Powering the Next Generation of Agentic AI Applications With Snowflake

Stay ahead of the curve with agentic AI applications powered by the Snowflake platform.

May 20, 2025 | By Shishir Shrivastava

Technology is advancing at an incredible pace, particularly in the world of artificial intelligence (AI). One moment we’re adapting to the latest AI development, and the next moment, another breakthrough is already on the horizon.

Nevertheless, recent developments in autonomous or semiautonomous systems—also known as AI agents or agentic AI—offer new opportunities for enterprises. Agentic AI can navigate complex tasks, evaluate the environment, make informed decisions and take actions to achieve specific goals. Unlike traditional AI models that focus on individual tasks (e.g., image recognition or language translation), AI agents are designed to be more general-purpose, proactive and capable of reasoning through complex scenarios. Combining advancements in machine and reinforcement learning, natural language processing, and contextual awareness, agentic AI can operate independently or collaboratively.

The concept of agentic AI stems from the evolution of AI from rule-based algorithms to proactive systems that simulate human-like decision-making. With the rise of large language models (LLMs) and multimodal AI, agentic AI has gained traction as a way to create systems that can handle open-ended tasks, adapt to new information and interact with users or other systems in a goal-oriented manner.

Agentic AI: Catalyst to the Next Industrial Wave

Agentic AI use cases are transforming how industries enhance efficiency, automate processes and drive innovation. AI agents offer many benefits to enterprises, such as:

• Automating repetitive tasks, reducing labor and operational costs
• Handling high volumes of tasks across systems, enabling growth without proportional resource increases
• Processing data instantly and responding to dynamic conditions or leveraging analytics to uncover trends and optimize strategies
• Interpreting data and making recommendations

The power of agentic AI applications? The ability to integrate with data ecosystems, using structured and unstructured data to drive insights and actions. This is where platforms like Snowflake, an AI-driven cloud, are critical. Snowflake’s Data Cloud enables seamless data sharing, storage and processing across multiple clouds, providing the perfect foundation for agentic AI to thrive. How? By providing access to high-quality, governed data at scale.

Agentic AI Use Cases and Applications

Agentic AI, when built on a robust data platform like Snowflake, can revolutionize operations and customer experience across most industries. 

In manufacturing, one of the key agentic AI use cases is monitoring real-time data from machinery to predict when equipment might fail based on patterns. Instead of waiting for a breakdown, AI automatically schedules maintenance, orders replacement parts or adjusts settings to extend the machine’s lifespan, minimizing downtime and repair costs.

In the financial services sector, agentic AI enhances fraud detection and risk management. As part of modern enterprise AI applications, AI agents leverage Snowflake’s ability to handle massive data sets to continuously monitor transactions, detect anomalies and initiate preventative measures without human oversight.

In healthcare, agentic AI use cases are transforming patient care and improving operational efficiency. AI agents monitor patient data in real time, predict potential health risks and recommend personalized treatment plans by analyzing historical medical records stored in Snowflake’s secure environment. 

Building Agentic AI Solutions

Creating next-generation applications powered by agentic AI requires a strategic approach with Snowflake as the backbone for data management and integration.

Snowflake’s Data Cloud allows enterprises to centralize structured, semi-structured and unstructured data from disparate sources, breaking down silos and enabling AI agents to access a single source of truth. Data scientists and engineers are able to build custom AI models and workflows directly within the Snowflake platform, streamlining the development of agentic AI applications. 

Integrating advanced AI frameworks and tools with Snowflake helps make the most of agentic AI solutions. Snowflake offers AI frameworks that can do the following:

• Create interactive web applications and dashboards for nontechnical users to interact with the agentic AI through Streamlit.
• Provide access to LLMs and AI functions through Cortex, Snowflake’s fully managed AI service.
• Coordinate and orchestrate operations across varied data sources to generate insights. Cortex Agents plan, execute and drive tasks to completion.
• Build custom AI models or integrate advanced agentic capabilities like multi-agent systems. Snowflake’s ability to support external AI frameworks like LangChain, PyTorch and TensorFlow helps in building rapid AI solutions in a democratic fashion.
• Connect Snowflake with external systems and enable serverless execution of AI-driven tasks.
• Monitor and govern AI operations to ensure compliance and transparency.
• Visualize data and AI outputs, providing awareness into agentic AI’s actions and recommendations.

Stay on the Cutting Edge of Agentic AI Applications

People also play a necessary role in the success of agentic AI’s functionality. It’s important to stay current with the latest practices and trends, so a culture of continuous learning and collaboration is vital. Agentic AI is an evolving field, and teams must stay updated on emerging tools, methodologies and best practices. Encouraging cross-functional collaboration between data scientists, engineers and business leaders ensures AI initiatives deliver measurable value. 

Agentic AI represents a bold step forward in harnessing the power of AI to drive innovation. By leveraging the Snowflake ecosystem as a foundational platform, organizations can build next-generation applications that are intelligent, autonomous and data-driven.