Authors: Mahesh Hegde - AVP of Data & Analytics, Vinothkumar Srinivasan - VP of Data & Analytics
Agentic AI: The Next Frontier in Automation
Generative AI has captured global attention with its ability to produce diverse content and insights. However, the future promises a shift from simple, knowledge-driven applications to intelligent agents capable of complex, automated actions. We are moving beyond mere content generation to a realm where AI executes multi-stage processes across digital environments.
It is predicted that 25% of enterprises using GenAI are expected to deploy AI agents in 2025, growing to 50% by 2027. The growth of AI agents—software solutions designed to complete tasks with minimal human intervention—will be fueled by innovation from both start-ups and established industry leaders identifying new revenue opportunities. Link
“When Agentic AI takes up a responsibility of finding business insights autonomously, and delivers that to business personas timely, businesses can realize true potential of data platform in decision-making”
A manufacturing firm wanted to replace their legacy collection of thousands of reports with an insights experience that is simple yet effective, complete and agile. Our answer was Microsoft Fabric + Agentic AI. While Microsoft Fabric supplied unified, centrally governed data infrastructure, Agentic AI made it easier to use the right insights for right problems for the right business users!
Designing Data infrastructure for Agentic AI applications
What does an Agentic Application need to be effective?
| Needs of agents | Microsoft Fabric design approach |
| Trustable quality data |
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| Completeness of information related to specific business process |
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| Real time data for agentic insights to be relevant to the time of action |
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| Searchability |
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| Right context |
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Agentic approach to delivering business insights for decision-making
Microsoft Copilot/ Custom Copilot
Microsoft Copilot leverages Microsoft OneLake and its data catalog within the Fabric platform to access and analyze a wide range of data sources across an organization, allowing the AI model to generate relevant responses based on a user's natural language prompts, effectively providing insights and assistance directly from the unified data pool without needing to manually locate specific datasets; essentially, OneLake acts as the central data repository that Copilot can query and draw information from, making it easier to leverage diverse data for AI-powered tasks.
To build a custom Copilot using Microsoft Fabric's OneLake and data catalog, you leverage the centralized data storage and discovery capabilities of OneLake to ingest and organize your enterprise data, then utilize the Fabric’s AI integration to train a custom Copilot model that can access and generate insights from this data using natural language prompts, effectively creating a personalized assistant for your specific business needs.
Example scenario:
A user asks Copilot, "What are the top sales performers in the last quarter?"
Copilot uses the OneLake catalog to locate the relevant sales data, then queries it to generate a list of top-performing sales representatives, providing insights based on the most current information.
Microsoft Fabric & RAG Applications
RAG combines the capabilities of retrieval-based and generation-based models. It involves using a retriever to search and retrieve relevant documents or information from a knowledge source, which is then used by a generator (LLM) to produce more accurate and contextually relevant responses.
The knowledge source can be a large corpus of documents, articles, or policies, which are converted into embeddings and stored in a vector database – a database that stores, indexes, and retrieves high-dimensional vectors.
How it works:
- A user asks a question, i.e., provides a query to the system.
- The retriever searches for and retrieves relevant documents from the vector database based on the user’s query.
- The retrieved documents are combined with the user’s query and passed to the generator (LLM)
- The generator then produces an answer based on both the retrieved information and its own knowledge with the ability to cite sources.
Microsoft Fabric is designed to handle large datasets and complex queries efficiently.
OneLake provides a single location to store and access all your data, simplifying data management for building RAG applications.
Azure AI Search integration: Index the text chunks and their corresponding embeddings within Azure AI Search, which is seamlessly accessible from Fabric.
Native integration with Azure OpenAI allows you to easily access and utilize powerful language models within your Fabric applications.
Agentic AI: Unleashing Autonomous Intelligence
By combining multiple AI capabilities, including natural language processing, reasoning, planning, and automation, AI agents are able to perform certain tasks efficiently and intelligently. At their core, these agents rely on large language models (LLMs) and other AI components to process user inputs, retrieve relevant information, and generate responses or actions. They can integrate with enterprise systems, APIs, and databases to access and analyze data, enabling them to provide contextual recommendations or automate workflows.
Beyond single-agent interactions, AI agents can also collaborate in multi-agent environments, where multiple specialized agents work together to complete complex tasks.
When we employ them to explore the rich collection of business data and come out with their key findings, decision support systems are powered by autonomous analysts that deliver critical insights to business, in time.
For example, in a business workflow, one agent might extract insights from financial reports while another validates compliance requirements, and a third generates executive summaries. This multi-agent approach enhances efficiency by distributing workloads and enabling seamless communication between AI-driven entities.
The synergy between Agentic AI and Microsoft Fabric unlocks the potential to build sophisticated, data-driven applications that go beyond traditional AI capabilities.
Agentic AI Application for Industry Use Cases:
Retail:
Dynamic Inventory Management: Agents analyze real-time sales data, supply chain information, and market trends to automatically adjust inventory levels, optimize pricing, and prevent stockouts.
Personalized Customer Experience: Agents create personalized product recommendations, targeted promotions, and automated customer service interactions based on individual customer preferences and behavior.
Healthcare:
Personalized Patient Care: Agents analyze patient medical records, genetic data, and lifestyle information to create personalized treatment plans and provide proactive health recommendations.
Automated Drug Discovery: Agents analyze vast amounts of research data, identify potential drug targets, and simulate clinical trials to accelerate the drug discovery process.
TMT (Technology, Media, and Telecommunications):
Content Creation and Curation: Agents generate personalized content recommendations, automate content creation, and curate relevant news and information for users.
Network Optimization: Agents monitor network performance, predict network outages, and automatically optimize network configurations to ensure seamless service delivery.
Manufacturing:
Predictive Maintenance: Agents analyze sensor data from manufacturing equipment to predict equipment failures and automatically schedule maintenance, reducing downtime and costs.
Supply Chain Optimization: Agents monitor supply chain disruptions, analyze demand forecasts, and automatically adjust production schedules and logistics to ensure efficient supply chain operations.
Quality Control: Agents can use visual AI to monitor production lines, and automatically remove defective products.
The convergence of Microsoft Fabric and Agentic AI represents a paradigm shift in how we interact with data. By automating complex tasks, generating proactive insights, and enabling autonomous decision-making, this powerful combination empowers organizations to unlock the full potential of their data and drive innovation.
Embrace the Future with Expert Guidance
Implementing Agentic AI within Microsoft Fabric requires careful planning and expertise. Sonata is a trusted advisor who can help you navigate this transformative journey and unlock the full potential of your data.