The era of AI in CRM is here, and its name is Salesforce Copilot. It’s more than just a chatbot that answers questions; in fact,it’s an intelligent assistant designed to take action. But its true power is unlocked when you teach it to perform custom tasks specific to your business.
Ultimately, this guide will walk you through the entire process of building your very first custom Salesforce Copilot action. We’ll create a practical tool that allows a user to summarize a complex support Case and post that summary to Chatter with a single command.
Understanding the Core Concepts of Salesforce Copilot
First, What is a Copilot Action?
A Copilot Action is, in essence, a custom skill you give to your Salesforce Copilot. It connects a user’s natural language request to a specific automation built on the Salesforce platform, usually using a Salesforce Flow.
To see how this works, think of the following sequence:
1. To begin, a user gives a command like, “Summarize this case for me and share an update.”
2. Salesforce Copilot then immediately recognizes the user’s intent.
3. This recognition subsequently triggers the specific Copilot Action you built.
4. Finally, the Flow connected to that action runs all the necessary logic, such as calling Apex, getting the summary, and posting the result to Chatter.
Our Project Goal: The Automated Case Summary Action
Our goal is to build a Salesforce Copilot action that can be triggered from a Case record page. To achieve this, our action will perform three key steps:
1. It will read the details of the current Case.
2. Next, the action will use AI to generate a concise summary.
3. Lastly, it will post that summary to the Case’s Chatter feed for team visibility.
Although you can do a lot in Flow, complex logic is often best handled in Apex. Therefore, we’ll start by creating a simple Apex method that takes a Case ID and returns its Subject and Description, which the Flow can then call.
The CaseSummarizer Apex Class
// Apex Class: CaseSummarizer
public with sharing class CaseSummarizer {
// Invocable Method allows this to be called from a Flow
@InvocableMethod(label='Get Case Details for Summary' description='Returns the subject and description of a given Case ID.')
public static List<CaseDetails> getCaseDetails(List<Id> caseIds) {
Id caseId = caseIds[0]; // We only expect one ID
Case thisCase = [SELECT Subject, Description FROM Case WHERE Id = :caseId LIMIT 1];
// Prepare the output for the Flow
CaseDetails details = new CaseDetails();
details.caseSubject = thisCase.Subject;
details.caseDescription = thisCase.Description;
return new List<CaseDetails>{ details };
}
// A wrapper class to hold the output variables for the Flow
public class CaseDetails {
@InvocableVariable(label='Case Subject' description='The subject of the case')
public String caseSubject;
@InvocableVariable(label='Case Description' description='The description of the case')
public String caseDescription;
}
}
After creating the Apex logic, we’ll build an Autolaunched Flow that orchestrates the entire process from start to finish.
Flow Configuration
Go to Setup > Flows and create a new Autolaunched Flow.
For this purpose, define an input variable: recordId (Text, Available for Input). This, in turn, will receive the Case ID.
Add an Action element: Call the getCaseDetails Apex method we just created, passing the recordId as the caseIds input.
Store the outputs: Store the caseSubject and caseDescription in new variables within the Flow.
Add a “Post to Chatter” Action:
Message: This is where we bring in AI. We’ll use a Prompt Template here soon, but for now, you can put placeholder text like {!caseSubject}.
Target Name or ID: Set this to {!recordId} to post on the current Case record.
Save and activate the Flow (e.g., as “Post Case Summary to Chatter”).
Step 3: Teaching the AI with a Prompt Template
Furthermore, this step tells the LLM how to generate the summary.
Prompt Builder Setup
Go to Setup > Prompt Builder.
Create a new Prompt Template.
For the prompt, write instructions for the AI. Specifically, use merge fields to bring in your Flow variables.
You are a helpful support team assistant.
Based on the following Case details, write a concise, bulleted summary to be shared with the internal team on Chatter.
Case Subject: {!caseSubject}
Case Description: {!caseDescription}
Summary:
4. Save the prompt (e.g., “Case Summary Prompt”).
Step 4: Connecting Everything with a Copilot Action
Now, this is the crucial step where we tie everything together.
Action Creation
Go to Setup > Copilot Actions.
Click New Action.
Select Salesforce Flow as the action type and choose the Flow you created (“Post Case Summary to Chatter”).
Instead of using a plain text value for the “Message” in your Post to Chatter action, select your “Case Summary Prompt” template.
Follow the prompts to define the language and behavior. For instance, for the prompt, you can use something like: “Summarize the current case and post it to Chatter.”
Activate the Action.
Step 5: Putting Your Copilot Action to the Test
Finally, navigate to any Case record. Open the Salesforce Copilot panel and type your command: “Summarize this case for me.”
Once you issue the command, the magic happens. Specifically, the Copilot will understand your intent, trigger the action, run the Flow, call the Apex, generate the summary using the Prompt Template, and post the final result directly to the Chatter feed on that Case.
Conclusion: The Future of CRM is Action-Oriented
In conclusion, you have successfully built a custom skill for your Salesforce Copilot. This represents a monumental shift from passive data entry to proactive, AI-driven automation. Indeed, by combining the power of Flow, Apex, and the Prompt Builder, you can create sophisticated agents that understand your business and work alongside your team to drive incredible efficiency.
The age of AI chatbots is evolving into the era of AI doers. Instead of just answering questions, modern AI can now execute tasks, interact with systems, and solve multi-step problems. At the forefront of this revolution on the Databricks platform is the Mosaic AI Agent Framework.
This guide will walk you through building your first Databricks AI Agent—a powerful assistant that can understand natural language, inspect your data, and execute Spark SQL queries for you, all powered by the latest GPT-5 model.
What is a Databricks AI Agent?
A Databricks AI Agent is an autonomous system you create using the Mosaic AI Agent Framework. It leverages a powerful Large Language Model (LLM) as its “brain” to reason and make decisions. You equip this brain with a set of “tools” (custom Python functions) that allow it to interact with the Databricks environment.
The agent works in a loop:
Reason: Based on your goal, the LLM decides which tool is needed.
Act: The agent executes the chosen Python function.
Observe: It analyzes the result of that function.
Repeat: It continues this process until it has achieved the final objective.
Our Project: The “Data Analyst” Agent
We will build an agent whose goal is to answer data questions from a non-technical user. To do this, it will need two primary tools:
A tool to get the schema of a table (get_table_schema).
A tool to execute a Spark SQL query and return the result (run_spark_sql).
Let’s start building in a Databricks Notebook.
Step 1: Setting Up Your Tools (Python Functions)
An agent’s capabilities are defined by its tools. In Databricks, these are simply Python functions. Let’s define the two functions our agent needs to do its job.
# Tool #1: A function to get the DDL schema of a table
def get_table_schema(table_name: str) -> str:
"""
Returns the DDL schema for a given Spark table name.
This helps the agent understand the table structure before writing a query.
"""
try:
ddl_result = spark.sql(f"SHOW CREATE TABLE {table_name}").first()[0]
return ddl_result
except Exception as e:
return f"Error: Could not retrieve schema for table {table_name}. Reason: {e}"
# Tool #2: A function to execute a Spark SQL query and return the result as a string
def run_spark_sql(query: str) -> str:
"""
Executes a Spark SQL query and returns the result.
