Snowflake announced April 21 that it is expanding Snowflake Intelligence and Cortex Code into what the company is now calling the "control plane for the agentic enterprise." Translated out of marketing: Snowflake is positioning its data platform as the layer that holds context, governance, and execution for every AI agent running inside a Fortune 500 company — whether the agent was built in Snowflake, in Databricks, in AWS Bedrock, in Azure AI Foundry, or by an independent vendor.
The announcement is not a single product. It is a dozen integration and SDK updates that together say one thing to CIOs and data platform owners: if you have already centralized data in Snowflake, the shortest path to production-grade agentic AI is to build on the platform you already pay for, not to stand up a parallel stack.
That pitch lands at a particular moment. Databricks is running the same play from the opposite direction with its Mosaic AI and Agent Bricks product line. Google Cloud is doing it at Next 2026 this week with Gemini Enterprise. Microsoft is doing it with Fabric and Foundry. AWS just launched Agent Registry in preview. Five well-capitalized incumbents are all claiming the same real estate at the same time. The enterprise buyer's problem is picking one — or, more realistically, deciding which two to combine without creating a governance nightmare.
Snowflake's April 21 update is its strongest shot yet at being the default pick.
What Snowflake Actually Shipped
The announcement bundles three categories of change: business-user agents, builder tooling, and cross-platform plumbing.
Snowflake Intelligence — for business users. This is the "ChatGPT for your enterprise data" product, and the April update adds the pieces that make it usable outside the analyst desk. New Model Context Protocol (MCP) connectors let Intelligence reach directly into Gmail, Google Calendar, Google Docs, Jira, Salesforce, and Slack. A public-preview iOS app lets users query and take action from a phone. A new "deep research" mode produces multi-step, cited reports by reasoning across structured tables, unstructured documents, and external context. An "Artifacts" feature saves and shares those analyses as reusable assets. And a new "Skills" capability, going generally available soon, packages repeatable task flows that Intelligence can execute on demand.
Cortex Code — for builders. This is Snowflake's AI coding agent, first launched in November 2025. The April update expands it in four directions: extended data access to non-Snowflake sources (AWS Glue, Databricks, PostgreSQL); cross-platform agent interop via MCP and the Agent Communication Protocol (ACP); IDE integration through a VS Code extension and a Claude Code plugin; and a new Agent SDK for Python and TypeScript so teams can embed Cortex Code into their own applications. Cortex Code Sandboxes — isolated execution environments inside Snowsight — round out the developer surface.
Cross-platform plumbing. MCP is the thread that runs through the announcement. By adopting Anthropic's MCP as a first-class protocol — alongside the newer Agent Communication Protocol — Snowflake is betting that the agent ecosystem will converge on open standards rather than proprietary APIs. That matters because it tells enterprise buyers that Intelligence and Cortex Code will plug into whatever agent framework they pick, rather than locking them in.
The Numbers That Matter
Two adoption metrics from the announcement frame the commercial story.
First, 9,100 customers using AI products weekly. That is the installed base Snowflake is selling the agentic upgrade into. For context, Snowflake's total customer count is roughly 11,000. More than 80% of customers are now using some AI capability on the platform every week — a penetration rate that most enterprise software vendors would pay handsomely for.
Second, more than half of customers have adopted Cortex Code since the November 2025 launch. That is a 5-month adoption curve for a developer-facing AI product. For comparison, when Databricks launched its Agent Bricks product in June 2025, it took close to nine months to cross the 25% customer adoption mark. If Snowflake's numbers hold up through year-end, Cortex Code is on track to be the fastest-adopted enterprise AI coding product shipped by any of the major data platforms.
The practical implication: Snowflake is not selling agentic AI to a skeptical audience. It is selling an expanded platform to customers who are already paying to use AI features and who have a credit-consumption model that bends toward more usage, not less.
Photo by Manuel Geissinger on Pexels
Why The Control Plane Framing Is The Real Play
Every major data and cloud vendor is now claiming to be the "control plane" for agentic AI. Microsoft calls Fabric and Foundry the control plane. Databricks calls Unity Catalog plus Mosaic the control plane. Google Cloud this week is calling Gemini Enterprise and Vertex AI the control plane. AWS is pitching AgentCore and Bedrock as the control plane. Snowflake is pitching Intelligence plus Cortex Code as the control plane.
The control plane framing matters because of what it implies about money. A "control plane" is where governance happens — which agents are registered, what data they can access, what actions they can execute, which user authorized them. Whoever owns the control plane owns the audit trail, the policy engine, and the identity surface. That vendor then has enormous gravitational pull on where the compute and the data sit, because moving either one means rewiring governance.
Snowflake's competitive advantage in this race is specific: the data is already there. For any enterprise that has consolidated its analytics and warehouse workloads into Snowflake over the last five to eight years, the company's argument is that building agentic workflows on Intelligence and Cortex Code avoids the data-egress penalty and the identity-federation overhead that shows up the minute you run your agents in Databricks, AWS Bedrock, or Azure AI Foundry against Snowflake data.
This argument is not new. It is the same argument Snowflake has been making since Snowpark launched in 2021: "run compute where the data lives." What is new in the April 21 update is that Snowflake is now extending that argument out beyond the warehouse wall. By adding MCP and ACP integrations and letting Cortex Code read from Databricks, AWS Glue, and PostgreSQL, Snowflake is positioning itself as the neutral broker that happens to be on the enterprise's preferred storage layer — rather than a walled garden.
