Agentic AI and Its Adverse Usage in Modern Solutions

7 min readDec 17, 2024

Developing Modern Solutions Using Agentic AI with Microsoft Azure and Microsoft Autogen

Dr. Magesh Kasthuri and Sourav Kamila, Wipro Limited

Introduction

In the rapidly evolving landscape of technology, 2025 stands as a landmark year for the advancements in Agentic AI. This innovative paradigm involves multiple AI agents working collaboratively to solve complex problems, offering unprecedented levels of efficiency, scalability, and robustness.

Agentic AI encompasses specialized agents, each tasked with handling specific functions. These agents communicate and coordinate with each other, achieving a collective goal that surpasses the capabilities of individual AI systems. Leveraging the strengths of each agent, this pattern is particularly effective for tasks requiring diverse expertise and parallel processing.

Industrial Applications and Trends

Healthcare

In the healthcare sector, Agentic AI is revolutionizing patient care. By integrating AI agents that specialize in diagnosis, treatment recommendations, and patient monitoring, the healthcare industry is witnessing enhanced patient care coordination. Microsoft Azure’s robust cloud capabilities and Microsoft Autogen’s powerful AI frameworks are instrumental in developing intelligent applications that automate administrative tasks and analyze patient data for personalized treatment plans. However, it is paramount to address concerns such as misdiagnosis and data security to ensure the reliable deployment of these technologies.

Intelligent App Solutions for Hyper Healthcare

In the healthcare sector, the collaboration of AI agents can revolutionize patient care, making it more seamless and efficient. By leveraging Microsoft Azure’s robust cloud capabilities and Microsoft Autogen’s powerful AI frameworks, we can develop intelligent applications that enhance patient care coordination. AI agents can ensure seamless communication and treatment across different healthcare providers, automate administrative tasks, and analyze patient data to offer personalized treatment plans. Agentic AI is revolutionizing healthcare through diagnostics, treatment planning, and patient monitoring. However, its misuse entails:

· Misdiagnosis: Incorrect AI-driven diagnoses can jeopardize patient health.

· Data Security: Sensitive patient data could be at risk if AI systems are not properly secured.

Architectural Insights

The architecture of such a solution involves a multi-layered approach:

· Data Ingestion Layer: Collects and processes data from various healthcare providers, IoT devices, and patient records.

· AI Agent Layer: Multiple AI agents, each specialized in different aspects of healthcare, such as diagnosis, treatment recommendation, and patient monitoring.

· Integration Layer: Ensures seamless integration with existing healthcare systems, electronic health records (EHR), and communication platforms.

· User Interaction Layer: Provides an intuitive interface for healthcare professionals and patients to interact with the AI agents and receive real-time insights and recommendations.

Banking

In banking, AI agents collaborate efficiently in loan processing, from risk assessment to application approval. This multiagent approach accelerates decision-making processes, reduces manual workload, and enhances the accuracy of financial assessments. As a result, banks can provide faster and more reliable services to their customers.

In the banking industry, AI agents can work collaboratively to streamline loan processing and assess risk more accurately. By utilizing Microsoft Azure and Autogen, banks can develop a multiagent system where AI agents handle various tasks such as credit scoring, fraud detection, and customer support, ensuring that loan applications are processed swiftly and securely. Agentic AI is transforming the banking industry by enhancing customer service, fraud detection, and risk management. However, its misuse poses significant risks:

· Fraudulent Transactions: AI systems can be exploited to execute fraudulent transactions, bypassing traditional security measures.

· Algorithmic Bias: AI algorithms, if not properly monitored, may develop biases that lead to discriminatory lending practices.

Architectural Insights

The architecture for banking solutions includes:

· Data Aggregation Layer: Gathers financial data from multiple sources, including customer history, credit bureaus, and transaction records.

· AI Analysis Layer: AI agents perform tasks like credit scoring, risk assessment, and fraud detection using machine learning models.

· Decision Support Layer: Provides recommendations and automated decision-making processes for loan approvals and customer interactions.

· Compliance Layer: Ensures all AI activities comply with regulatory standards and ethical guidelines.

Agentic AI in Insurance

Advanced Claims Processing and Customer Service

In the insurance domain, AI agents can enhance claims processing, underwriting, and customer service. Microsoft Azure’s scalability combined with Autogen’s AI capabilities allows insurance companies to deploy intelligent solutions where AI agents assess claims, detect fraudulent activities, and provide personalized customer support. In insurance, Agentic AI is used for underwriting, claims processing, and customer service. Adverse uses include:

· Privacy Invasion: AI systems can excessively monitor policyholders, leading to privacy violations.

· Denial of Claims: Automated decision-making can result in unjust denial of legitimate claims.

