Snowflake Cortex Agents: Scalable AI for Enterprise Data Insights

Are you dealing with loads of unstructured data? Facing issues in identifying which datasets are meaningful? Struggling to make your AI applications safe and compliant?

There’s no need to be concerned about these challenges anymore. Cortex Agents, by Snowflake, have been introduced to deliver secure, safe, compliant, and reliable solutions for both individuals and organizations alike.

Cortex Agents are intelligent and task-oriented AI agents that are designed to analyze, query, and act upon both structured and unstructured data files. They use Large Language Models (or LLMs) for understanding natural language requests, planning tasks, and utilizing various tools for generating governed and accurate insights.

Enabling a more comprehensive and intellectual approach towards data reporting and evaluation, Cortex Agents allows better handling in complex enterprise environments.

Cortex Agents has been regarded as a major step by Snowflake since it allows them to launch AI agents at the proper scale with combined governance as well as retrieval of data from both unstructured and structured data. They allow Snowflake customers to use native platforms for developing AI to resolve business requirements.

How is Snowflake suitable for the development of AI Agent?

Cortex Agents, as part of the Snowflake consulting & implementation services, are designed to deliver intelligent, compliant, and scalable automation across your data ecosystem. They can plan by interpreting even ambiguous queries, breaking them into manageable tasks, and routing them through the appropriate tools for policy-aligned execution.

Once the plan is in place, they leverage Snowflake tools to access and analyze both structured and unstructured data sources.

Through reflection, these agents assess tool outputs to refine their approach, whether that means requesting more information or generating a final, accurate response.

Post the deployment, the teams can monitor and iterate agent behavior using tools like TruLens to ensure performance, trust, and continuous optimization on scale.

Business Impact of Snowflake Cortex Agents

Quicker insights: Speeds up decision making process through automated data identification and analysis.

Scalable AI Adoption: Lets enterprises incorporate AI-focused intelligent solutions throughout the business processes.

Enterprise-Grade Security: Offers total compliance and data control within Snowflake’s governed environment.

Higher Productivity: Decreases manual efforts for analytics and any third party dependency.

Snowflake Cortex Agents – Understanding their Workflow

Cortex Agents orchestrate both structured and unstructured data sources to deliver insights. They plan tasks, use tools to execute these tasks, and generate responses.

Agents use Cortex Analyst (structured) and Cortex Search (unstructured) as tools, along with LLMs, to analyze data. Cortex Search extracts insights from unstructured sources, while Cortex Analyst generates SQL to process structured data.

The overall workflow of cortex agents involves four major components:

Proper Planning

Several applications tend to shift the processing of data from structured and unstructured sources. A conversational application designed to respond to user queries is an ideal example of this scenario. A business user might wish to check for a list of most distributors in terms of revenue which is derived from structured data sources. Again, the agent might seek information about a contract from unstructured data sources. Cortex Agents can parse a request to orchestrate a plan and arrive at a solution or response.

Tool usage

Once the plan is set, these agents initiate retrieval of data in an efficient manner. The Cortex Search (a part of the Cortex Agents) extracts the required information from unstructured data sources. And the Cortex Analyst (another part) develops SQL queries to process structured data. This combination of tool identification and execution delivers sophisticated applications capable of handling enterprise-grade data.

Reflection

Once the tools are properly used, the agent evaluates the next step – seeking explanation, repetition, or creating a final answer. This allows the agent to manage complicated data queries while following the Snowflake’s protocols.

Monitoring & Iteration

Post deployment of the agents; users will be able to monitor, evaluate, and improve the overall behavior for undertaking constant improvements. Developers, on the client-side application, can implement TruLens for observing/monitoring the interactions of these agents. This enables enterprises to update/upgrade AI agents within their security and compliance parameters.

Common Uses of Cortex Agents

These agents are designed to bring AI workflows into the enterprise level through planning across both structured and unstructured data sources. Also, they enable delivering proper information while ensuring efficiency and accuracy. Below are some of the common uses of these agents.

