Rohit Bhardwaj

Autonomous Agents Enterprise Architecture 4.0 - RAG and GraphRAG - Full Day

Enterprise Architecture Harnessing Retrieval-Augmented Generation for Intelligent Decision-Making

9:00 AM MDT

This interactive, hands-on workshop is designed for software developers and architects eager to explore cutting-edge AI technologies. We’ll delve deep into Retrieval-Augmented Generation (RAG) and GraphRAG, equipping participants with the knowledge and skills to build autonomous agents capable of intelligent reasoning, dynamic data retrieval, and real-time decision-making.

Through practical exercises, real-world use cases, and collaborative discussions, you’ll learn how to create applications that leverage external knowledge sources and relational data structures. By the end of the day, you’ll have a solid understanding of RAG and GraphRAG and the ability to integrate these methodologies into production-ready autonomous agents.

In this interactive workshop, participants will delve into the foundational concepts of RAG and GraphRAG, exploring how these technologies can be utilized to develop autonomous agents capable of intelligent reasoning and dynamic data retrieval. The workshop will cover essential topics such as data ingestion, embedding techniques, and the integration of graph databases with generative AI models.
Attendees will engage in practical exercises that involve setting up RAG pipelines, utilizing vector databases for efficient information retrieval, and implementing GraphRAG workflows to enhance the capabilities of their applications. By the end of the workshop, participants will have a comprehensive understanding of how to harness these advanced methodologies to build robust autonomous agents tailored to their specific use cases.

Session 1: Introduction to Autonomous Agents and Their Applications
Explore the evolution and role of autonomous agents in modern software systems.
Understand how these agents interact with external knowledge sources to make decisions.
Discuss real-world applications in industries like healthcare, finance, and e-commerce.

Session 2: Overview of Retrieval-Augmented Generation (RAG)
Understanding RAG architecture: How RAG combines external knowledge retrieval with generative models.
Explore the core components of RAG, including:
Embedding generation
Vector similarity search
Context-enhanced generation
Use cases and benefits:
Building chatbots with domain-specific knowledge.
Dynamic, context-aware content generation.
Decision-making in complex systems.

Session 3: Introduction to GraphRAG
What is GraphRAG?: Leverage graph-based relational knowledge for enhanced AI capabilities.
Key concepts:
Graph-based indexing techniques.
Using graph databases like Neo4j to store and retrieve relational data.
Advantages:
Improved accuracy through relationship-based retrieval.
Handling complex queries with structured graph connections.
Real-world applications:
Fraud detection.
Knowledge graphs for personalized recommendations.

Session 4: Hands-On Lab 1 — Setting Up a Basic RAG Pipeline
Data preparation and ingestion:
Formatting datasets for vector search and embedding generation.
Utilizing pre-trained models (e.g., BERT, OpenAI embeddings) for RAG pipelines.
Implementation:
Create a simple RAG application in Python using libraries like LangChain and Hugging Face.
Integrate with vector databases such as Pinecone or Weaviate for fast retrieval.

Session 5: Advanced Techniques in RAG and GraphRAG
Self-reflective and adaptive RAG:
Implement feedback loops for improving retrieval and generation quality.
Integrating graph databases:
Learn how to connect graph databases (e.g., Neo4j) with RAG pipelines.
Explore advanced retrieval techniques combining vectors and graphs.
Optimizing autonomous agents:
Strategies for scaling knowledge retrieval.
Balancing generation quality and computational efficiency.

Session 6: Hands-On Lab 2 — Building an Autonomous Agent with GraphRAG
Developing an end-to-end application:
Incorporate relational knowledge from graphs.
Build intelligent agents that retrieve, infer, and respond autonomously.
Key workflows:
Embedding generation for contextual awareness.
Graph-based queries to handle relational knowledge.
Use case examples: domain-specific chatbots, recommendation systems, or predictive analytics.