This is the agent's primary tool for interacting with data.
"""
try:
result_df = spark.sql(query)
# Convert the result to a string format for the LLM to understand
return result_df.toPandas().to_string()
except Exception as e:
return f"Error: Failed to execute query. Reason: {e}"
Step 2: Assembling Your Databricks AI Agent
With our tools defined, we can now use the Mosaic AI Agent Framework to create our agent. This involves importing the Agent class, providing our tools, and selecting an LLM from Model Serving.
For this example, we’ll use the newly available openai/gpt-5model endpoint.
from databricks_agents import Agent
# Define the instructions for the agent's "brain"
# This prompt guides the agent on how to behave and use its tools
agent_instructions = """
You are a world-class data analyst. Your goal is to answer user questions by querying data in Spark.
Here is your plan:
1. First, you MUST use the `get_table_schema` tool to understand the columns of the table the user mentions. Do not guess column names.
2. After you have the schema, formulate a Spark SQL query to answer the user's question.
3. Execute the query using the `run_spark_sql` tool.
4. Finally, analyze the result from the query and provide a clear, natural language answer to the user. Do not just return the raw data table. Summarize the findings.
"""
# Create the agent instance
data_analyst_agent = Agent(
model="endpoints:/openai-gpt-5", # Using a Databricks Model Serving endpoint for GPT-5
tools=[get_table_schema, run_spark_sql],
instructions=agent_instructions
)
Step 3: Interacting with Your Agent
Your Databricks AI Agent is now ready. You can interact with it using the .run() method, providing your question as the input.
Let’s use the common samples.nyctaxi.trips table.
# Let's ask our new agent a question
user_question = "What were the average trip distances for trips paid with cash vs. credit card? Use the samples.nyctaxi.trips table."
# Run the agent and get the final answer
final_answer = data_analyst_agent.run(user_question)
print(final_answer)
What Happens Behind the Scenes:
Reason: The agent reads your prompt. It knows it needs to find average trip distances from the samples.nyctaxi.trips table but first needs the schema. It decides to use the get_table_schema tool.
Act: It calls get_table_schema('samples.nyctaxi.trips').
Observe: It receives the table schema and sees columns like trip_distance and payment_type.
Reason: Now it has the schema. It formulates a Spark SQL query like SELECT payment_type, AVG(trip_distance) FROM samples.nyctaxi.trips GROUP BY payment_type. It decides to use the run_spark_sql tool.
Act: It calls run_spark_sql(...) with the generated query.
Observe: It receives the query result as a string (e.g., a small table showing payment types and average distances).
Reason: It has the data. Its final instruction is to summarize the findings.
Final Answer: It generates and returns a human-readable response like: “Based on the data, the average trip distance for trips paid with a credit card was 2.95 miles, while cash-paid trips had an average distance of 2.78 miles.”
Conclusion: Your Gateway to Autonomous Data Tasks
Congratulations! You’ve just built a functional Databricks AI Agent. This simple text-to-SQL prototype is just the beginning. By creating more sophisticated tools, you can build agents that perform data quality checks, manage ETL pipelines, or even automate MLOps workflows, all through natural language commands on the Databricks platform.
The world of data is buzzing with the promise of Large Language Models (LLMs), but how do you move them from simple chat interfaces to intelligent actors that can do things? The answer is agents. This guide will show you how to build your very first Snowflake Agent in minutes, creating a powerful assistant that can understand your data and write its own SQL.
A Snowflake Agent is an advanced AI entity, powered by Snowflake Cortex, that you can instruct to complete complex tasks. Unlike a simple LLM call that just provides a text response, an agent can use a set of pre-defined “tools” to interact with its environment, observe the results, and decide on the next best action to achieve its goal.
Reason: The LLM thinks about the goal and decides which tool to use.
Act: It executes the chosen tool (like a SQL function).
Observe: It analyzes the output from the tool.
Repeat: It continues this loop until the final goal is accomplished.
Our Project: The “Text-to-SQL” Agent
We will build a Snowflake Agent with a clear goal: “Given a user’s question in plain English, write a valid SQL query against the correct table.”
To do this, our agent will need two tools:
A tool to look up the schema of a table.
A tool to draft a SQL query based on that schema.
Let’s get started!
Step 1: Create the Tools (SQL Functions)
An agent is only as good as its tools. In Snowflake, these tools are simply User-Defined Functions (UDFs). We’ll create two SQL functions that our agent can call.
First, a function to get the schema of any table. This allows the agent to understand the available columns.
-- Tool #1: A function to describe a table's schema
CREATE OR REPLACE FUNCTION get_table_schema(table_name VARCHAR)
RETURNS VARCHAR
LANGUAGE SQL
AS
$$
SELECT GET_DDL('TABLE', table_name);
$$;
Second, we’ll create a function that uses SNOWFLAKE.CORTEX.COMPLETE to draft a SQL query. This function will take the user’s question and the table schema as context.
-- Tool #2: A function to write a SQL query based on a schema and a question
CREATE OR REPLACE FUNCTION write_sql_query(schema VARCHAR, question VARCHAR)
RETURNS VARCHAR
LANGUAGE SQL
AS
$$
SELECT SNOWFLAKE.CORTEX.COMPLETE(
'llama3-8b', -- Using a fast and efficient model
CONCAT(
'You are a SQL expert. Based on the following table schema and user question, write a single, valid SQL query. Do not add any explanation, just the code.\n\n',
'Schema:\n', schema, '\n\n',
'User Question:\n', question
)
)
$$;
With our tools ready, we can now assemble the agent itself.
Step 2: Create Your First Snowflake Agent
Creating the agent involves a single, declarative SQL command. Here, we define the agent, grant it access to our functions (tools), and give it its core objective using a prompt.
-- Now, we create the agent and give it the tools
CREATE OR REPLACE AGENT my_first_sql_agent
MODEL = 'llama3-70b' -- Use a powerful model for reasoning
FUNCTIONS = (get_table_schema, write_sql_query)
PROMPT =
$$
You are an expert SQL-writing agent. Your goal is to help users by writing a single SQL query that answers their question.
Here is your plan:
1. First, you MUST use the get_table_schema tool to find the columns of the table the user mentions.
2. Once you have the schema, you MUST use the write_sql_query tool to generate the final SQL.
3. Finally, output the generated SQL query as your final answer. Do not add any extra text or explanation.
$$;
And that’s it. Your Snowflake Agent is now live and ready for instructions.
Step 3: Chat with Your Agent
To interact with your agent, you use the CALL command. The agent will show its thought process as it uses the tools you provided.
Let’s assume you have the SNOWFLAKE_SAMPLE_DATA.TPCH_SF1.CUSTOMER table.
-- Let's test our new agent!
CALL my_first_sql_agent('Show me the names of all customers in the USA, table name is SNOWFLAKE_SAMPLE_DATA.TPCH_SF1.CUSTOMER');
Expected Agent Thought Process (the output you’ll see):
Thinking: The user wants customer names from the USA and has given me a table name. My first step is to get the schema of that table. I will call get_table_schema.
Observing Output: (The agent now sees the CREATE TABLE statement with columns like C_NAME, C_NATIONKEY, etc., and knows there’s a related NATION table for country names).
