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It is well known that Anthropic is a trailblazer in the AI space and is arguably the leader of the proverbial pack. Anthropic's models are ubiquitous in the agentic space, powering a wide range of AI applications to support innovation in almost all industries. The three most notable components of the Claude ecosystem are:
- Claude Code: The ideal AI companion to a developer, a software engineering agent or “copilot” that executes coding tasks by interacting with source code, development tools, and the local environment.
- Claude Cowork: An agentic workflow orchestrator that decomposes and coordinates tasks across multiple tools, models, and workflows.
- Claude models: Spanning the many releases of Haiku, Sonnet, Opus, Fable and Mythos, these models consistently score in the top ranks of LLM benchmarks and power the intelligence behind agents and AI processes in companies and industries across the world.
As you can see, these components all provide functionality that is intended to drive greater efficiency by accelerating everyday tasks with agents.
Meanwhile, Snowflake has invested heavily over the last few years in growing their own platform's capabilities in the AI space; becoming the AI Data Cloud that we see today. Along a similar vein, the two most notable components of Snowflake's AI ecosystem are:
- Snowflake CoCo: An AI-powered coding copilot for data professionals that executes data engineering, analytics, machine learning, and AI development tasks by interacting with source code, development tools, and the local environment; specialized for Snowflake with a unique understanding of Snowflake’s environment, code, and development tools.
- Snowflake CoWork: A dedicated interface for agentic workflow orchestration natively deployed within Snowflake to provide a personal agentic work experience, understanding natural language questions and prompts to orchestrate tasks and context-aware analysis across agents; combined with the ability to engage in deep research, extended thinking, and interactions with external tools.
But what about models? Snowflake is investing in Cortex AI as a governed AI platform that hosts and orchestrates best-in-class foundation models, including Anthropic Claude, alongside its own AI infrastructure and open models.
Most notably, Anthropic has become a major strategic partner of Snowflake, with Claude models now deeply integrated into Snowflake Cortex AI; Snowflake CoWork and Snowflake CoCo leverage Claude models by default.
As a result, the pair have a deep partnership, with Claude LLMs proving to be some of the most popular models for Snowflake users, particularly when using CoCo.
The Gap Between AI Demos and AI Systems
It usually starts the same way. A team builds a prototype. Someone asks a question about your data, and an LLM gives you an answer. It feels like magic until it doesn't. The model misinterprets a table. It generates SQL that almost works. It misses key business logic that lives outside the raw data. And suddenly, confidence disappears. We see this pattern repeatedly.
LLMs are capable of generating answers, but they typically don't have access to production-grade quality data, metadata, and context. That gap becomes especially obvious in complex enterprise environments where metadata, relationships, and business logic matter just as much as the raw tables themselves.
Separate Responsibilities
In general, we tend to define clearly separated responsibilities. As mentioned, Claude is more appropriate for general process automation due to its extensive integrations with third-party tools such as AI Notetakers, Gmail, Docusign and many more. Claude thrives within customer-facing, front office style functions that require data across multiple tools for real-time decision-making.
Snowflake’s AI capabilities, however, are more suited for tasks that require structured and governed data access, protected by a series of customisable guardrails.
Snowflake as Claude’s Context Layer
Claude's models provide the underlying reasoning, but they are not context-aware on their own. LLMs are powered by complex algorithms that enable them to identify patterns, learn from them, and apply them. They are probability engines that run the risk of hallucinating when they have insufficient information, and their logic is entirely different to that of a human.
Ensuring accuracy is about providing sufficient context. That’s where Snowflake comes in. Snowflake gives agents the context that they need to accurately perform their operations. In other words, Snowflake is the harness.
A Typical Architecture: Cortex Agents, Snowflake, and Claude
In the below diagram, you can see a generalised example architecture that demonstrates how Snowflake and Claude work together. Claude is fed a question or task through some kind of interface, such as a Slack bot or an AI agent embedded within a CRP system, and routes these to a pre-defined Cortex Agent which is built on domain-specific data and responds back to Claude, which provides a cohesive answer.