Session 7: Monitoring and Evaluating Autonomous Agents in Production
Best practices for deployment:
Optimize RAG and GraphRAG agents for real-world scenarios.
Ensure reliability through robust infrastructure and error handling.
Monitoring tools:
Using tools like Prometheus, Grafana, and AWS CloudWatch to monitor system performance.
Metrics for evaluating decision quality and system responsiveness.

Session 8: Group Discussion — Future Trends in Autonomous Agents and AI Technologies
Predict how autonomous agents will evolve with advancements in generative AI.
Discuss emerging technologies:
Multi-modal RAG for processing images, text, and audio.
Real-time graph updates for dynamic knowledge retrieval.
Share ideas and insights with fellow participants to foster innovation.

Enterprise Architecture 4.0: The AI-Driven Future Preview

Designing the Intelligent Enterprise with Generative AI, Agents, and Next-Gen Technologies

8:30 AM MDT

As digital ecosystems evolve at breakneck speed, enterprises must reimagine their architectural blueprints—not as static diagrams, but as adaptive, intelligence-driven systems. This talk offers a compelling preview of Enterprise Architecture 4.0, weaving together cutting-edge technologies, strategic foresight, and practical frameworks to prepare architects and technology leaders for the next wave of transformation.

We begin by exploring how Generative AI, Assistive Agents, Predictive Analytics, and Copilot GPTs can be embedded into modern architectures—turning traditional systems into living, learning ecosystems. Attendees will gain foundational knowledge of RAG (Retrieval-Augmented Generation), vector databases, and graph databases—critical for context-aware reasoning, personalization, and intelligent data processing.

Then, using the Gartner Emerging Tech Impact Radar as our compass, we explore the innovations reshaping enterprise software over the next 3–5 years:

Emerging Technologies Shaping the Future
Generative AI & GPT Agents: Beyond code generation—autonomous reasoning, AI coaching, and domain-specific copilots.

Vector & Graph Databases: Powering search, personalization, and relationship-aware AI.

Augmented & Virtual Reality (AR/VR): Enhancing training, field ops, and immersive collaboration.

Edge Computing: Enabling low-latency intelligence and decentralized decision-making at the enterprise edge.

AI/ML at Scale: Mitigating bias, enforcing AI ethics, and integrating ML into business operations.

Blockchain: Ensuring data integrity, supply chain transparency, and smart contract automation.

Autonomous Agents & Advanced Automation: Automating complex workflows through intelligent, multi-modal agents.

We highlight how Enterprise Architecture becomes the navigation system for these innovations—ensuring alignment with business strategy, reducing technical debt, and unlocking agility and resilience.

Key Takeaways & Outcomes
By the end of this session, attendees will:

Understand the strategic shift toward Enterprise Architecture 4.0 and why it’s essential—not optional.

Gain a preview of patterns, principles, and tools to be explored in the 2-day workshop, including AI-first frameworks and modular architectures.

Learn how to integrate emerging technologies like GPT agents, vector search, blockchain, and AR/VR into a unified roadmap.

Discover how to elevate the EA function—from compliance-oriented governance to future-shaping innovation leadership.

Whether you’re preparing for next-gen AI systems, scaling automation, or future-proofing your enterprise strategy, this talk provides the preview and perspective you need to lead with confidence into the AI-driven future.

Graph Mastery to AI: : Unleashing the Power of Connections

From Knowledge Graphs to Graph-RAG: Supercharging AI and Innovation with Graph Technologies

10:30 AM MDT

Graph technology has emerged as the fastest-growing sector in database systems over the past decade—and now, it's at the heart of AI transformation. This talk explores the strategic imperative of mastering graph technologies for professionals designing intelligent systems, optimizing codebases, and architecting future-ready enterprises.

Mastering graph databases, knowledge graphs, and advanced algorithms is no longer a niche skill—it's foundational to enabling AI use cases, powering semantic search, driving recommendation engines, and orchestrating Retrieval-Augmented Generation (RAG) with high precision.
In this comprehensive session, we'll explore high-level graph algorithms that form the backbone of modern, complex systems and discuss how these algorithms are integral to the architecture of efficient graph databases. We will delve into the advanced functionalities and strategic implementations of knowledge graphs, illustrating their essential role in integrating disparate data sources, empowering AI applications including generative AI, and enhancing business intelligence.

