In the era of enterprise AI, raw data storage is no longer the main differentiator. While foundation models can analyze schema structures effortlessly, without explicit business definitions, even the most advanced LLMs will generate inaccurate conclusions with complete conviction. For AI agents, establishing context is everything.
Snowflake provides an exceptional engine for storing and querying petabyte-scale data. However, connecting an AI agent directly to raw warehouse tables assumes the model can automatically decipher your internal metrics, regional filters, and security rules. Closing this gap requires shifting from basic prompt engineering to context engineering, establishing a governed semantic layer that provides models with true organizational alignment before a single query executes.
This guide explores the architectural limits of relying solely on warehouse tables for AI, what an enterprise context layer brings to your stack, and how to deploy this pattern alongside Snowflake without duplicating data or disrupting existing workflows.
Key takeaways
- While Snowflake manages storage and compute reliably, it does not natively govern the organizational meaning behind raw columns, leaving AI models to guess business logic.
- Implementing a context layer establishes a single source of truth for metrics, ensuring automated agents and human analysts calculate KPIs using identical business logic.
- Building a context layer does not require migrating off Snowflake. Using data federation, you can connect external datastores while keeping Snowflake as your primary analytical hub.
- By establishing access policies at the context layer, security rules travel with your data across multi-cloud environments rather than being locked inside a single proprietary tool.
- Protocols like MCP provide AI agents with a secure, managed gateway to access organizational definitions without relying on shadow integrations.
What context engineering means for Snowflake teams
Context engineering is the work of connecting AI agents and copilots to your Snowflake data through a governed layer of shared business definitions, metadata, and policy enforcement, ensuring that every answer traces back to a source both people and machines can trust. For most enterprise data platform leaders, that layer is missing today.
Analysts typically know which table holds the real revenue number and which fiscal calendar finance uses, but an AI agent querying the same data warehouse doesn’t have that context. That difference matters because it makes the difference between success and failure of AI in production. When you connect a copilot straight to raw Snowflake tables, you’re betting the model can guess your business logic correctly every time. Context engineering removes that bet by giving agents the same governed definitions, metadata, and access rules your analysts already rely on.
Why context engineering matters now
As Snowflake teams put more agents into production, the limiting factor is changing. It’s no longer which model you choose but how well that model understands your business context, and that typically comes down to the question of whether it can access it. Seen this way, the problem of context engineering is really a data access problem.
The bottleneck has moved from models to context
Foundation models keep improving, but a better model doesn’t know your business definitions on its own. For this reason, context is the new AI bottleneck. Context, not model quality, is what determines whether an AI agent answers a business question correctly. Two teams can run the identical model on the identical Snowflake data warehouse and get different answers, because one gave the agent governed definitions and the other didn’t.
For Snowflake teams, that means the next advantage doesn’t come from a better model. It comes from richer, more governed context around the data you already have.
Where Snowflake alone falls short for AI agents
Snowflake stores and runs your data reliably, but it wasn’t built to hold your company’s shared business logic. A data warehouse can tell an agent what columns exist. It can’t tell the agent which of three “customer” tables is authoritative, what your company means by “active user,” or which rows a given requester is allowed to see.
Without a governed context layer, every agent ends up building its own private interpretation of the same Snowflake tables. One agent calculates churn one way, another calculates it a different way, and both sound equally sure. As models commoditize, shared business definitions and governed metadata, the parts Snowflake alone doesn’t provide, are what separate a right AI answer from a confidently wrong one.
What a context layer adds on top of Snowflake
An enterprise context layer doesn’t replace Snowflake. Instead, it sits alongside your data warehouse, your analytics engine, and your agentic tools, and it applies common definitions and policy across every data domain you have.
Business definitions and metadata
Every enterprise has a handful of terms that mean more than one thing depending on who you ask. A context layer stores a single, governed definition for each term and its supporting metadata, so an analyst in finance and an AI agent answering a board question pull from the same source.
Additionally, open standards are starting to make these definitions portable across vendors. In November 2025, Starburst joined the Open Semantic Interchange, an open-source initiative led by Snowflake and industry partners to standardize semantic metadata across vendors. That means a business definition you write once doesn’t lock you into a single tool.
Governance and policy enforcement
A context layer also enforces who can see what, at the row and column level, before a query ever reaches an agent or an analyst. Instead of every application rebuilding its own permission checks against Snowflake, the context layer applies one set of rules everywhere it’s used. That consistency matters more as agents start acting on behalf of many users, each with different access, at once.