Thinking: Okay, I have the schema. The user wants names (C_NAME) where the nation is USA. I have all the information I need to write the query. I will now call write_sql_query.
Calling Tool:write_sql_query('<schema_output>', 'Show me the names of all customers in the USA')
Observing Output:SELECT c.c_name FROM customer c JOIN nation n ON c.c_nationkey = n.n_nationkey WHERE n.n_name = 'USA';
Final Answer: The agent outputs the generated SQL.
Conclusion: From Minutes to Mastery
You’ve just built a functional Snowflake Agent that can reason and act within your data cloud. This simple prototype is just the beginning. Imagine agents that can manage data quality, perform complex transformations, or even administer security, all through natural language commands. Welcome to the future of data interaction.
Breaking: Tech Giants Unite to Solve AI’s Biggest Bottleneck
The Open Semantic Interchange was announced by Snowflake in their official blog On September 23, 2025, something unprecedented happened in the data industry. Open Semantic Interchange (OSI), a groundbreaking initiative led by Snowflake, Salesforce, BlackRock, and dbt Labs, was announced to solve AI’s biggest problem. These 15+ technology companies would give away their data secrets—collaboratively creating the Open Semantic Interchange as an open, vendor-neutral standard for how business data is defined across all platforms.
This isn’t just another tech announcement. It’s the industry admitting that the emperor has no clothes.
For decades, every software vendor has defined business metrics differently. Your data warehouse calls it “revenue.” Your BI tool calls it “total sales.” Your CRM calls it “booking amount.” Your AI model? It has no idea they’re the same thing.
This semantic chaos has created what VentureBeat calls “the $1 trillion AI problem“—the massive hidden cost of data preparation, reconciliation, and the manual labor required before any AI project can begin.
Enter the Open Semantic Interchang (OSI)—a groundbreaking initiative that could become as fundamental to AI as SQL was to databases or HTTP was to the web.
What is Open Semantic Interchange (OSI)? Understanding the Semantic Standard
Open Semantic Interchange is an open-source initiative that creates a universal, vendor-neutral specification for defining and sharing semantic metadata across data platforms, BI tools, and AI applications.
The Simple Explanation of Open Semantic Interchange
Think of OSI as a Rosetta Stone for business data. Just as the ancient Rosetta Stone allowed scholars to translate between Egyptian hieroglyphics, Greek, and Demotic script, OSI allows different software systems to understand each other’s data definitions.
When your data warehouse, BI dashboard, and AI model all speak the same semantic language, magic happens:
No more weeks reconciling conflicting definitions
No more “which revenue number is correct?”
No more AI models trained on misunderstood data
No more rebuilding logic across every tool
Open Semantic Interchange Technical Definition
OSI provides a standardized specification for semantic models that includes:
Business Metrics: Calculations, aggregations, and KPIs (revenue, customer lifetime value, churn rate)
Dimensions: Attributes for slicing data (time, geography, product category)
Hierarchies: Relationships between data elements (country → state → city)
Business Rules: Logic and constraints governing data interpretation
Context & Metadata: Descriptions, ownership, lineage, and governance policies
Built on familiar formats like YAML and compatible with RDF and OWL, this specification stands out by being tailored specifically for modern analytics and AI workloads.
The $1 Trillion Problem: Why Open Semantic Interchange Matters Now
The Hidden Tax: Why Semantic Interchange is Critical for AI Projects
Every AI initiative begins the same way. Data scientists don’t start building models—they start reconciling data.
Week 1-2: “Wait, why are there three different revenue numbers?”
Week 3-4: “Which customer definition should we use?”
Week 5-6: “These date fields don’t match across systems.”
Week 7-8: “We need to rebuild this logic because BI and ML define margins differently.”
According to industry research, data preparation consumes 60-80% of data science time. For enterprises spending millions on AI, this represents a staggering hidden cost.
Real-World Horror Stories Without Semantic Interchange
Fortune 500 Retailer: Spent 9 months building a customer lifetime value model. When deployment came, marketing and finance disagreed on the “customer” definition. Project scrapped.
Global Bank: Built fraud detection across 12 regions. Each region’s “transaction” definition differed. Model accuracy varied 35% between regions due to semantic inconsistency.
Healthcare System: Created patient risk models using EHR data. Clinical teams rejected the model because “readmission” calculations didn’t match their operational definitions.
These aren’t edge cases—they’re the norm. The lack of semantic standards is silently killing AI ROI across every industry.
Why Open Semantic Interchange Now? The AI Inflection Point
Generative AI has accelerated the crisis. When you ask ChatGPT or Claude to “analyze Q3 revenue by region,” the AI needs to understand:
What “revenue” means in your business
How “regions” are defined
Which “Q3” you’re referring to
What calculations to apply
Without semantic standards, AI agents give inconsistent, untrustworthy answers. As enterprises move from AI pilots to production at scale, semantic fragmentation has become the primary blocker to AI adoption.
The Founding Coalition: Who’s Behind OSI
OSI isn’t a single-vendor initiative—rather it’s an unprecedented collaboration across the data ecosystem.
Companies Leading the Open Semantic Interchange Initiative
Snowflake: The AI Data Cloud company spearheading the initiative, contributing engineering resources and governance infrastructure
Salesforce (Tableau): Co-leading with Snowflake, bringing BI perspective and Tableau’s semantic layer expertise
dbt Labs:Furthermore,contributing the dbt Semantic Layer framework as a foundational technology
BlackRock:Moreover, representing financial services with the Aladdin platform, ensuring real-world enterprise requirements
RelationalAI:Finally, bringing knowledge graph and reasoning capabilities for complex semantic relationships
This coalition represents competitors agreeing to open-source their competitive advantage for the greater good of the industry.
Why Competitors Are Collaborating on Semantic Interchange
As Christian Kleinerman, EVP Product at Snowflake, explains: “The biggest barrier our customers face when it comes to ROI from AI isn’t a competitor—it’s data fragmentation.”
Indeed, this observation highlights a critical industry truth. Rather than competing against other vendors, organizations are actually fighting against their own internal data inconsistencies. Moreover, this fragmentation costs enterprises millions annually in lost productivity and delayed AI initiatives.
Similarly, Southard Jones, CPO at Tableau, emphasizes the collaborative nature: “This initiative is transformative because it’s not about one company owning the standard—it’s about the industry coming together.”
In other words, the traditional competitive dynamics are being reimagined. Instead of proprietary lock-in strategies, therefore, the industry is choosing open collaboration. Consequently, this shift benefits everyone—vendors, enterprises, and end users alike.
Ryan Segar, CPO at dbt Labs: “Data and analytics engineers will now be able to work with the confidence that their work will be leverageable across the data ecosystem.”
The message is clear: Standardization isn’t a commoditizer—it’s a catalyst. Like USB-C didn’t hurt device makers, OSI won’t hurt data platforms. It shifts competition from data definitions to innovation in user experience and AI capabilities.