Join us to navigate the complexities and opportunities this dynamic field presents, ensuring you remain at the cutting edge of technology and continue to drive significant advancements in your projects and enterprises.

What You’ll Learn:
Advanced Graph Algorithms
Concise review of key graph theory concepts tailored for AI and data engineers.

Application of algorithms like Greedy, Dijkstra's, Bellman-Ford, and PageRank for real-world graph optimization, pathfinding, and influence modeling.

Graph Database Architecture
Comparison of graph vs. relational models for large-scale, interconnected data.

Best practices in data modeling, indexing, and query performance tuning in platforms like Neo4j, TigerGraph, and Amazon Neptune.

Mastery of Knowledge Graphs
How to build and scale enterprise-grade knowledge graphs for semantic search, personalization, and intelligent recommendations.

Role of ontologies, entities, and relationships in structuring organizational knowledge.

Graph-RAG and AI-Enhanced Use Cases
Deep dive into Graph-RAG (Graph-enhanced Retrieval-Augmented Generation): combining structured knowledge graphs with unstructured retrieval to power trustworthy, explainable generative AI.

Use cases:

Domain-specific copilots with traceable knowledge lineage.

AI assistants that reason over connected knowledge.

Compliance-aware search and recommendations.

Customer 360 + Agent 360 views for enterprise workflows.

Case Studies and Future Technologies
Real-world case studies of graph adoption in healthcare, finance, e-commerce, and public sector AI.

Preview of emerging trends:

Graph Neural Networks (GNNs)

Hybrid vector–graph databases

Multimodal reasoning over structured + unstructured data

Outcomes & Takeaways:
By the end of this session, you will:

Understand why graph mastery is foundational for AI and system innovation.

Learn to architect performant, scalable graph systems for enterprise use.

See how Graph-RAG bridges structured knowledge and LLMs to deliver smarter AI assistants.

Be equipped to apply graph technologies to drive innovation, efficiency, and AI trustworthiness in your own organization.

Revolutionizing Design: ChatGPT's Role in Next-Generation Software Architecture

1:00 PM MDT

With advanced AI tools, software architects can enhance their project design, compliance adherence, and overall workflow efficiency. Join Rohit Bhardwaj, an expert in generative AI, for a session that delves into the integration of ChatGPT, a cutting-edge generative AI model, into the realm of software architecture. The session aims to provide attendees with hands-on experience in prompt engineering for architectural tasks and optimizing requirement analysis using ChatGPT. It is a compelling talk explicitly designed for software architects who are interested in leveraging generative AI to improve their work.

Outline:
Introduction

A brief overview of the session.
Importance of generative AI in software architecture.
Introduction to ChatGPT and its relevance for software architects.

Prompt Engineering for Architectural Tasks

Crafting Effective Prompts for ChatGPT
Strategies for creating precise and effective prompts.
Examples of architectural prompts and their impact.
Hands-On Exercise: Creating Architectural Prompts
Interactive session: Participants will craft and test their prompts.
Feedback and discussion on prompt effectiveness.

Optimizing Requirement Analysis

Leveraging ChatGPT for Requirement Analysis and Design
Integration of AI in empathizing with client needs and journey mapping.
Cost Estimations, Compliance, Security, and Performance
Selecting appropriate technologies and patterns with AI assistance
Hands-On Exercise: Requirement Analysis and Design
Case Study
Using Empathy Map and Customer Journey Map tools in conjunction with AI.
Case Study Cost Estimations, Compliance, Security, and Performance

Custom GPTs, Embeddings, Agents

Key Takeaways:
Enhanced understanding of how generative AI can be used in software architecture.
Practical skills in prompt engineering tailored for architectural tasks.
Strategies for effectively integrating ChatGPT into requirement analysis processes.

