AI Agents Unleashed: A Comprehensive Guide for Industry Trailblazers – Part 2

February 27, 2025 Dr. Gopala Krishna Behara Agile, Applications & Technology, Artificial Intelligence, Digital Transformation, Elevating Architecture, Machine Learning, Software, Strategy & Planning, Uncategorized 0

(Editor’s Note: What follows is Part 2 of “AI Agents Unleashed: A Comprehensive Guide for Industry Trailblazers.” Part 1 appeared yesterday.)

Dr. Gopala Krishna Behara & Prasad Palli

Cutting-Edge Technologies in AI Agent Systems

AI agent systems are revolutionized by several advanced technologies that enable them to perform complex tasks with remarkable efficiency and intelligence.  These cutting-edge technologies include:

  • Machine Learning: AI Agent systems rely on machine learning algorithms to learn from data and improve their performance over time.
  • Deep Learning: AI Agent systems involve training artificial neural networks on large datasets. It performs tasks like image recognition, natural language processing, and speech recognition.
  • Computer Vision: AI Agent systems involve in processing visual data to identify and locate objects, understand scenes, and track motion.
  • Natural Language Processing (NLP): AI Systems perform Text Analysis, Speech Recognition and language generation.
  • Robotics: AI agents perform repetitive and rule-based tasks such as data entry, reducing manual effort and errors.
  • Multi-Agent Systems: Manages interactions and cooperation among multiple AI agents. It divides complex problems into smaller, manageable tasks for agents to solve collaboratively.
  • Big Data Technologies: Utilizes distributed storage systems to handle large volumes of data. Ensures fast and efficient access to stored data.
  • Blockchain: Provides secure, transparent, and tamper-proof record-keeping for transactions and data exchanges. Facilitates decentralized trust and verification.
  • IoT: Connects devices to AI Agents for continuous data collection and communication. AI agents manage and control IoT devices, ensuring seamless data flow and integration.
  • Edge Computing: Processes data at the edge of the network, closer to the data source, to reduce latency and improve response times Enables AI agents to make timely decisions based on real-time data.
  • Cloud Computing: Provides scalable computing resources and storage on-demand. Ensures AI agents can access data and applications from anywhere, facilitating remote monitoring and analysis.
  • Cybersecurity: Protects AI systems and data from unauthorized access, breaches, and cyber threats. AI agents monitor for security threats and anomalies, protecting systems from cyber-attacks.

These technologies enable them to function autonomously, efficiently, and intelligently across various domains.

AI Agent Reference Architecture

The following Figure shows contextual architecture of AI Agent with key components and layers.

The various components of AI Agent architecture are classified as,

  • Input Data Sources
  • AI Agents
  • Large Language Models
  • Tools Integration
  • Memory
  • AI Orchestration
  • Data Repository
  • Output
  • Governance

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                       Figure 3 AI Agent Reference Architecture

Input Data Sources: The data sources provide the insight required to solve business problems. The data sources are structured, semi-structured, and unstructured, and they come from many sources covering user interactions, real time data streams, multi model data covering Images, Text, Video, Audio etc.

AI Agents: AI Agents process input data coming from various data sources.  The data is formatted in a way that it is interpretable by the LLM. AI Agent analyzes the response and decides on delivering the response directly to the user or use specific tools to perform additional actions based on LLM’s output. If the output involves data retrieving the agent makes API calls, process data and convert it into coherent response.

Large Language Models: LLMs are a type of AI system trained on a large amount of text data that can understand natural language and generate human like responses. The processed input is fed into the LLM, which generates a response based on its training.

Tool Integration: It enables connecting the agent with external applications, databases, Automation tools to extend its functionality. Key aspects of tools integration include

  • API Integration: Agents to communicate with other software systems
  • Third Party Integration: Agents to integrate with NLP systems, ML Models to enhance its capabilities
  • Automation Tools Integration: AI Agents can automate repetitive tasks to increase efficiency.

Memory: AI Agents remember previous interactions, user preferences, on going tasks to provide personalized and effective user experience. It helps AI Agents to consider data storage and retrieval mechanisms. Key memory management characteristics include Scalability, Privacy, Consistency and Adaptability.