How Open Semantic Interchange (OSI) Works: Technical Deep Dive
The Open Semantic Interchange Specification Structure
OSI defines semantic models in a structured, machine-readable format. Here’s what a simplified OSI specification looks like:
Metrics Definition:
Name, description, and business owner
Calculation formula with explicit dependencies
Aggregation rules (sum, average, count distinct)
Filters and conditions
Temporal considerations (point-in-time vs. accumulated)
Compilation: Engines that translate OSI specs into platform-specific code (SQL, Python, APIs)
Transport: REST APIs and file-based exchange
Validation: Schema validation and semantic correctness checking
Extension: Plugin architecture for domain-specific semantics
Integration Patterns
Organizations can adopt OSI through multiple approaches:
Native Integration: Platforms like Snowflake directly support OSI specifications
Translation Layer: Tools convert between proprietary formats and OSI
Dual-Write: Systems maintain both proprietary and OSI formats
Federation: Central OSI registry with distributed consumption
Real-World Use Cases: Open Semantic Interchange in Action
Use Case 1: Open Semantic Interchange for Multi-Cloud Analytics
Challenge: A global retailer runs analytics on Snowflake but visualizations in Tableau, with data science in Databricks. Each platform defined “sales” differently.
Before OSI:
Data team spent 40 hours/month reconciling definitions
Business users saw conflicting dashboards
ML models trained on inconsistent logic
Trust in analytics eroded
With OSI:
Single OSI specification defines “sales” once
All platforms consume the same semantic model
Dashboards, notebooks, and AI agents align
Data team focuses on new insights, not reconciliation
Impact: 90% reduction in semantic reconciliation time, 35% increase in analytics trust scores
Use Case 2: Semantic Interchange for M&A Integration
Challenge: A financial services company acquired three competitors, each with distinct data definitions for “customer,” “account,” and “portfolio value.”
Before OSI:
18-month integration timeline
$12M spent on data mapping consultants
Incomplete semantic alignment at launch
Ongoing reconciliation needed
With OSI:
Each company publishes OSI specifications
Automated mapping identifies overlaps and conflicts
Human review focuses only on genuine business rule differences
Use Case 3: Open Semantic Interchange Improves AI Agent Trust
Challenge: An insurance company deployed AI agents for claims processing. Agents gave inconsistent answers because “claim amount,” “deductible,” and “coverage” had multiple definitions.
Before OSI:
Customer service agents stopped using AI tools
45% of AI answers flagged as incorrect
Manual verification required for all AI outputs
AI initiative considered a failure
With OSI:
All insurance concepts defined in OSI specification
AI agents query consistent semantic layer
Answers align with operational systems
Audit trails show which definitions were used
Impact: 92% accuracy rate, 70% reduction in manual verification, AI adoption rate increased to 85%
Use Case 4: Semantic Interchange for Regulatory Compliance
Challenge: A bank needed consistent risk reporting across Basel III, IFRS 9, and CECL requirements. Each framework defined “exposure,” “risk-weighted assets,” and “provisions” slightly differently.
Before OSI:
Separate data pipelines for each framework
Manual reconciliation of differences
Audit findings on inconsistent definitions
High cost of compliance
With OSI:
Regulatory definitions captured in domain-specific OSI extensions
Semantic models as reusable as open-source libraries
Cross-industry semantic model marketplace
AI agents natively understanding OSI specifications
Open Semantic Interchange Benefits for Different Stakeholders
Data Engineers
Before OSI:
Rebuild semantic logic for each new tool
Debug definition mismatches
Manual data reconciliation pipelines
With OSI:
Define business logic once
Automatic propagation to all tools
Focus on data quality, not definition mapping
Time Savings: 40-60% reduction in pipeline development time
Data Analysts
Before OSI:
Verify metric definitions before trusting reports
Recreate calculations in each BI tool
Reconcile conflicting dashboards
With OSI:
Trust that all tools use same definitions
Self-service analytics with confidence
Focus on insights, not validation
Productivity Gain: 3x increase in analysis output
Open Semantic Interchange Benefits for Data Scientists
Before OSI:
Spend weeks understanding data semantics
Build custom feature engineering for each project
Models fail in production due to definition drift
With OSI:
Leverage pre-defined semantic features
Reuse feature engineering logic
Production models aligned with business systems
Impact: 5-10x faster model development
How Semantic Interchange Empowers Business Users
Before OSI:
Receive conflicting reports from different teams
Unsure which numbers to trust
Can’t ask AI agents confidently
With OSI:
Consistent numbers across all reports
Trust AI-generated insights
Self-service analytics without IT
Trust Increase: 50-70% higher confidence in data-driven decisions
Open Semantic Interchange Value for IT Leadership
Before OSI:
Vendor lock-in through proprietary semantics
High cost of platform switching
Difficult to evaluate best-of-breed tools
With OSI:
Freedom to choose best tools for each use case
Lower switching costs and negotiating leverage
Faster time-to-value for new platforms
Strategic Flexibility: 60% reduction in platform lock-in risk
Challenges and Considerations
Challenge 1: Organizational Change for Semantic Interchange
Issue: OSI requires organizations to agree on single source of truth definitions—politically challenging when different departments define metrics differently.
Solution:
Start with uncontroversial definitions
Use OSI to make conflicts visible and force resolution
Establish data governance councils
Frame as risk reduction, not turf battle
Challenge 2: Integrating Legacy Systems with Semantic Interchange
Issue: Older systems may lack APIs or semantic metadata capabilities.
Solution:
Build translation layers
Gradually migrate legacy definitions to OSI
Focus on high-value use cases first
Use OSI for new systems, translate for old
Challenge 3: Specification Evolution
Issue: Business definitions change—how does OSI handle versioning and migration?
Solution:
Built-in versioning in OSI specification
Deprecation policies and timelines
Automated impact analysis tools
Backward compatibility guidelines
Challenge 4: Domain-Specific Complexity
Issue: Some industries have extremely complex semantic models (e.g., derivatives trading, clinical research).
Solution:
Domain-specific OSI extensions
Industry working groups
Pluggable architecture for specialized needs
Start simple, expand complexity gradually
Challenge 5: Governance and Ownership
Issue: Who owns the semantic definitions? Who can change them?
Solution:
Clear ownership model in OSI metadata
Approval workflows for definition changes
Audit trails and change logs
Role-based access control
How Open Semantic Interchange Shifts the Competitive Landscape
Vendors competed by locking in data semantics. Moving from Platform A to Platform B meant rebuilding all your business logic.
This created:
High switching costs
Vendor power imbalance
Slow innovation (vendors focused on lock-in, not features)
Customer resentment
After OSI: The Innovation Era
With semantic portability, vendors must compete on:
User experience and interface design
AI capabilities and intelligence
Performance and scalability
Integration breadth and ease
Support and services
Southard Jones (Tableau): “Standardization isn’t a commoditizer—it’s a catalyst. Think of it like a standard electrical outlet: the outlet itself isn’t the innovation, it’s what you plug into it.”
