise context into an industry-aware decision substrate. It encodes domain ontologies, value trees, and policy guardrails, ensuring that agents retrieve not just data, but decisions that align with specific industry needs and business policies.

This graph integrates with Snowflake's governed data, inheriting its lineage and policy controls. It makes the data foundation industry-aware, applying the correct business semantics and policy guardrails to every agent interaction.

For instance, in financial services, the Context Graph encodes credit risk frameworks. In consumer packaged goods, it defines channel-specific customer definitions and trade promotion logic. This industry-specific knowledge is maintained as a living asset, evolving with industry changes and client learnings.

AI Models and Applications for Action

Context alone doesn't drive outcomes. It must be coupled with AI models for reasoning and integrated into the applications where work gets done. Snowflake enables choice in AI models, allowing integration with leading options like Claude, Gemini, and ChatGPT through open standards.

Accenture complements this by providing industry jump-starters and ensuring secure, optimized integrations into enterprise systems. This ensures that intelligence is grounded in data, powered by the best models, and actionable within existing workflows.

The Snowflake Agentic Control Plane in Action

The agentic control plane, enhanced by Accenture's Context Graph, coordinates across these elements to enable enterprise-scale action. Snowflake's own tools, like Snowflake CoWork for business users and Snowflake CoCo for developers, act as agents within this plane.

Snowflake CoWork allows business users to access data and take action across applications using natural language. Snowflake CoCo empowers developers to build agentic applications, tapping into any data and system.

Accenture's Reinvention.AI platform further extends this control plane across a client's existing tools and platforms, ensuring decisions integrate seamlessly throughout the enterprise while maintaining governance.

Governed Decisions, Operationalized

With a strong context foundation and a coordinated control plane, the agentic enterprise becomes operational. Data transforms from a passive asset into an active substrate for making, governing, auditing, and executing decisions end-to-end.

This leads to consistency across functions, as agents reason from the same shared context. It compresses the time from question to governed action, enabling business users and developers to achieve outcomes in seconds rather than through multi-step handoffs.

Furthermore, every interaction enriches the context, creating a compounding foundation of structured knowledge and policy. Companies that build this system now will not just adopt agentic AI; they will reinvent on it.

The agentic enterprise is no longer a future state for those investing in their context foundation and decision intelligence layers today. Snowflake and Accenture are actively building this unified agentic foundation for their joint clients.

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Daniel Singer

Written by

Daniel Singer

Editor, StartupHub.ai

Daniel Singer is the editor of StartupHub.ai, a technology expert and thought leader on AI and its applications across sectors, from fintech and healthcare to developer tooling and consumer software. He writes and tests the tools covered here thoroughly and regularly, and built StartupHub.ai to give founders, operators and buyers a clearer read on what they are actually being sold.