Upgrading business intelligence

Cortex agents can break down complicated queries, acquire appropriate data, and create relevant responses. Also, they can automate the overall procedure of recognizing trends and patterns, along with any abnormality in data. These agents are employed to investigate data, identify connections, and acquire an in-depth understanding of business operations.

Enhancing data efficiency and accuracy Cortex agents can automate the complete data retrieval and evaluation process, thereby decreasing the need to get and analyze the data manually. Due to automation of data retrieval and analysis, decisions can be taken faster and on a more informed level. These agents ensure that the data retrieved is accurate and adheres to enterprise policies. Fueling AI-powered applications Cortex agents can be incorporated into conversational interfaces for responding to user queries related to data. These agents can be employed for automating tasks that are repetitive in nature, therefore giving employees/clients/customers focus on more strategically vital tasks.

Security Applications

Cortex agents are employed to analyze data for identifying and countering threats. These agents can be used for accumulating forensic data for security professionals to investigate incidents. They also help organizations highlight and manage cyber risks to enhance their overall security response and capabilities.

What Leaders Gain?

Stronger governance and risk control: AI that works within the defined compliance and security parameters.

Cross functional alignment: Reliable and authentic data for all the teams to focus on a common goal.

Clear executive visibility: AI-backed and powered unified data for faster decision making.

Future-ready AI strategy: A reliable foundation for future AI initiatives across the organization.

Better ROI in investments: Increasing the present value of Snowflake data and infrastructure assets.

Why Build Cortex Agents with Snowflake?

A good question. There might be several platforms; however, building cortex agents using Snowflake provides an impactful manner to employ AI for various data-driven enterprise grade innovations.

They let the development of trustworthy conversational applications which employ both unstructured and structured data.

A quick glance at the reasons that highlight the importance of snowflake for creating them.

• Employing both structured and unstructured data for a unified data experience.

• Incorporate rich context for delivering more accurate, AI-driven insights.

• Streamline automation of data retrieval & workflow for quicker AI deployment.

• Understand agent decisions and easily integrate via APIs.

• Integrate with other conversational applications for real-time AI interactions.

• Select from GPT-4.1, Claude 3.7 Sonnet, o4-mini, and other top LLM models.

• Simplified user experience with intuitive AI access to complex data and faster insights.

• Empower teams to make data-driven decisions quickly.

• Develop enterprise-grade performance within the Snowflake ecosystem.

Components of Cortex Agents

Orchestration

Managing the flow of data & operations throughout different sources such as data warehouses, unstructured documents, and APIs. It works as a control center to ensure seamless coordination among various data inputs, tools, and models. This will enable the AI workflows to be simplified and constant.

Task Planning

Transforming user queries into actionable subtasks to decide the ideal sequence for their execution. This will let cortex agents decide and strategize the retrieval and processing of data to ensure correct and effective responses for complex queries.

Tool Execution

Executing the correct set of tools as per the data type helps in precise and relevant analysis and acquisition of required information. For example, Cortex Analyst is designed for structured data types, whereas Cortex Search is for unstructured data types.

LLMs or Large Language Models

By incorporating with the most popular LLMs such as OpenAI GPT-4.1 and o4-mini, it allows users to ask queries in simple layman terms while receiving intelligent, human-like insights as per their queries.

Response Generation

This component enables refining output from various data sources, tools, & LLMs into a final and acceptable response to a user’s query. This ensures that the result/output delivered is aligned with the user’s intent.

Cortex Analyst: AI-powered SQL generation, with semantic understanding

Unlike typical text-to-SQL systems that rely only on pattern matching, Cortex Analyst uses a semantic model to map business terms to underlying data. This unique approach improves precision in real-world use cases that involve complex multi-table environments.

What’s new with Cortex Analyst?

Handling increased schema complexity

Cortex Analyst now goes beyond just star-schema and Snowflake-schema JOINs. Our new advanced JOIN validation mitigates common issues, such as JOIN hallucinations and double counting, which often arise in complex queries. This allows Cortex Analyst to support multi-table queries without compromising precision.