This shift benefits customers through:
Better products (vendors focus on innovation)
Lower costs (competition increases)
Flexibility (easy to switch or multi-source)
Faster AI adoption (semantic consistency enables trust)
How to Get Started with Open Semantic Interchange (OSI)
For Enterprises
Step 1: Assess Current State (1-2 weeks)
Inventory your data platforms and BI tools
Document how metrics are currently defined
Identify semantic conflicts and pain points
Estimate time spent on definition reconciliation
Step 2: Pilot Use Case (1-2 months)
Choose a high-impact but manageable scope (e.g., revenue metrics)
Define OSI specification for selected metrics
Implement in 2-3 key tools
Measure impact on reconciliation time and trust
Step 3: Expand Gradually (6-12 months)
Add more metrics and dimensions
Integrate additional platforms
Establish governance processes
Train teams on OSI practices
Step 4: Operationalize (Ongoing)
Make Open semantic interchange part of standard data modeling
Integrate into data governance framework
Participate in community to influence roadmap
Share learnings and semantic models
For Technology Vendors
Kickoff Phase: Evaluate Strategic Fit (Immediate)
Review Open semantic interchange specification
Assess compatibility with your platform
Identify required engineering work
Estimate go-to-market impact
Next : Join the Initiative (Q4 2025)
Become an Open semantic interchange partner
Participate in working groups
Contribute to specification development
Collaborate on reference implementations
Strenghthen the core: Implement Support (2026)
Add OSI import/export capabilities
Provide migration tools from proprietary formats
Update documentation and training
Certify OSI compliance
Finally: Differentiate (Ongoing)
Build value-added services on top of OSI
Focus innovation on user experience
Lead with interoperability messaging
Partner with ecosystem for joint solutions
The Future: What’s Next for Open Semantic Interchange
2025-2026: Specification & Early Adoption
Initial specification published (Q4 2025)
Reference implementations released
Major vendors announce support
First enterprise pilot programs
Community formation and governance
2027-2028: Mainstream Adoption
OSI becomes default for new projects
Translation tools for legacy systems mature
Domain-specific extensions proliferate
Marketplace for shared semantic models emerges
Analyst recognition as emerging standard
2029-2030: Industry Standard Status
International standards body adoption
Regulatory recognition in financial services
Built into enterprise procurement requirements
University curricula include Open semantic interchange
Semantic models as common as APIs
Long-Term Vision
The Semantic Web Realized: Open semantic interchange could finally deliver on the promise of the Semantic Web—not through abstract ontologies, but through practical, business-focused semantic standards.
AI Agent Economy: When AI agents understand semantics consistently, they can collaborate across organizational boundaries, creating a true agentic AI ecosystem.
Data Product Marketplace: Open semantic interchange enables data products with embedded semantics, making them immediately usable without integration work.
Cross-Industry Innovation: Semantic models from one industry (e.g., supply chain optimization) could be adapted to others (e.g., healthcare logistics) through shared Open semantic interchange definitions.
Conclusion: The Rosetta Stone Moment for AI
Conclusion: The Rosetta Stone Moment for AI
The launch of Open Semantic Interchange marks a watershed moment in the data industry. For the first time, fierce competitors have set aside proprietary advantages to solve a problem that affects everyone: semantic fragmentation.
However, this isn’t just about technical standards—rather, it’s about unlocking a trillion dollars in trapped AI value.
Specifically, when every platform speaks the same semantic language, AI can finally deliver on its promise:
First, trustworthy insights that business users believe
Second, fast time-to-value without months of data prep
Third, flexible tool choices without vendor lock-in
Finally, scalable AI adoption across the enterprise
Importantly, the biggest winners will be organizations that adopt early. While others struggle with semantic reconciliation, early adopters will be deploying AI agents, building sophisticated analytics, and making data-driven decisions with confidence.
Ultimately, the question isn’t whether Open Semantic Interchange will become the standard—instead, it’s how quickly you’ll adopt it to stay competitive.
The revolution has begun. Indeed, the Rosetta Stone for business data is here.
So, are you ready to speak the universal language of AI?
The world of data analytics is changing. For years, accessing insights required writing complex SQL queries. However, the industry is now shifting towards a more intuitive, conversational approach. At the forefront of this revolution is agentic AI—intelligent systems that can understand human language, reason, plan, and automate complex tasks.
Snowflake is leading this charge by transforming its platform into an intelligent and conversational AI Data Cloud. With the recent introduction of Snowflake Cortex Agents, they have provided a powerful tool for developers and data teams to build their own custom AI assistants.
This guide will walk you through, step-by-step, how to build your very first AI data agent. You will learn how to create an agent that can answer complex questions by pulling information from both your database tables and your unstructured documents, all using simple, natural language.
What is a Snowflake Cortex Agent and Why Does it Matter?
First and foremost, a Snowflake Cortex Agent is an AI-powered assistant that you can build on top of your own data. Think of it as a chatbot that has expert knowledge of your business. It understands your data landscape and can perform complex analytical tasks based on simple, conversational prompts.
This is a game-changer for several reasons:
It Democratizes Data: Business users no longer need to know SQL. Instead, they can ask questions like, “What were our top-selling products in the last quarter?” and get immediate, accurate answers.
It Automates Analysis: Consequently, data teams are freed from writing repetitive, ad-hoc queries. They can now focus on more strategic initiatives while the agent handles routine data exploration.
It Provides Unified Insights: Most importantly, a Cortex Agent can synthesize information from multiple sources. It can query your structured sales data from a table and cross-reference it with strategic goals mentioned in a PDF document, all in a single response.
The Blueprint: How a Cortex Agent Works
Under the hood, a Cortex Agent uses a simple yet powerful workflow to answer your questions. It orchestrates several of Snowflake’s Cortex AI features to deliver a comprehensive answer.
Planning: The agent first analyzes your natural language question to understand your intent. It figures out what information you need and where it might be located.
Tool Use: Next, it intelligently chooses the right tool for the job. If it needs to query structured data, it uses Cortex Analyst to generate and run SQL. If it needs to find information in your documents, it uses Cortex Search.
Reflection: Finally, after gathering the data, the agent evaluates the results. It might ask for clarification, refine its approach, or synthesize the information into a clear, concise answer before presenting it to you.
Step-by-Step Tutorial: Building a Sales Analysis Agent
Now, let’s get hands-on. We will build a simple yet powerful sales analysis agent. This agent will be able to answer questions about sales figures from a table and also reference goals from a quarterly business review (QBR) document.
Prerequisites
A Snowflake account with ACCOUNTADMIN privileges.
A warehouse to run the queries.
Step 1: Prepare Your Data
First, we need some data to work with. Let’s create two simple tables for sales and products, and then upload a sample PDF document.
Run the following SQL in a Snowflake worksheet:
-- Create our database and schema
CREATE DATABASE IF NOT EXISTS AGENT_DEMO;
CREATE SCHEMA IF NOT EXISTS AGENT_DEMO.SALES;
USE SCHEMA AGENT_DEMO.SALES;
-- Create a products table
CREATE OR REPLACE TABLE PRODUCTS (
product_id INT,
product_name VARCHAR,
category VARCHAR
);
INSERT INTO PRODUCTS (product_id, product_name, category) VALUES
(101, 'Quantum Laptop', 'Electronics'),
(102, 'Nebula Smartphone', 'Electronics'),
(103, 'Stardust Keyboard', 'Accessories');
-- Create a sales table
CREATE OR REPLACE TABLE SALES (
sale_id INT,
product_id INT,
sale_date DATE,
sale_amount DECIMAL(10, 2)
);
INSERT INTO SALES (sale_id, product_id, sale_date, sale_amount) VALUES
(1, 101, '2025-09-01', 1200.00),
(2, 102, '2025-09-05', 800.00),
(3, 101, '2025-09-15', 1250.00),
(4, 103, '2025-09-20', 150.00);
-- Create a stage for our unstructured documents
CREATE OR REPLACE STAGE qbr_documents;
Now, create a simple text file named QBR_Report_Q3.txt on your local machine with the following content and upload it to the qbr_documents stage using the Snowsight UI.