Data products as the delivery mechanism
Governed definitions need a way to reach the people and agents who use them. In this model, data products operate as the delivery mechanism for this. Instead of every agent querying raw Snowflake tables directly, teams package curated views, metrics, and definitions into data products that carry their governance rules wherever they’re used.
How context engineering works with Snowflake
None of this needs you to move off Snowflake. Instead, you should use Starburst to augment your access to your organization’s entire context and business logic, and apply that access to your Snowflake data.
Here’s what that looks like in practice.
Federating Snowflake with other systems without copying data
With federation, you can connect the other systems you already run, such as data lakes on Amazon S3 or tools built for other cloud platforms, while Snowflake stays in place as your data warehouse of record. Starburst can also query Snowflake-managed Iceberg tables directly through the Snowflake Catalog Metastore, so teams get federated access across a data warehouse and other systems while limiting the need to copy data between them.
That approach shows up in results. BestSecret, a European retailer, cut Snowflake-related costs by 70 percent after it began to scale beyond Snowflake with Starburst, by federating workloads across systems instead of running everything inside the data warehouse alone. Separately, GigaOm’s total cost of ownership research studied a similar Snowflake alternative approach. It found that a Starburst data lakehouse setup delivered about 3 times faster time to insight, with a lower total cost of ownership, than a Snowflake-only setup over a 3-year period.
Serving governed context to agents over MCP
Once governed definitions and data products are in place, you need a safe way to serve them to agents. The Model Context Protocol (MCP) is emerging as a common way to do that. Starburst Galaxy runs a hosted Model Context Protocol (MCP) server, so agents get governed, observable access to your data instead of connecting through unmanaged local servers no one on your team can see or check.
Steps to start context engineering on your Snowflake data
To get started, you don’t need to solve every part of this at once. A few sequential steps help you get moving.
- List the definitions your agents will need first, such as your fiscal calendar, your churn definition, and which table is the source of truth for each domain. Skipping this step leaves agents guessing at exactly these questions.
- Pick one pilot use case, put a governed context layer in front of it, and connect that layer to your Snowflake data warehouse and to any other systems the use case touches.
- Enforce policy at the context layer instead of inside each application, so analysts and agents inherit the same access rules.
- Package the pilot’s definitions and data as a data product, so the next team can reuse it instead of rebuilding it from scratch.
- Add a governed protocol for agent access, such as MCP, before you let agents query production data directly.
Start small, prove the pattern on one use case, then extend the same governed context layer to the next team asking Snowflake questions with an AI agent.
Want to know more about optimizing your cost and performance on Snowflake? Download the Data Engineer’s Guide to Reducing Your Cloud Data Warehouse Spending.
FAQs
What is context engineering, and how is it different from prompt engineering?
Prompt engineering shapes how you word a single question to a model. Context engineering gives every agent and copilot governed business definitions, metadata, and policy enforcement, so it works from the same source of truth an analyst already trusts. Instead of crafting a clever prompt for each request, you build a context layer that any agent can draw on consistently.
Why isn’t Snowflake data enough for an AI agent to answer correctly?
Snowflake tells an agent what tables and columns exist, but it doesn’t tell the agent which of three “customer” tables is authoritative, what your company means by “active user,” or which rows a given requester can see. Without governed context on top of that data, each agent builds its own private interpretation of the same tables, and different agents can produce different, equally confident answers.
Do you need to move Snowflake data to build a context layer?
No. Context engineering works through federation, so you connect other systems you already run, such as data lakes on Amazon S3, while Snowflake stays in place as your data warehouse of record. Starburst can also query Snowflake-managed Iceberg tables directly, an approach covered in Snowflake Iceberg catalog support, so you get federated access without copying data between systems.
What’s the difference between a semantic layer and a context layer?
A semantic layer usually covers business definitions and metrics for one analytics tool or team. A context layer extends that idea across every data domain you have, adding governance and policy enforcement on top of the definitions, so the same rules apply whether an analyst or an AI agent is asking the question.
What governance should a context layer enforce before agents get access?
At minimum, a context layer should enforce row and column level access rules before a query reaches an agent or an analyst, so every application isn’t rebuilding its own permission checks against Snowflake. Package governed definitions and access rules into data products, an approach covered in context layer, so the rules travel with the data wherever it’s used.
How does the Model Context Protocol (MCP) relate to context engineering?
MCP gives agents a governed, observable way to reach the definitions and data products your context layer already provides, instead of connecting through unmanaged local servers no one on your team can see or check. Starburst Galaxy runs a hosted Model Context Protocol (MCP) server for this purpose, so agents get access to Snowflake data under the same governance rules your analysts use.