AI Orchestration: AI Orchestration involves managing the coordination and interaction between multiple AI agents to achieve specific goals or tasks. The key components of AI Orchestration are,

  • Adaptive Task Management:
    • System dynamically assigns and reassigns tasks to AI agents based on their capabilities, availability, and current workload
    • Ensures tasks are executed efficiently and effectively by adapting to changing conditions and requirements
  • Multi-Agent Collaboration:
    • AI agents work together, sharing information and resources to achieve common goals.
    • Seamless communication and coordination among agents
  • Performance Monitoring:
    • System continuously monitors the performance of AI agents to ensure they are meeting the desired outcomes and standards.
    • Tracking various metrics such as accuracy, efficiency, and response time to identify areas for improvement and optimize the overall performance of the system.

By orchestrating these components, AI systems can function more cohesively and effectively, leading to better outcomes and more intelligent decision-making processes.

Data Repository: Comprehensive data covering both structured and unstructured data sources are defined as part of repository. It facilitates efficient data management by utilizing both centralized and distributed repositories, employing vector stores for quick information retrieval, and leveraging knowledge graphs for contextual reasoning. Also, the data is categorized and organized so that it can be used by AI Agent and models.

Output: It consists of AI insights that are transformed into personalized, context-aware results. These results are continuously updated. The system’s knowledge base is also refreshed in the process.

Governance: The AI architecture integrates essential governance and safeguards to ensure safety, compliance, and ethical AI deployment. It ensures AI agents operate safely, securely, and within regulatory boundaries.

Leading Cloud Providers and Their AI Agent Services

The prediction that 2025 will be the Year of AI Agents is gaining traction among industry experts and analysts. They foresee AI Agent services offered by top cloud providers transforming various industries. AI Agents Features Comparison between Microsoft, Google & Amazon Web Services are depicted below,

Feature Microsoft Google Amazon Web Services
AI Agent Service Azure OpenAI Service, Copilot and Azure AI Agent Service

 

 Google’s Gemini 2.0, Project Jarvis

 

 Amazon Bedrock platform for agentic AI orchestration

 
Supported LLM Models GPT-3, GPT-4, Codex PaLM 2, Imagen, Gemini GPT-2, GPT-3, Jurassic-2, Claude, Llama, Titan
Tasks performed Data analysis, code generation, and task automation Content generation, data analysis, and code assistance Customer service, virtual assistance, and content generation
Integration of AI Agent Seamless integration with Microsoft products like Office 365, Dynamics 365, and Teams Integration with Google services like Google Workspace, BigQuery, and Google Cloud Storage

 

 Integration with AWS services like S3, DynamoDB, Lambda and SageMaker

 
Persistent Threads Store message history and automatically handle truncation when the conversation exceeds the model’s context length Offer persistent memory capabilities that enable AI systems to retain and recall information over extended periods using Google Agent space Support persistent resources and capacity assurance for model training using Amazon SageMaker
Tool Access Agents can access multiple tools in parallel, such as code interpreters and custom-built tools Agents can perform multi-step processes autonomously or semi-autonomously, integrating with various data sources and tools Agents can handle advanced machine learning workflows, including distributed training jobs and data caching
Customization Custom models and fine-tuning Pre-trained models and customization Custom models and fine-tuning
Security and Certifications Data Encryption, Azure Active Directory (AAD) , Azure Security Center, Azure Monitor , Azure Log Analytics, ISO/IEC 27001, SOC 2, and NIST

 

 Data Encryption, Google IAM , Google Cloud Security Command Center, Google Cloud Armor, Google Cloud Monitoring , Google Cloud Logging, ISO/IEC 27001, SOC 2, and PCI-DSS

 

 Data Encryption, AWS IAM, AWS Security Hub, Amazon GuardDuty, AWS CloudWatch and AWS CloudTrail, ISO/IEC 27001, SOC 2, and PCI-DSS

 
Compliance Supports

HIPAA Compliance, GDPR Compliance, Regulatory Compliance, Audit and Reporting, Data Residency

 Supports

HIPAA Compliance, GDPR Compliance, Regulatory Compliance, Audit and Reporting, Data Residency