Quarterly Business Review – Q3 2025 Summary
Our primary strategic goal for Q3 was to drive the adoption of our new flagship product, the ‘Quantum Laptop’. We aimed for a sales target of over $2,000 for this product. Secondary goals included expanding our market share in the accessories category.
Next, we need to teach the agent about our structured data. We do this by creating a Semantic Model. This is a YAML file that defines our tables, columns, and how they relate to each other.
# semantic_model.yaml
model:
name: sales_insights_model
tables:
- name: SALES
columns:
- name: sale_id
type: INT
- name: product_id
type: INT
- name: sale_date
type: DATE
- name: sale_amount
type: DECIMAL
- name: PRODUCTS
columns:
- name: product_id
type: INT
- name: product_name
type: VARCHAR
- name: category
type: VARCHAR
joins:
- from: SALES
to: PRODUCTS
on: SALES.product_id = PRODUCTS.product_id
Save this as semantic_model.yaml and upload it to the @qbr_documents stage.
Now, let’s make our PDF document searchable. We create a Cortex Search Service on the stage where we uploaded our file.
CREATE OR REPLACE CORTEX SEARCH SERVICE sales_qbr_service
ON @qbr_documents
TARGET_LAG = '0 seconds'
WAREHOUSE = 'COMPUTE_WH';
Step 4: Combine Them into a Cortex Agent
With all the pieces in place, we can now create our agent. This single SQL statement brings together our semantic model (for SQL queries) and our search service (for document queries).
CREATE OR REPLACE CORTEX AGENT sales_agent
MODEL = 'mistral-large',
CORTEX_SEARCH_SERVICES = [sales_qbr_service],
SEMANTIC_MODELS = ['@qbr_documents/semantic_model.yaml'];
Step 5: Ask Your Agent Questions!
The agent is now ready! You can interact with it using the CALL command. Let’s try a few questions.
First up: A simple structured data query.
CALL sales_agent('What were our total sales?');
Next: A more complex query involving joins.
CALL sales_agent('Which product had the highest revenue?');
Then comes: A question for our unstructured document.
CALL sales_agent('Summarize our strategic goals from the latest QBR report.');
Finally , the magic: The magic! A question that combines both.
CALL sales_agent('Did we meet our sales target for the Quantum Laptop as mentioned in the QBR?');
This final query demonstrates the true power of a Snowflake Cortex Agent. It will first query the SALES and PRODUCTS tables to calculate the total sales for the “Quantum Laptop.” Then, it will use Cortex Search to find the sales target mentioned in the QBR document. Finally, it will compare the two and give you a complete, synthesized answer.
Conclusion: The Future is Conversational
You have just built a powerful AI data agent in a matter of minutes. This is a fundamental shift in how we interact with data. By combining natural language processing with the power to query both structured and unstructured data, Snowflake Cortex Agents are paving the way for a future where data-driven insights are accessible to everyone in an organization.
As Snowflake continues to innovate with features like Adaptive Compute and Gen-2 Warehouses, running these AI workloads will only become faster and more efficient. The era of conversational analytics has arrived, and it’s built on the Snowflake AI Data Cloud.
The financial services industry is in the midst of a technological revolution. At the heart of this change lies Artificial Intelligence. Consequently, financial institutions are constantly seeking new ways to innovate and enhance security. They also want to deliver personalized customer experiences. However, they face a significant hurdle: navigating fragmented data while adhering to strict compliance and governance requirements. To solve this, Snowflake has introduced Cortex AI for Financial Services, a groundbreaking suite of tools designed to unlock the full potential of AI in the sector.
What is Snowflake Cortex AI for Financial Services?
First and foremost, Snowflake Cortex AI is a comprehensive suite of AI capabilities. It empowers financial organizations to unify their data and securely deploy AI models, applications, and agents. By bringing AI directly to the data, Snowflake eliminates the need to move sensitive information. As a result, security and governance are greatly enhanced. This approach allows institutions to leverage their own proprietary data alongside third-party sources and cutting-edge large language models (LLMs). Ultimately, this helps them automate complex tasks and derive faster, more accurate insights.
Key Capabilities Driving the Transformation
Cortex AI for Financial Services is built on a foundation of powerful features. These are specifically designed to accelerate AI adoption within the financial industry.
Snowflake Data Science Agent: This AI-powered coding assistant automates many time-consuming tasks for data scientists. For instance, it handles data cleaning, feature engineering, and model prototyping. This, in turn, accelerates the development of crucial workflows like risk modeling and fraud detection.
Cortex AISQL: With its AI-powered functions, Cortex AISQL allows users to process and analyze unstructured data at scale. This includes market research, earnings call transcripts, and transaction details. Therefore, it transforms workflows in customer service, investment analytics, and claims processing.
Snowflake Intelligence: Furthermore, this feature provides business users with an intuitive, conversational interface. They can query both structured and unstructured data using natural language. This “democratization” of data access means even non-technical users can gain valuable insights without writing complex SQL.
Managed Model Context Protocol (MCP) Server: The MCP Server is a true game-changer. It securely connects proprietary data with third-party data from partners like FactSet and MSCI. In addition, it provides a standardized method for LLMs to integrate with data APIs, which eliminates the need for custom work and speeds up the delivery of AI applications.
Use Cases: Putting Cortex AI to Work in Finance
The practical applications of Snowflake Cortex AI in the financial services industry are vast and transformative:
Fraud Detection and Prevention: By training models on historical transaction data, institutions can identify suspicious patterns in real time. Consequently, this proactive approach helps minimize losses and protect customers.
Credit Risk Analysis: Cortex Analyst, a key feature, can analyze vast amounts of transaction data to assess credit risk. By building a semantic model, for example, financial institutions can enable more accurate and nuanced risk assessments.
Algorithmic Trading Support:While not a trading platform itself, Snowflake’s infrastructure supports algorithmic strategies. Specifically, Cortex AI provides powerful tools for data analysis, pattern identification, and model development..
Enhanced Customer Service: Moreover, AI agents powered by Cortex AI can create sophisticated customer support systems. These agents can analyze customer data to provide personalized assistance and automate tasks, leading to improved satisfaction.
Market and Investment Analysis: Cortex AI can also analyze a wide range of data sources, from earnings calls to market news. This provides real-time insights that are crucial for making informed and timely investment decisions.
The Benefits of a Unified AI and Data Strategy
By adopting Snowflake Cortex AI, financial institutions can realize a multitude of benefits:
Enhanced Security and Governance: By bringing AI to the data, sensitive financial information remains within Snowflake’s secure and governed environment.
Faster Innovation: Automating data science tasks allows for the rapid development and deployment of new products.