Architectural Insights

The architecture for insurance AI solutions involves:

· Data Collection Layer: Aggregates customer data, policy information, and claim records.

· AI Processing Layer: AI agents analyze data for claims assessment, fraud detection, and risk modeling.

· Service Interaction Layer: Facilitates communication between AI agents and customers, offering personalized support and real-time updates.

· Integration Layer: Ensures seamless connectivity with existing insurance management systems and third-party services.

Retail

Retail giants are increasingly relying on AI-driven demand forecasting. AI agents analyze sales data, market trends, and consumer behavior to optimize inventory and promotions. This strategy leads to better stock management and increased sales, positioning retailers to respond swiftly to market dynamics.

For the retail sector, AI agents can transform customer experiences by providing personalized shopping recommendations, managing inventory efficiently, and optimizing supply chains. Using Azure and Autogen, retailers can create a multiagent ecosystem where AI agents analyze customer preferences, predict demand, and automate logistics. Retailers leverage Agentic AI for personalized marketing, inventory management, and customer service. Potential adverse effects are:

· Data Misuse: AI systems might misuse consumer data for targeted advertising, compromising privacy.

· Job Displacement: AI-driven automation can lead to significant job losses in the retail sector.

Architectural Insights

The architecture for retail solutions includes:

· Customer Data Layer: Collects and processes data from various touchpoints, including online stores, physical outlets, and social media.

· Recommendation Engine Layer: AI agents generate personalized product recommendations based on customer behavior and preferences.

· Inventory Management Layer: AI agents predict demand, manage stock levels, and optimize supply chain operations.

· Customer Interaction Layer: Provides a seamless interface for customers to interact with AI agents across multiple channels, including websites, mobile apps, and in-store kiosks.

Manufacturing

In manufacturing, AI systems are optimizing production schedules and maintenance activities. By analyzing equipment performance and production data, these AI agents minimize downtime and maximize operational efficiency. This intelligent orchestration of resources is pivotal in maintaining the competitiveness of manufacturing firms.

Architectural Insights

The architecture of Agentic AI solutions involves a multi-layered approach:

· Data Ingestion Layer: Collects and processes data from various sources, including IoT devices and records.

· AI Agent Layer: Consists of multiple AI agents, each specialized in different aspects of the target domain.

· Integration Layer: Ensures seamless integration with existing systems and communication platforms.

Trends in Agentic AI

According to analyst reports from Gartner, Forrester, IDC, and Everest, several trends are shaping the future of Agentic AI:

· Increased Adoption: There is a growing adoption of Agentic AI across various industries, driven by the need for automation and efficiency.

· Enhanced Ethical Standards: Companies are increasingly focusing on ethical AI practices to mitigate risks associated with bias and data privacy.

· Integration with Emerging Technologies: Agentic AI is being integrated with technologies like blockchain, IoT, and edge computing to enhance its capabilities and applications.

· Focus on Explainability: There is a heightened emphasis on making AI systems more transparent and explainable to build trust with users.

MS Autogen Agentic framework

Traditional RAG VS Agentic RAG

Traditional RAG (Static, Reliable and Limited)-It is great for straightforward information retrieval. It works by pulling data from a set of predefined queries and then generating a response based on that. But it lacks adaptability and reasoning where it struggles with complex multistep tasks.

Agentic RAG (Dynamic Intelligent and Powerful)-It takes a RAG step further by incorporating an AI agent that adapts to the context in real time. Think of it as a more proactive and reasoning system. It doesn’t just fetch data , it understand your query and actively guide the process and solving complex problems with ease.

Conclusion

The future of Agentic AI is bright and full of potential. With the continuous advancements in AI technologies and the integration with emerging technologies like blockchain, IoT, and edge computing, Agentic AI solutions will become more sophisticated, efficient, and secure. The focus on explainability and ethical AI practices will ensure that these systems are transparent, trustworthy, and beneficial for society.

In conclusion, developing modern solutions using Agentic AI on the Microsoft Azure platform with Microsoft Autogen opens up new horizons for various industries. By harnessing the power of multiple AI agents, organizations can achieve unprecedented levels of automation, efficiency, and customer satisfaction. The architectural insights provided here are just the beginning of what is possible, and as technology evolves, so too will the capabilities and applications of Agentic AI.

As we advance further into 2025, Agentic AI continues to shape the future of technology. Its ability to combine the expertise of multiple agents to tackle sophisticated tasks makes it a cornerstone of intelligent, adaptive systems. Companies across various sectors are increasingly adopting this approach to stay ahead of the curve, driving innovation and operational excellence.

In conclusion, Agentic AI is not just a trend but a transformative force in new age technology. It exemplifies the synergy of collaborative AI agents, setting a precedent for the future of intelligent systems in 2025 and beyond.