 Supports

HIPAA Compliance, GDPR Compliance, Regulatory Compliance, Audit and Reporting, Data Residency
Managed Services Azure OpenAI, Machine Learning

 

 Vertex AI, Pre-trained Models

 

 Bedrock, SageMaker

 
Usecases Customer service bots, predictive analytics, and automated workflows Natural language processing, image recognition, and data analysis Predictive analytics, customer service automation, and data processing
Pricing Model Consumption-based Pay-as-you-go Pay-as-you-go

Real world Use cases – AI Agents in Healthcare

AI Agents use cases are endless, and they are evolving continuously. Businesses across industry are experimenting with different ways to incorporate AI Agent. Also, there is a high demand for increased efficiency and improved decision-making capabilities across industries. The AI Agent applications improve automation, reduce costs and increase revenues for the enterprises.

The future of AI Agents in healthcare is incredibly promising, with numerous advancements and innovations on the horizon. Some of the key use cases are,

Enhanced Diagnostics and Imaging

AI Agents based systems help in providing more accurate and timely analysis of medical images. These systems help in detecting early signs of chronic diseases like cancer with greater precision than human radiologists. Continuous learning and adaptation will further improve diagnostic accuracy over time.

Personalized Medicine

Systems based on AI Agents helps in analyzing patient data, genetic information to generate tailored treatment plans. This approach ensures that patients receive treatments that are specifically suited to their unique needs, improving efficacy and reducing side effects.

Predictive Analytics and Patient Monitoring

AI agents help in enhancing patient monitoring by predicting potential health issues before they become critical. This allows for early interventions and better management of chronic conditions, ultimately improving patient care and reducing hospital readmissions.

Streamlined Administrative Operations

Systems based on AI Agents automate routine administrative tasks, such as managing patient records, scheduling appointments, and handling billing processes. This reduces the administrative burden on healthcare professionals, allowing them to focus more on patient care.

Clinical Decision Support

AI agents help in assisting healthcare providers in making informed clinical decisions by analyzing vast amounts of data and providing evidence-based recommendations. This will improve the quality of care and patient outcomes.

Remote Patient Monitoring and Telemedicine

AI Agents along with IoT devices and wearable technology enable continuous monitoring of patients’ health metrics in real-time. This allows healthcare providers to detect issues early and provide timely interventions, even outside of traditional clinical settings.

Operational Efficiency

AI agents based systems help in optimizing hospital operations, from scheduling and resource allocation to inventory management. This helps reduce costs and improve the overall efficiency of healthcare delivery.

Automated Claims Processing

AI agent-based systems help in automating the claims processing operations. AI agents review the claims, perform the fraud detection, policy compliance check, approvals and reimbursement. AI agents use feedback from the manual review process to continuously improve their algorithms and decision-making capabilities. AI agents enhance the efficiency, accuracy, and overall effectiveness of the claims processing workflow in a healthcare insurance setting.

The following is the depiction of the usage of AI Agent for the Healthcare domains,

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             Figure 4: AI Agent for Healthcare Domains

In summary, AI agent enhances the efficiency, accuracy, and overall effectiveness of the claims processing workflow in a healthcare insurance setting.

Limitations of AI Agents

Some of the limitations of the usage of AI Agents today are,

Lack of Context Understanding: Sometimes AI Agents lack a deep understanding of context. This can lead to misinterpretations, incorrect decisions or inappropriate actions. For example: AI agent’s inability to fully understand the context and traces of the patient’s situation could lead to a misdiagnosis, potentially delaying appropriate treatment and affecting patient outcomes.

Poor Data Quality and Quantity: For Learning and Operate, AI Agents require a huge amount of accurate data such that they can generate accurate output. Poor, inaccurate, biased and incomplete data leads to inaccurate output. For example: If the Electronic Health Records (HER) data is incomplete, outdated, or contains errors, the AI agent’s predictions can be significantly impacted. If a patient’s medical history is missing critical information about past surgeries or chronic conditions, the AI agent might fail to accurately assess the patient’s risk of complications during a new treatment.

Lack of Multi-domain Adoption: Most of the AI Agents are specialized in specific tasks related to specific domain. Extending these Agents to multiple domains might require additional functionality, programming and lot of retraining. For Example: The AI model trained primarily on mammograms (breast x-rays) to detect breast cancer that cannot be applied to other types of medical imaging such as CT scans of the lungs and MRI scans of the brain.