Modernizing Legacy Systems: AI-Enhanced Cloud Adoption Frameworks

Achieving Innovation, Trade-offs, and Scalability in Modern Development Using LLMs like ChatGPT and Enterprise-Grade AI.

3:00 PM MDT

“By 2030, 80 percent of heritage financial services firms will go out of business, become commoditized, or exist only formally but not competing effectively”, predicts Gartner.

This session explores the integration of AI, specifically ChatGPT, into cloud adoption frameworks to modernize legacy systems. Learn how to leverage AWS Cloud Adoption Framework (CAF) 3.0, Microsoft Cloud Adoption Framework for Azure, and Google Cloud Adoption Framework to build cloud-native architectures that maximize scalability, flexibility, and security. Designed for architects, technical leads, and senior IT professionals, this talk provides actionable insights and strategies for successful digital transformation.

Attendees will learn how to:

Integrate AI assistants into cloud readiness, migration, and optimization phases.

Use AI to analyze legacy code, auto-generate documentation, and map dependencies.

Employ the AWS CAF 3.0, Microsoft CAF, and Google CAF to guide large-scale migration while balancing security, compliance, and cost.

Design cloud-native architectures powered by continuous learning, resilience, and automation.

Packed with case studies, modernization blueprints, and AI-assisted workflows, this session equips architects and technical leaders to bridge the gap between heritage systems and future-ready enterprises.

Agenda (60–90 minutes)
1 Introduction: Why Legacy Modernization Now (10 min)

The Gartner 2030 prediction and what it means for enterprises.

The rise of AI-augmented modernization.

2 Understanding Cloud Adoption Frameworks (15 min)

Overview of AWS CAF 3.0, Microsoft CAF for Azure, Google CAF.

Common pillars: strategy, governance, people, platform, security, and operations.

Strengths and trade-offs across frameworks.

3 Strategic Role of AI in Legacy Modernization (15 min)

How LLMs augment discovery, documentation, and refactoring.

ChatGPT as a legacy analysis assistant: reading COBOL, PL/SQL, Java monoliths.

AI-driven dependency mapping, test case generation, and modernization playbooks.

4 Steps for Moving Legacy Systems to the Cloud (20 min)

Assessment → Migration Planning → Modernization Execution → Optimization.

Incremental vs. Full Rewrite: decision matrix and hybrid models.

Ensuring compliance, resilience, and audit readiness throughout migration.

5 Designing AI-Ready Cloud-Native Architectures (15 min)

Embedding RAG, microservices, and event-driven architecture.

Leveraging container orchestration (EKS, AKS, GKE) and serverless compute.

Implementing AI observability, MLOps, and data pipelines on cloud.

6 Case Studies & Real-World Transformations (10 min)

BFSI: Mainframe-to-Microservices using AWS CAF + GenAI refactoring.

Manufacturing: SAP modernization using Azure CAF + AI code summarization.

Retail: Omnichannel API modernization with GCP CAF + Copilot GPTs.

7 Best Practices & Roadmap (5 min)

Align modernization with business capability models.

Embed AI governance into CAF workflows.

Build continuous improvement loops through feedback and metrics.

8 Q&A / Wrap-Up (5 min)

Recap core insights.

The future of AI-enhanced cloud adoption and autonomous modernization.

Securing the Digital Landscape: A Deep Dive into OWASP Top 10 for Applications, APIs, and LLMs

5:00 PM MDT

Join us for an immersive journey into the heart of modern cybersecurity challenges. In this groundbreaking talk, we delve into the intricacies of securing your digital assets with a focus on three critical domains: applications, APIs, and Large Language Models (LLMs).

As developers and architects, you understand the paramount importance of safeguarding your systems against evolving threats. Our session offers an exclusive opportunity to explore the industry-standard OWASP Top 10 vulnerabilities tailored specifically to your domain.

Uncover the vulnerabilities lurking within your applications, APIs, and LLMs, and gain invaluable insights into mitigating risks and fortifying your defenses. Through live demonstrations and real-world examples, you'll witness firsthand the impact of security breaches and learn proactive strategies to combat them.

