Why Businesses Should Use Agentic AI in Data Management?
Businesses are struggling to manage and maintain the unprecedented growth of data, whether it be structured, unstructured or semi-structured data. As many businesses are still using traditional data management and legacy systems, they are facing challenges to store, manage and analyze this large volume of data efficiently. Businesses are constantly looking for platforms and tools that will help them harness this data without increasing operational costs. By integrating Agentic AI in data management systems, your workload will reduce drastically.
Agentic AI in Data Management: A Hit or a Miss?
Agentic AI refers to artificial intelligence systems that operate autonomously on behalf of their users. These intelligent systems make decisions, learn from interactions, and continuously improve without requiring constant human involvement. This autonomy makes Agentic AI ideal for managing complex challenges and making the implementation of artificial intelligence in business easier!
With the surge of data generated daily, traditional data management techniques and legacy systems are not enough. Additionally, relying on human intervention to handle data entry, cleansing, and analysis is time-consuming and creates errors. Agentic AI automates these tasks, ensuring data integrity and providing real-time insights that are vital for informed decision-making. Powered by advanced machine learning (ML) and natural language processing (NLP), Agentic AI systems can manage vast datasets with ease and precision, minimizing human error and optimizing efficiency.
With the integration of Snowflake AI Data Cloud and Kasmo’s expertise, businesses can manage, store, analyze and harness the power of unstructured, structured and semi-structured data seamlessly.
The Benefits of Integrating Agentic AI in Data Management
Enhanced Operational Efficiency
Agentic AI systems streamline data-related tasks such as data entry, cleansing, and validation. By automating these time-consuming processes, businesses free up valuable human resources to focus on more strategic tasks. This boosts operational efficiency and drives organizational growth.
Improved Accuracy and Precision
One of the standout advantages of Agentic AI is its ability to combine the power of large language models (LLMs) with traditional programming techniques, resulting in more accurate decision-making. By leveraging real-time data, Agentic AI delivers outputs that are more accurate and actionable, minimizing errors and ensuring optimal results.
Improved Data Quality
High-quality data is crucial for making sound business decisions. Agentic AI continuously monitors data sources, detecting anomalies, correcting errors, and ensuring data is up to date.
Real-Time Insights
Agentic AI empowers businesses by providing real-time insights, allowing companies to quickly adapt to shifting market trends, customer preferences, and discover new opportunities.
Enhanced Scalability
Traditional data management systems often struggle to scale effectively, resulting in performance bottlenecks, increased costs, and operational inefficiencies. Agentic AI is built to handle large-scale, complex data environments, adapting seamlessly to growing data volumes. This scalability ensures that businesses can continue to operate smoothly without compromising regular operations.
What are Data Agents?
Data Agents are specialized AI systems designed to seamlessly integrate data with relevant tools, enabling businesses to generate accurate, data-driven insights. The ability to make decisions based on reliable data can significantly impact a company’s success or failure.
Just like other AI agents, specialization is key for Data Agents. They need to be tailored to specific business functions and departments. For instance, a sales team might use a different Data Agent compared to a research and development team, as their data needs differ.
Key Actions of Data Agents
Data Retrieval
Data Agents are designed to efficiently retrieve data from both structured and unstructured data. Whether pulling data from a database or social media platforms, the agent must route requests accurately while adhering to strict data access and governance policies.
Collaboration with Other Agents
Sometimes, Data Agents need to combine the retrieved data with other tools or collaborate with other agents. This collaboration is necessary to perform complex tasks, such as generating reports, creating charts, or analyzing market trends.
Output Delivery
Once the required data is processed, the results need to be delivered effectively. This could be in the form of sending messages through a chat interface, creating customizable dashboards, or even emailing a detailed report to your stakeholders or partners.
Snowflake for Data Agents
Source: Snowflake
AI agents have already proven their value across various enterprise functions, from customer service to sales, finance, and IT operations. These data agents help automate tasks, enhance productivity, and streamline workflows. However, the effectiveness of AI agents or data agents depends on the quality of the underlying data and the system they are stored in. Many organizations struggle to align their AI models with the massive volumes of data, leading to inefficiencies.
Snowflake’s AI-Powered Data Agents
Source: Snowflake
The integration of Snowflake AI Data Cloud and the launch of Cortex Agents have provided businesses with a powerful tool to bridge the gap between structured and unstructured data. These solutions make it possible to retrieve, process, and analyze data with high accuracy while ensuring compliance with governance protocols. With the release of Cortex Agents, customers now have access to a unified platform that allows seamless access to both unstructured and structured data.
Data Agents specialize in combining both structured (e.g., tables and views) and unstructured (e.g., text and audio) data to generate precise insights. Businesses can use Data Agents to improve operational efficiency, especially when it comes to handling large datasets. However, businesses still face the challenge of integrating AI models with diverse data types.
Large Language Models (LLMs) excel at working with unstructured data, but many organizations lack the infrastructure to handle such data properly. On the contrary, even though structured data can be managed efficiently, when it comes to integrating LLMs to interpret and analyze it effectively, it becomes challenging, especially if you are using traditional systems.
Cortex Agents
The introduction of Cortex Agents expands Snowflake’s capabilities by streamlining data orchestration across structured and unstructured data sources. These agents integrate Cortex Analyst (for structured data retrieval) and Cortex Search (for unstructured data retrieval), working in tandem with LLMs to provide accurate and actionable insights.