Poor Prompt Response: A significant challenge in AI agents is ensuring compatibility when integrating new tools or APIs, as this often necessitates revising existing prompts. Poorly structured prompts can lead to ambiguous or incorrect responses and managing prompt changes across different versions of the agent can become a complex and burdensome task. For Example: A hospital integrating a new API for genetic testing service with their existing AI diagnostic system covering EHR, Lab test database and medical imaging software faces the challenge of revising prompts to ensure compatibility. Poorly structured prompts can lead to incorrect diagnostics, affecting patient care.

Adversarial Attacks: Feeding Malicious Inputs to the AI Agents lead to incorrect predictions and incorrect decisions.  For Example: A malicious actor modifies a lung X-ray image by introducing subtle pixel-level perturbations. AI agent misinterprets the altered image and fails to detect a malignant tumor, which it would have otherwise identified in the original image. This misdiagnosis can lead to a delay in the patient’s treatment, potentially causing serious harm.

High Resource Cost: High computational and resource costs are associated with running advanced AI agents. For Example: AI agent uses deep learning algorithms for analyzing medical images like MRIs and CT scans. This requires high computational power and huge data storage. This leads to higher infrastructure cost, increased energy consumption, ongoing maintenance, periodic upgrades and higher scalability. Hence the design of sophisticated AI agents is required which operate effectively.

Ethical Concerns: sometimes AI Agents come across ongoing ethical and privacy concerns regarding data usage and decision-making transparency. For Example: Using patient data for purposes beyond the actual usage, such as for research or commercial gain. It’s important to establish clear guidelines and ensure that data usage aligns with ethical standards.

Difficulty with Long Term Planning: Reactive AI Agents are confined to making short-term decisions, lack of memory and focused on fixed responses. They find it challenging to handle complex, multi-step tasks that demand long-term strategic planning or adaptation over extended periods. For example: AI agents might not consider the long-term treatment plans of patients with chronic conditions who require regular follow-up appointments. AI agents could inadvertently schedule appointments in a way that disrupts the continuity of care, leading to gaps in treatment and potentially worsening patient outcomes.

Conclusion

Many people perceive AI Agents as merely enhanced chatbots. They represent the future of the Digital Workforce.

The use of AI Agents across enterprises is becoming more and more widespread, possibly even trending toward industrialization.

Understand AI Agent fundamentals to identify business use cases. Develop a strategy for data and AI across the enterprise. Identify the highest value of use cases requiring LLMs.

AI agents provide superior flexibility, intelligence, and efficiency over traditional software. They can autonomously execute tasks, adapt to new situations, and deliver more personalized interactions, making them an invaluable asset for contemporary businesses.  They can,

  • Continuously observe and evaluate market conditions
  • Formulate and test strategic hypotheses
  • Adjust recommendations based on real-time feedback
  • Integrate various strategic frameworks concurrently

Key Benefits of AI Agents are,

  • Automate repetitive tasks to save time and manual work
  • Analyze data and identify patterns quickly
  • Operate 24/7 with no downtime
  • Scale applications and adapt to increased demand
  • Maintain consistent performance

Train the people to promote AI Agents driven initiatives. Consider reskilling and upskilling employees to work with AI Agents effectively. Address and stay informed about emerging ethical guidelines and regulations related to AI.

Acknowledgements

The authors would like to thank Tanay Srivastava, Director, Tricon Solution LLC for giving the required time and support in many ways in bringing up this Comprehensive Guide as part of Technical Services efforts.

About Authors

Dr. Gopala Krishna Behara is an Enterprise Architect at Tricon Solutions LLC.  He has around 28 years of IT experience. He can be reached at gopalakrishna.behara@triconitsolutions.com.

Prasad Palli is Principal Enterprise Architect at Albertsons Companies. He has around 25+ years of IT experience. He can be reached at palli_prasad@hotmail.com

Disclaimer

The views expressed in this article/presentation are those of authors and Tricon Solutions LLC & Albertsons does not subscribe to the substance, veracity or truthfulness of the said opinion.