That framing forces a harder question for Databricks. Databricks can say "run your agents on our Mosaic AI stack," but for a customer whose gold-layer tables sit in Snowflake, that is a multi-quarter data migration project. The path of least resistance is the incumbent, and on the data side, that increasingly means Snowflake.
What CIOs Should Do With This
For CIOs and enterprise data leaders, the April 21 announcement creates three practical decisions for the next two quarters.
1. Inventory your enterprise agent sprawl — and pick a governance vendor. If your organization already has agents running in multiple places (copilots inside Salesforce, Gemini agents in Google Workspace, Copilot Studio agents in Microsoft 365, custom agents built by the data team), you have an agent governance problem whether you acknowledge it or not. The decision is not whether to have a control plane — it is whether to build one ad hoc or adopt one from a vendor you already pay. Snowflake's MCP-forward posture makes it a plausible default for any organization that already centralizes data there. AWS Agent Registry, Microsoft Entra Agent ID, and ServiceNow's AI Control Tower are the non-data-platform alternatives. Pick one, write the policy, and stop drifting.
2. Review your Snowflake contract and credit model before the next Snowflake Summit. Intelligence and Cortex Code consume Snowflake credits. A Cortex Code adoption that goes from 50% of teams to 100% of teams in six months creates a compute bill that looks very different from last year's. The treasury conversation you want is about credit commitments, reserved capacity, and the Skills-consumption economics before your teams scale usage — not after. Snowflake Summit is in June; the pricing conversation should happen before, not during.
3. Decide on your agent-building IDE story. The new VS Code extension and the Claude Code plugin mean your builders can now stay in their preferred editor while Cortex Code runs behind the scenes. That is a genuine developer-experience upgrade. But it also means the choice of coding assistant (Claude Code, Cursor, Copilot) is now a first-order architectural decision, because it determines how your engineers interact with agentic workflows. If your organization has not standardized on an AI coding environment, now is the time.
Photo by fauxels on Pexels
What CFOs Should Do With This
For CFOs, the announcement reframes a line item. Two moves.
Treat Snowflake as a platform bet, not a warehouse bet. The per-credit economics of running Snowflake Intelligence are materially different from running pure analytical queries. A business user who runs five cited "deep research" reports per week is consuming compute in a new category — one that scales with headcount, not with data volume. Build the unit economics into your 2027 plan now so the Q3 2026 Snowflake invoice does not ambush your FP&A team.
Ask the platform question on every AI vendor evaluation. If a Snowflake control-plane story is credible for your data environment, then every standalone agentic AI vendor pitching into your organization needs to answer "why not do this in Snowflake" before you sign. That framing will save real money on point-solution sprawl. It will also force vendors to sharpen their differentiation — which is a benefit to the buyer whichever way the evaluation comes out.
The Competitive Read
The Snowflake announcement is best read alongside four parallel moves this month:
- Google Cloud Next 2026 (this week) — Gemini Enterprise positioned as the agentic cloud with Vertex AI and Agent Designer.
- AWS Agent Registry preview (April 9) — control plane for discovery and governance across clouds.
- Salesforce Headless 360 at TDX (April 16) — every Salesforce capability exposed as an MCP tool for agent builders.
- ThinkingAI + MiniMax Agentic Engine (April 16) — an infrastructure-level bet on open-source agent runtimes.
Each of these is a claim on the same strategic real estate: the layer between enterprise data, enterprise identity, and agent execution. Snowflake's advantage is that it already sits on the data side of that layer for most of the Fortune 500. Its disadvantage is that it does not own the identity or the productivity surface — which is where Microsoft Entra and Google Workspace are most entrenched.
The most likely end state is not winner-take-all. It is a two-vendor combination: a data-side control plane (Snowflake or Databricks) paired with an identity-and-productivity control plane (Microsoft or Google). The enterprise that picks those two coherently will be in a better position in 2027 than the one that tries to buy a single bundled stack from AWS or builds ad-hoc on Anthropic's MCP without a governance anchor.
The Risk Snowflake Still Carries
The obvious pushback on the April 21 announcement is that "control plane" is easy to announce and hard to prove. Cortex Code adoption is real; Intelligence adoption on complex, production-grade agentic workflows is less proven. MCP is an open protocol, which is a feature, but it is also only as useful as the breadth of agents that implement it cleanly. And the company has not yet published customer stories that demonstrate an end-to-end agentic production workflow using Intelligence plus external MCP connectors at enterprise scale.
Snowflake will need to ship those case studies before Summit in June for the control-plane claim to stick. If it does, the category pricing power shifts toward Snowflake. If it does not, the announcement stays a roadmap update, and Databricks, Microsoft, and Google will narrow the window.
The 2027 Framing
Zoom out. The enterprise AI buying cycle that opened in 2023 with "ChatGPT for your enterprise" has narrowed by 2026 to a different question: "Which two platforms do I build my agentic stack on?"
The Snowflake April 21 update is the company's most explicit claim yet to be one of those two. Combined with the November 2025 Cortex Code launch and the Intelligence roadmap, the company now has a coherent control-plane story — something that was missing 12 months ago. For any enterprise that has already made Snowflake the data backbone, the cost of also making it the agentic backbone is lower than it has ever been.
That is the bet. It is a reasonable bet. Whether it pays off depends on execution between now and Summit in June — and on whether Databricks responds in kind at its Data + AI Summit the same month.
Either way, the question for CIOs this quarter is the one Snowflake has just forced onto the roadmap: what is your control plane, and can you name it in a sentence? If you can't, you do not yet have an agentic strategy. You have a sprawl.
Photo by Manuel Geissinger on Pexels
Photo by fauxels on Pexels