More likely than not, Claude's models will be providing underlining intelligence beneath those Cortex Agents, too.
A Deeper Dive on the Wide Range of Functionality Offered by Cortex Agents
Within the architecture above, Cortex Agents act as the bridge between in-bound prompts from Claude’s agents and the data within Snowflake.
- The Cortex Agents form the foundation of how Snowflake can accept a natural language prompt and respond with relevant, data-driven information.
- Cortex Agents leverage agent tools like Cortex Analyst, which generates SQL over structured data using semantic models and/or semantic views, and Cortex Search, which takes a natural language query and retrieves chunks from unstructured data (e.g. PDFs) that are relevant to said query, and then reason over the results.
- Developers can provide additional context to the underlying configuration of the Cortex Agent, including sample questions to help users get started on their journey for insights.
- The functionality of a Cortex Agent can be extended in a variety of ways, including:
- Packaged agent skills that give an agent repeatable, task-specific capabilities though modular bundles of instructions and scripts
- MCP connectors to interact with Model Context Protocol servers outside of Snowflake to invoke tools and interact with other applications
- Access to shared Knowledge Extensions that can be used in a retrieval-augmented generation (RAG) architecture to integrate licensed and proprietary content.
Workflow Example
Let’s take an example from a previous blog; automating accounts payable processing with AI.
This is how that combination of Claude and Snowflake could appear:
In the above workflow, Claude:
- Sees an attachment via some integration with Outlook or Gmail
- Decides whether it’s an invoice
- Queries Snowflake, via CoCo, to check whether it’s been processed before
- Uses AI_PARSE_DOCUMENT() to extract the invoice text and stores the result in a table
- Queries a Cortex Search service to understand how similar invoices have been processed, historically
- Takes that context and uses it to process the invoice and post it to the ledger system
Further Examples to Demonstrate Potential
Aimpoint’s ambition for our Claude services customers is to enable them and empower business users to self-serve in a safe and governed way. That means a strong Snowflake context layer with ready-made touchpoints Claude can use.
Imagine an environment where data is curated and governed neatly within Snowflake (with Cortex ready to go) and users can choose what data suits their needs; without necessarily needing to interact with the Snowflake user interface directly.
Here are some further examples that demonstrate the power unlocked through the combination of Snowflake and Claude:
- Generate a PowerPoint board deck with the latest KPIs, financials, and commentary by combining Claude's presentation capabilities with a Cortex agent that understands the underlying data.
- Write a quarterly business review (QBR) in Microsoft Word or Google Docs, with a Cortex Agent supplying sales performance, customer health, and operational metrics.
- Enrich Salesforce opportunities by having Claude leverage CoCo to identify enrichment data points per customer, that can be used to recommend next best actions, identify cross-sell opportunities, or simply draft notes directly into the CRM.
- Leverage a Claude-driven Slack bot to quickly ask business questions and receive accurate answers using governed metrics, provided by a Cortex Agent.
- Automatically generate Confluence pages by leveraging Claude to produce documentation by asking CoCo to analyze dbt projects, Airflow repos, etc.
The Bottom Line
The combination of Snowflake and Claude supports the production of trusted, enterprise-grade AI systems where data is structured and governed, context is treated as a first-class object, models can reason, not just generate, and outputs are integrated into real workflows.
That’s the difference between a demo and something you can actually deploy.
Our Expertise
Aimpoint Digital is both an Anthropic Select Tier services partner and a Snowflake Elite Tier services partner. We have the capability and passion to support your endeavors to implement AI successfully at scale. We also love a challenge and thrive on tackling complex scenarios.
If you’d like to explore how to accelerate your company’s Anthropic journey, or how to ensure your Snowflake context layer fits the gold standard, get in touch!