Democratization of Data: Natural language interfaces empower more users to access and analyze data directly.
Reduced Operational Costs: Finally, the automation of complex tasks leads to significant cost savings and increased efficiency.
Getting Started with Snowflake Cortex AI
For institutions ready to begin their AI journey, the path is clear. The Snowflake Quickstarts offer a wealth of tutorials and guides. These resources provide step-by-step instructions on how to set up the environment, create models, and build powerful applications.
The Future of Finance is Here
In conclusion, Snowflake Cortex AI for Financial Services represents a pivotal moment for the industry. By providing a secure, scalable, and unified platform, Snowflake is empowering financial institutions to seize the opportunities of tomorrow. The ability to seamlessly integrate data with the latest AI technology will undoubtedly be a key differentiator in the competitive landscape of finance.
Introduction: The Dawn of Context-Aware AI in Enterprise Data
Enterprise AI is experiencing a fundamental shift in October 2025. Organizations are no longer satisfied with isolated AI tools that operate in silos. Instead, they’re demanding intelligent systems that understand context, access governed data securely, and orchestrate complex workflows across multiple platforms.
Enter the Snowflake MCP Server—a groundbreaking managed service announced on October 2, 2025, that bridges the gap between AI agents and enterprise data ecosystems. By implementing the Model Context Protocol (MCP), Snowflake has created a standardized pathway for AI agents to interact with both proprietary company data and premium third-party datasets, all while maintaining enterprise-grade security and governance.
This comprehensive guide explores how the Snowflake MCP Server is reshaping enterprise AI, what makes it different from traditional integrations, and how organizations can leverage this technology to build next-generation intelligent applications.
What is the Model Context Protocol (MCP)?
Before diving into Snowflake’s implementation, it’s essential to understand the Model Context Protocol itself.
The Problem MCP Solves
Historically, connecting AI agents to enterprise systems has been a fragmented nightmare. Each integration required custom development work, creating a web of point-to-point connections that were difficult to maintain, scale, and secure. Data teams spent weeks building bespoke integrations instead of focusing on innovation.
The Model Context Protocol emerged as an industry solution to this chaos. Developed by Anthropic and rapidly adopted across the AI ecosystem, MCP provides a standardized interface for AI agents to connect with data sources, APIs, and services.
Think of MCP as a universal adapter for AI agents—similar to how USB-C standardized device connections, MCP standardizes how AI systems interact with enterprise data platforms.
Key Benefits of MCP
Interoperability: AI agents from different vendors can access the same data sources using a common protocol
Security: Centralized governance and access controls rather than scattered custom integrations
Speed to Market: Reduces integration time from weeks to hours
Vendor Flexibility: Organizations aren’t locked into proprietary ecosystems
Snowflake MCP Server: Architecture and Core Components
The Snowflake MCP Server represents a fully managed service that acts as a bridge between external AI agents and the Snowflake AI Data Cloud. Currently in public preview, it offers a sophisticated yet streamlined approach to agentic AI implementation.
How the Architecture Works
At its core, the Snowflake MCP Server connects three critical layers:
Layer 1: External AI Agents and Platforms The server integrates with leading AI platforms including Anthropic Claude, Salesforce Agentforce, Cursor, CrewAI, Devin by Cognition, UiPath, Windsurf, Amazon Bedrock AgentCore, and more. This broad compatibility ensures organizations can use their preferred AI tools without vendor lock-in.
Layer 2: Snowflake Cortex AI Services Within Snowflake, the MCP Server provides access to powerful Cortex capabilities:
Cortex Analyst for querying structured data using semantic models
Cortex Search for retrieving insights from unstructured documents
Cortex AISQL for AI-powered extraction and transcription
Data Science Agent for automated ML workflows
Layer 3: Data Sources This includes both proprietary organizational data stored in Snowflake and premium third-party datasets from partners like MSCI, Nasdaq eVestment, FactSet, The Associated Press, CB Insights, and Deutsche Börse.
The Managed Service Advantage
Unlike traditional integrations that require infrastructure deployment and ongoing maintenance, the Snowflake MCP Server operates as a fully managed service. Organizations configure access through YAML files, define security policies, and the Snowflake platform handles all the operational complexity—from scaling to security patches.
Cortex AI for Financial Services: The First Industry-Specific Implementation
Snowflake launched the MCP Server alongside Cortex AI for Financial Services, demonstrating the practical power of this architecture with industry-specific capabilities.
Why Financial Services First?
The financial services industry faces unique challenges that make it an ideal testing ground for agentic AI:
Data Fragmentation: Financial institutions operate with data scattered across trading systems, risk platforms, customer databases, and market data providers
Regulatory Requirements: Strict compliance and audit requirements demand transparent, governed data access
Real-Time Decisioning: Investment decisions, fraud detection, and customer service require instant access to both structured and unstructured data
Third-Party Dependencies: Financial analysis requires combining proprietary data with market research, news feeds, and regulatory filings
Key Use Cases Enabled
Investment Analytics: AI agents can analyze portfolio performance by combining internal holdings data from Snowflake with real-time market data from Nasdaq, research reports from FactSet, and breaking news from The Associated Press—all through natural language queries.
Claims Management: Insurance companies can process claims by having AI agents retrieve policy documents (unstructured), claims history (structured), and fraud pattern analysis—orchestrating across Cortex Search and Cortex Analyst automatically.
Customer Service: Financial advisors can query “What’s the risk profile of client portfolios exposed to European tech stocks?” and receive comprehensive answers that pull from multiple data sources, with full audit trails maintained.
Regulatory Compliance: Compliance teams can ask questions about exposure limits, trading patterns, or risk concentrations, and AI agents will navigate the appropriate data sources while respecting role-based access controls.
Technical Deep Dive: How to Implement Snowflake MCP Server
For data engineers and architects planning implementations, understanding the technical setup is crucial.
Configuration Basics
The Snowflake MCP Server uses YAML configuration files to define available services and access controls. Here’s what a typical configuration includes:
Service Definitions: Specify which Cortex Analyst semantic models, Cortex Search services, and other tools should be exposed to AI agents
Security Policies: Define SQL statement permissions to control what operations agents can perform (SELECT, INSERT, UPDATE, etc.)
Connection Parameters: Configure authentication methods including OAuth, personal access tokens, or service accounts
Tool Descriptions: Provide clear, descriptive text for each exposed service to help AI agents select the appropriate tool for each task
Integration with AI Platforms
Connecting external platforms to the Snowflake MCP Server follows a standardized pattern:
For platforms like Claude Desktop or Cursor, developers add the Snowflake MCP Server to their configuration file, specifying the connection details and authentication credentials. The MCP client then automatically discovers available tools and makes them accessible to the AI agent.
For custom applications using frameworks like CrewAI or LangChain, developers leverage MCP client libraries to establish connections programmatically, enabling sophisticated multi-agent workflows.
Security and Governance
One of the most compelling aspects of the Snowflake MCP Server is that it maintains all existing Snowflake security controls:
Data Never Leaves Snowflake: Unlike traditional API integrations that extract data, processing happens within Snowflake’s secure perimeter
Audit Logging: All agent interactions are logged for compliance and monitoring
Role-Based Access: Agents operate under defined Snowflake roles with specific privileges
Agentic AI Workflows: From Theory to Practice
Understanding agentic AI workflows is essential to appreciating the Snowflake MCP Server’s value proposition.