Semantic model generation and monitoring

Our public preview of the new Analyst Admin UI in Snowsight simplifies the process of building and refining semantic models. Admins can select tables and columns and use LLMs (running within Snowflake’s secure perimeter) to generate a starting Semantic Model YAML file. The admin interface also monitors user engagement and feedback. This allows customers to track usage and make informed improvements to semantic models over time.

Customization for business-specific logic

With Custom Instructions now in GA, users can customize Cortex Analyst to their unique business needs using natural language in the Semantic Model file. Common use cases include specifying fiscal year start dates, explaining internal naming conventions, and prioritizing key tables during SQL generation.

Proven performance on benchmarks

Based on internal benchmarks, we have achieved 90% accuracy for text-to-SQL use cases. With Anthropic’s Claude 3.5 Sonnet, we are able to further enhance the performance for improved experience. Cortex Analyst, running Claude, outperforms other models on real-world queries by using information stored in the semantic model.

With these updates, Cortex Analyst enhances structured data analysis and simplifies admin setup for agentic applications.

Cortex Search: High-quality context engine for unstructured data

Cortex Agents use Cortex Search to retrieve unstructured data (text, audio, image, video). Cortex Search is a native hybrid search, a combination of vector and lexical (keyword) search, with an additional semantic reranking step, to deliver high-quality, low-latency retrieval at scale.

What’s new with Cortex Search?

Increased scale and affordability

Cortex Search now supports indexing hundreds of millions of rows. Additionally, serving costs for Cortex Search have been reduced by 30% because of infrastructure optimizations.

Improved customizability

Cortex Search now provides the ability to select the vector embedding model for semantic search. This includes two multilingual models, snowflake-arctic-embed-l-v2.0 and voyage-multilingual-2. Additionally, Cortex Search supports date-range filtering on metadata columns.

New preview features

New preview features include the Cortex Search Admin UI (for observability and quality tuning); boosting and decaying numeric and time signals; result in confidence scores; and advanced filtering capabilities. With these new features, Cortex Search offers a scalable and customizable foundation for search and agentic applications built on Snowflake data.

AI Observability: Evaluation and tracing of AI Agents

AI observability brings reliability, performance, and trust to generative AI applications. With proper evaluations and monitoring, businesses can get more accurate results, optimize costs, and address their governance needs.

What’s new with Cortex AI Observability?

Cortex AI Observability on Snowflake is powered by TruLens and will be available in public preview soon.

End-to-end evaluation

AI Observability can evaluate the performance of agents and apps, using techniques such as LLM-as-a-judge. It can report metrics such as relevance and harmfulness, giving customers the ability to quickly iterate and refine the agent for improved performance.

Comparison

Users can compare evaluation runs side-by-side and assess the quality and accuracy of responses across different LLM configurations to identify the best configuration for production deployments.

Comprehensive tracing

Customers can enable logging for every step of agent executions across input prompts, tool use and final response generation. This allows for easy debugging and refinement for accuracy, latency, and cost.

Snowflake Cortex Agents – Applications Across Industries

Healthcare: Clinics and hospitals use AI for writing, summarizing, and recording any and every communication with patients. This helps in reducing burden on admins while increasing patient management and capacity.

Legal and Compliance: The Cortex Search component facilitates quick retrieval of information across several documents, case studies, and contracts.

Retail and consumer goods: Cortex Agents help analyze customer sentiments and purchase behaviors, while the search functions enhance product discovery and personalized marketing.

Financial services: Portfolio managers produce investment ideas from earnings and transcripts and news. The underwriters create summaries through loan documents. Cortex Agents automates all these processes through quick scanning of huge data from regulatory documents.

Sales and marketing: The teams make use of specialized AI bots to identify relevant pitch materials and case studies quickly.

Manufacturing and R&D: Researchers incorporate the Cortex Agent bots to synthesize scientific literature and other relevant technical documents.

Snowflake Cortex Agents – Challenges and Ways to Overcome Them

Like every tool and platform, Snowflake Cortex Agents come up with their own set of challenges. Obviously, they also come up with their own set of solutions, so users need not worry about not being able to get out of those challenges. Let’s learn about them to understand them better.

Challenges

• Compatibility issues: Cortex agents are required to be compatible with the different environments and versions of software and OS.