Whether you're a seasoned architect seeking to fortify your organization's security posture or a developer striving to build resilient systems, this talk equips you with the knowledge and tools essential for navigating the complex landscape of cybersecurity.

Agenda

• OWASP Top 10 Overview

  • Introduction to OWASP
  • Significance of OWASP Top 10
  • Overview of OWASP Top 10 for Applications, APIs, and LLMs

• OWASP Top 10 for Application Security

  • Presentation: Common Vulnerabilities and Mitigation Strategies
  • Demonstration: Live Examples of Application Security Vulnerabilities

• OWASP Top 10 for API Security

  • Presentation: Key Challenges in API Security and Best Practices
  • Demonstration: Illustration of API Security Vulnerabilities and Attacks

• OWASP Top 10 for LLM Applications (Large Language Models)

  • Presentation: Unique Security Concerns in LLM Applications
  • Demonstration: Showcase of LLM Security Vulnerabilities and Risks

• Q&A and Discussion

  • Open Floor for Questions and Discussion

• Conclusion

  • Summary of Key Takeaways
  • Call to Action: Implementing Security Best Practices

Mastering the AI System Design Methodology

9:00 AM MDT

Mastering the AI-First System Design Methodology is a must-attend talk for developers and architects seeking to elevate their system design capabilities in the era of intelligent systems. In this dynamic 90-minute session, attendees will embark on a comprehensive journey through the foundational principles of modern system design—now reimagined for AI integration—with a practical focus on the C4 model and its application to AI-enabled architectures.

This session is designed to equip professionals with the frameworks and tools necessary to build scalable, efficient, AI-aware systems that deliver lasting impact in a rapidly evolving digital ecosystem.

We'll begin by exploring the critical importance of understanding business requirements and stakeholder intent—an essential step in designing systems that align human values with machine intelligence. From there, we’ll guide attendees through a structured, AI-augmented design methodology: from stakeholder engagement and context modeling to system decomposition and refinement using LLMs and generative AI assistants.

Each stage will be brought to life with real-world examples, hands-on exercises, and interactive discussions—demonstrating how AI can accelerate ideation, automate documentation, optimize decisions, and identify design flaws early in the process.

Special focus will be given to incorporating empathy maps, value chain analysis, and customer journey mapping, enhanced with AI-driven pattern recognition and predictive insights. These tools enable deeper understanding of user behavior and business dynamics, resulting in more responsive and adaptive system architectures.

Whether you're a seasoned architect embracing AI-driven transformation or a developer ready to future-proof your design thinking, this talk will deliver actionable insights into building robust, intelligent, and human-centric systems. Join us to reimagine system design through the lens of AI—and become a key innovator in your organization’s AI-first journey.

The Importance of System Design

• The role of system design in software development
• Examples of project successes and failures

Overview of System Design Methodology

• Introduction to System Design Methodology
• The C4 Model: Context, Containers, Components, and Code

Deep Dive into the Methodology Stages

* Engage with Business Stakeholders
    * Techniques for engagement and prioritization
    * Case Study: A startup's journey to understand market needs

* Identify Vital Business Capabilities
    * Mapping business capabilities
    * Case Study: Streamlining operations for a logistics company

* Understand the Internal and External Personas
    * Using empathy maps and customer journey mapping
    * Case Study: Designing a healthcare app with patient and provider personas

* Develop a New Value Proposition
    * Crafting value propositions
    * Case Study: Innovating retail experience with a new e-commerce platform

* Define Solution Architecture
    * Detailing architecture and capability modules
    * Case Study: Architectural overhaul for a financial services firm

* Define Component Process Flows
    * Visualizing interactions and process flows
    * Case Study: Enhancing the order fulfillment process for an online retailer

* Review, Refine, and Finalize
    * Consolidating insights and preparing for implementation
    * Case Study: Finalizing and launching a new feature for a social media platform