What Makes AI “Agentic”?
Traditional AI systems respond to single prompts with single responses. Agentic AI systems, by contrast, can:
Plan Multi-Step Tasks: Break complex requests into sequential subtasks
Use Tools Dynamically: Select and invoke appropriate tools based on the task at hand
Reflect and Iterate: Evaluate results and adjust their approach
Maintain Context: Remember previous interactions within a session
How Snowflake Enables Agentic Workflows
The Snowflake MCP Server enables true agentic behavior through Cortex Agents, which orchestrate across both structured and unstructured data sources.
Example Workflow: Market Analysis Query
When a user asks, “How has our semiconductor portfolio performed compared to industry trends this quarter, and what are analysts saying about the sector?”
The agent plans a multi-step approach:
Query Cortex Analyst to retrieve portfolio holdings and performance metrics (structured data)
Search Cortex Search for analyst reports and news articles about semiconductors (unstructured data)
Cross-reference findings with third-party market data from partners like MSCI
Synthesize a comprehensive response with citations
Each step respects data governance policies, and the entire workflow happens within seconds—a task that would traditionally require multiple analysts hours or days to complete.
Open Semantic Interchange: The Missing Piece of the AI Puzzle
While the Snowflake MCP Server solves the connection problem, the Open Semantic Interchange (OSI) initiative addresses an equally critical challenge: semantic consistency.
The Semantic Fragmentation Problem
Enterprise organizations typically define the same business metrics differently across systems. “Revenue” might include different line items in the data warehouse versus the BI tool versus the AI model. This semantic fragmentation undermines trust in AI insights and creates the “$1 trillion AI problem“—the massive cost of data preparation and reconciliation.
How OSI Complements MCP
Announced on September 23, 2025, alongside the MCP Server development, OSI is an open-source initiative led by Snowflake, Salesforce, BlackRock, and dbt Labs. It creates a vendor-neutral specification for semantic metadata—essentially a universal language for business concepts.
When combined with MCP, OSI ensures that AI agents not only can access data (via MCP) but also understand what that data means (via OSI). A query about “quarterly revenue” will use the same definition whether the agent is accessing Snowflake, Tableau, or a custom ML model.
Industry Impact: Who Benefits from Snowflake MCP Server?
While initially focused on financial services, the Snowflake MCP Server has broad applicability across industries.
Healthcare and Life Sciences
Clinical Research: Combine patient data (structured EHR) with medical literature (unstructured documents) for drug discovery
Population Health: Analyze claims data alongside social determinants of health from third-party sources
Regulatory Submissions: AI agents can compile submission packages by accessing clinical trial data, adverse event reports, and regulatory guidance documents
Retail and E-Commerce
Customer Intelligence: Merge transaction data with customer service transcripts and social media sentiment
Supply Chain Optimization: Agents can analyze inventory levels, supplier performance data, and market demand signals from external sources
Personalization: Create hyper-personalized shopping experiences by combining browsing behavior, purchase history, and trend data
Manufacturing
Predictive Maintenance: Combine sensor data from IoT devices with maintenance logs and parts inventory
Quality Control: Analyze production metrics alongside inspection reports and supplier certifications
Supply Chain Resilience: Monitor supplier health by combining internal order data with external financial and news data
Implementation Best Practices
For organizations planning to implement the Snowflake MCP Server, following best practices ensures success.
Start with Clear Use Cases
Begin with specific, high-value use cases rather than attempting a broad rollout. Identify workflows where combining structured and unstructured data creates measurable business value.
Invest in Semantic Modeling
The quality of Cortex Analyst responses depends heavily on well-defined semantic models. Invest time in creating comprehensive semantic layers using tools like dbt or directly in Snowflake.
Establish Governance Early
Define clear policies about which data sources agents can access, what operations they can perform, and how results should be logged and audited.
Design for Explainability
Configure agents to provide citations and reasoning for their responses. This transparency builds user trust and satisfies regulatory requirements.
Monitor and Iterate
Implement monitoring to track agent performance, query patterns, and user satisfaction. Use these insights to refine configurations and expand capabilities.
Challenges and Considerations
While powerful, the Snowflake MCP Server introduces considerations that organizations must address.
Cost Management
AI agent queries can consume significant compute resources, especially when orchestrating across multiple data sources. Implement query optimization, caching strategies, and resource monitoring to control costs.
Data Quality Dependencies
Agents are only as good as the data they access. Poor data quality, incomplete semantic models, or inconsistent definitions will produce unreliable results.
Skills Gap
Successfully implementing agentic AI requires skills in data engineering, AI/ML, and domain expertise. Organizations may need to invest in training or hire specialized talent.
Privacy and Compliance
While Snowflake provides robust security controls, organizations must ensure that agent behaviors comply with privacy regulations like GDPR, especially when combining internal and external data sources.
The Future of Snowflake MCP Server
Based on current trends and Snowflake’s product roadmap announcements, several developments are likely:
Expanded Industry Packs
Following financial services, expect industry-specific Cortex AI suites for healthcare, retail, manufacturing, and public sector with pre-configured connectors and semantic models.
Enhanced Multi-Agent Orchestration
Future versions will likely support more sophisticated agent crews that can collaborate on complex tasks, with specialized agents for different domains working together.
Deeper OSI Integration
As the Open Semantic Interchange standard matures, expect tighter integration that makes semantic consistency automatic rather than requiring manual configuration.
Real-Time Streaming
Current implementations focus on batch and interactive queries. Future versions may incorporate streaming data sources for real-time agent responses.
Conclusion: Embracing the Agentic AI Revolution
The Snowflake MCP Server represents a pivotal moment in enterprise AI evolution. By standardizing how AI agents access data through the Model Context Protocol, Snowflake has removed one of the primary barriers to agentic AI adoption—integration complexity.
Combined with powerful Cortex AI capabilities and participation in the Open Semantic Interchange initiative, Snowflake is positioning itself at the center of the agentic AI ecosystem. Organizations that embrace this architecture now will gain significant competitive advantages in speed, flexibility, and AI-driven decision-making.
The question is no longer whether to adopt agentic AI, but how quickly you can implement it effectively. With the Snowflake MCP Server now in public preview, the opportunity to lead in your industry is here.
Ready to get started? Explore the Snowflake MCP Server documentation, identify your highest-value use cases, and join the growing community of organizations building the future of intelligent, context-aware enterprise applications.
Key Takeaways
The Snowflake MCP Server launched October 2, 2025, as a managed service connecting AI agents to enterprise data
Model Context Protocol provides a standardized interface for agentic AI integrations
Cortex AI for Financial Services demonstrates practical applications with industry-specific capabilities
Organizations can connect platforms like Anthropic Claude, Salesforce Agentforce, and Cursor to Snowflake data
The Open Semantic Interchange initiative ensures AI agents understand data semantics consistently
Security and governance controls remain intact with all processing happening within Snowflake
Early adoption provides competitive advantages in AI-driven decision-making