With Cortex Agents, enterprises can build scalable AI-driven solutions while ensuring that the data remains secure and compliant with governance policies. This unified approach to data processing and retrieval empowers businesses to develop AI-powered applications across a variety of industries, from finance to healthcare, retail, and manufacturing.
Use Cases for Cortex Agents in Different Industries
- Media: Identify trends in ad performance and adjust marketing strategies accordingly.
- Financial Services: Assess market conditions and adjust portfolios to maximize returns.
- Healthcare and Life Sciences: Monitor patient data from wearables to provide real-time insights.
- Retail: Predict demand and adjust stock levels in real time.
- Manufacturing: Predict machine failures and optimize inventories based on demand.
How Snowflake Intelligence and Data Science Agent will bridge the gap between enterprise data and business action?
At the Snowflake Summit 2025, Snowflake unveiled two groundbreaking innovations aimed at revolutionizing enterprise data management: Snowflake Intelligence and Data Science Agent. These agentic AI solutions are designed to bridge the gap between complex enterprise data and actionable business insights, enabling both technical and non-technical users to easily interact with and derive insights from their data.
Snowflake Intelligence
Snowflake Intelligence, currently in public preview, offers a seamless, unified conversational interface for business users and IT teams. With the help of intelligent data agents, users can now ask natural language questions and instantly uncover actionable insights across a wide range of data formats, including both structured tables and unstructured documents. This capability enables even non-technical users to interact with enterprise data without writing a single line of code.
By running directly within Snowflake’s environment, Snowflake Intelligence inherits all security protocols, data masking, and governance policies automatically. It integrates data from multiple sources like Salesforce, Google Drive, Box, Zendesk, and more, using the innovative Snowflake Openflow to bring together insights from various data formats simultaneously. Whether it’s a document, spreadsheet, or database, Snowflake Intelligence helps organizations break down data silos and gain a comprehensive understanding of their business metrics. Leveraging large language models from Anthropic and OpenAI, Snowflake Intelligence provides a powerful, no-code interface that simplifies business decision-making.
Data Science Agent
Data Science Agent (currently in private preview) aims to significantly enhance the productivity of data science teams. Developing and debugging machine learning (ML) models can be an extremely time-consuming process, often leading to bottlenecks and delayed completion of projects. Data Science Agent automates many of the routine tasks involved in ML model development, such as data analysis, preparation, feature engineering, and training, helping data scientists save valuable time and accelerate production.
Benefits of Snowflake Intelligence and Data Science Agents
Snowflake Intelligence and Data Science Agent represents a major leap forward in how organizations will interact with their data. By integrating advanced reasoning capabilities and machine learning workflows into the Snowflake platform, these innovations aim to simplify the AI and ML experience for a broad range of users.
Non-technical business users can have natural, intelligent conversations by utilizing their business data, while data scientists can focus on more strategic tasks by automating mundane parts of the ML process. The Cortex Agents that power Snowflake Intelligence and Data Science Agent ensure that every interaction with data remains accurate, secure, and governed, without the need for complex coding or technical expertise.
Why Kasmo Should Be Your Snowflake Implementation Partner
Kasmo is a global Snowflake Consulting Partner, helping businesses unlock the potential of their data through advanced solutions. Headquartered in Plano, Texas, and a dedicated Snowflake Center of Excellence (CoE), in Hyderabad, our team of experts focuses on creating reusable solutions tailored to address your business challenges and help you drive operational excellence.
With over 80+ certified consultants, we provide businesses with high-level expertise in the Snowflake ecosystem. Kasmo has a proven track record of delivering impactful solutions to clients with our unique accelerators and solutions. These solutions combined with our expertise help clients to successfully migrate and utilize the full potential of their data from diverse sources like SAP, SQLServer, MongoDB and PostgreSQL.
With a deep understanding of industry challenges and Snowflake’s capabilities, Kasmo is uniquely positioned to help businesses with:
- Strategic Assessment and Data Migration: Kasmo helps businesses assess their current infrastructure and design a scalable migration strategy to Snowflake. This ensures a smooth transition from legacy systems to a modern, cloud-native environment.
- Custom Solutions: By leveraging Snowflake and AI capabilities, Kasmo can build custom applications that meet the most stringent regulatory requirements.
- End-to-End Support: From implementation to optimization, Kasmo provides end-to-end support, enabling businesses to focus on their core competencies while Kasmo ensures the data architecture aligns with the organization’s strategic goals.
- Maximizing ROI with Cost-Efficient Strategies: Kasmo helps organizations reduce the total cost of ownership (TCO) by optimizing resource allocation, leveraging Snowflake’s elastic compute model, and enabling cost-effective scaling.
Conclusion
As businesses increasingly rely on data to drive decisions, managing and processing large volumes of both structured and unstructured data becomes a critical challenge. Integrating Agentic AI in data management systems helps businesses drive smarter decision-making as AI agents continuously learn from data interactions and improve without the need for constant human intervention.
With tools like Cortex Agents and Snowflake Intelligence, businesses can enhance operational efficiency, reduce errors, and unlock real-time insights that lead to faster, more informed decisions. The ability to scale seamlessly and integrate data from multiple sources, while maintaining governance and compliance. As AI continues to evolve, leveraging these AI agents and data agents will empower companies to break down data silos, automate complex workflows, and foster accurate, data-driven decision-making.