• Consumption of resources: They can utilize a significant chunk of resources, possibly affecting performance. 

• Evolving threats: Threats related to cyber domain are constantly upgrading. This needs the agents to be constantly updated with the latest threat of intelligence and security features.

• Failures in upgrading agents: Cortex agents might stop interacting or experience issues during their upgrade.

• Delays in updating content: Any delay in updating their content; the systems might be prone to new threats.

Overcoming Them

• Phased rollout: Releasing the update in a phased sequence by starting with the authorized control group and then gradually out to the rest of the organization.

• Global settings: Adjusting global settings on the cortex agents in terms of upgrades and updates to ensure seamless policies throughout the network.

• Regular updates: Keeping the latest version of cortex agents ensures efficient protection against new threats.

• Proactive communication: On time reporting issues in the cortex agents to the respective team can result in faster resolution.

• Monitoring: Tracking the health and performance of cortex agents on a regular basis can help identify potential issues at an early stage.

• Frequency of updating content: Updating content on an immediate basis helps resolve any latest threat.

• Allocation of resources: Allocating sufficient resources to the agents as well as their relevant processes is vital to prevent any setbacks to their performance.

Real Business Outcomes with Snowflake Cortex Agents

Increase in patient throughput: Alberta Health Services incorporated the Cortex AI for automating its documentation process, especially for the emergency department’s physicians. By proper transcription, recording, and summarization, doctors were able to decrease manual paperwork, thereby facilitating them to treat 13% more patients than before.

Cost Reduction in Support Operations: TS Imagine, a UK-based provider of financial services, implemented Cortex AI to automate its monitoring system of 100k plus emails and 60k annual support tickets. This led to an instant AI-powered categorization, thus letting the business save significantly 30% on operational costs.

Quick implementation for customer insights: Advisor360o achieved a great reduction in their client feedback process from a month to 2 days by utilizing Cortex AI to develop a customer sentiment pipeline. This allowed the company to provide spontaneous and accurate feedback to customers.

Conclusion

Whether it’s enhancing business intelligence, automating data retrieval, or supporting security initiatives, Cortex Agents provide a robust foundation for aligning AI-driven workflows with organizational goals. By operating natively within the Snowflake consulting services and ecosystem, they ensure that governance, compliance, and trust remain central to every interaction ecosystem, they ensure that governance, compliance, and trust remain central to every interaction.

BluEnt offers its years of expertise and experience in Snowflake services that are ideal for the development of Cortex Agents. It ensures that organizations can get the best AI agents to resolve their obstacles while scaling their business. BluEnt operationalizes the Snowflake Cortex Agents to act as KPI-synced business operators. Rather than launching simple GenAI experiments, we focus on developing role-embedded AI agents directly into the enterprise processes. The result is not AI potential but a well-controlled integration for eliminating backlogs and speeding decision making.

FAQs

How is Snowflake Cortex Agent different from traditional BI or SQL tools?Snowflake Cortex Agent fundamentally differs from traditional Business Intelligence (BI) and SQL tools by using agentic AI to enable natural language interaction and automatic execution of multi-step tasks across both structured and unstructured data, all within a unified and secure platform.

Can Snowflake Cortex Agents securely access enterprise data?Yes, Snowflake Cortex Agents can securely access enterprise data. They are built to operate natively within the Snowflake ecosystem, which means they inherit Snowflake’s established security, governance, and compliance protocols.

How do Cortex Agents improve data teams’ productivity?Snowflake Cortex Agents improve data teams’ productivity by providing autonomous, intelligent, and secure AI systems that automate complex, multi-step data tasks, reducing the need for manual data preparation, SQL coding, and dashboard building. By leveraging large language models (LLMs) to understand and interact with both structured and unstructured data, these agents allow data teams to move from reactive reporting to proactive analysis.

Can Cortex Agents be embedded into various enterprise applications?Yes, Snowflake Cortex Agents can be embedded into various enterprise applications to provide, analyze, and act upon data directly within existing workflows. They are designed to bridge the gap between complex data operations (structured and unstructured) and user-facing applications, reducing the need to move data across systems.