Rohit Bhardwaj

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

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

9:00 AM MDT

AI has permanently transformed the role of Enterprise Architects. Traditional architectures built around data, applications, and integration are no longer enough. Modern intelligent systems rely on retrieval-augmented reasoning (RAG), relationship-driven graph reasoning (GraphRAG), and autonomous AI agents that must operate safely, predictably, and in alignment with business goals.

This full-day immersive workshop introduces the ARCHAI Blueprint, the first EA 4.0 framework that unifies:

– ARCHAI Fabric — enterprise knowledge & reasoning layer powered by RAG and GraphRAG

– ARCHAI Agents — assistive, autonomous, and cooperative agents with guardrails

– ARCHAI View — C4++ modeling for intelligent architectures

– ARCHAI Maturity Model — a 5-level roadmap toward the autonomous enterprise

Through storytelling, live architecture labs, and hands-on modeling, participants will learn how to design safe, scalable, AI-augmented enterprise architectures. You will build an end-to-end architecture for a realistic case study—ArchiMetal, a global manufacturing enterprise modernizing with AI.

By the end, you will not just understand RAG and GraphRAG—you will know how to embed them into production-grade enterprise architecture that is governable, observable, and future-proof.

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KEY TAKEAWAYS

Participants will leave with the ability to:

1. Architect AI-Driven Knowledge Systems
   •Design enterprise-scale RAG and GraphRAG pipelines
   •Build knowledge fabrics that unify documents, graphs, embeddings & metadata
   •Govern retrieval consistency, drift, safety, lineage & real-time updates

2. Model Intelligent Systems Using ARCHAI View
   •Produce C0 → C3 diagrams (C4++ enhanced for AI)
   •Model knowledge flows, agent interactions, guardrails & reasoning boundaries

3. Design and Govern Enterprise AI Agents
   •Define agent roles, decisions, constraints, and safety boundaries
   •Create multi-agent workflows across business domains
   •Establish guardrail & observability architecture

4. Build AI-Augmented Business, Data, Application & Technology Architectures
   •Extend TOGAF with AI reasoning-layer constructs
   •Integrate RAG/GraphRAG into EA artifacts and capability maps
   •Architect runtime platforms for inference, retrieval, safety & cost control

5. Create an EA 4.0 Roadmap Using the ARCHAI Maturity Model
   •Assess enterprise readiness
   •Identify transformation milestones across 5 maturity levels
   •Build a 12–36 month strategic roadmap for intelligent systems adoption

Welcome & Foundations of EA 4.0
•Why enterprise architecture must evolve for AI
•Overview of the 5 ARCHAI components
•ARCHAI Blueprint
•ARCHAI View
•ARCHAI Fabric
•ARCHAI Agents
•ARCHAI Maturity Model

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Session 1 — Architecture Vision
•The new Enterprise Knowledge & Reasoning Layer
•Why RAG/GraphRAG require architectural foundations
•Intelligent system context modeling (C0/C1)
•Introducing the ArchiMetal case study

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Session 2 — Business Architecture for AI
•Mapping AI-driven capabilities and value streams
•Decision hotspots and agent opportunities
•Business capability redesign
•ARCHAI Maturity Model assessment

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Session 3 — Data Architecture: ARCHAI Fabric
•Designing the knowledge layer (RAG + GraphRAG)
•Vector, graph, ontology, and metadata models
•Governance for retrieval, drift, lineage, and safety
•C2 modeling for the Fabric

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Session 4 — Application Architecture: ARCHAI Agents
•Assistive, autonomous & cooperative agent patterns
•Agent decision boundaries and governance
•Multi-agent workflows & human-in-loop logic
•C2/C3 diagrams for agent flows

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Session 5 — Technology Architecture
•AI & retrieval runtimes
•Guardrail and policy engines
•Observability for reasoning, retrieval, and agent behavior
•Technical standards for EA 4.0 systems

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Session 6 — Integrated Architecture Lab
•Build the full ARCHAI Blueprint for ArchiMetal
•Create C0 → C3 diagrams (ARCHAI View)
•Design Fabric + agent ecosystem
•Map guardrails & governance
•Define the EA 4.0 transformation roadmap

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Session 7 — Governance & Operating Model
•Knowledge governance (Fabric)
•Agent governance (charters, permissions, kill switches)
•Model & retrieval lifecycle governance
•Risk, compliance, auditability
•EA 4.0 operating model for intelligent systems

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Session 8 — Future Trends & Roadmap
•Multi-modal RAG & graph fusion
•Enterprise agent meshes
•Intelligent twins & edge reasoning
•Autonomous governance
•3–5 year ARCHAI roadmap

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Closing & Next Steps
•Recap of frameworks & deliverables
•EA transformation priorities for the next 90 days
•Certification and final Q&A

Enterprise Architecture 4.0: The AI-Driven Future Preview

ARCHAI Blueprint for architecting and designing the Intelligent Enterprise with Generative AI, Agents, and Next-Gen Technologies

8:30 AM MDT

AI, agentic workflows, digital twins, edge intelligence, spatial computing, and blockchain trust are converging to reshape how enterprises operate.
This session introduces Enterprise Architecture 4.0—a practical, future-ready approach where architectures become intelligent, adaptive, and continuously learning.

You’ll explore the EA 4.0 Tech Radar, understand the six major waves of disruption, and learn the ARCHAI Blueprint—a structured framework for designing AI-native, agent-ready, and trust-centered systems.
Leave with a clear set of patterns and a 12-month roadmap for preparing your enterprise for the next era of intelligent operations.

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KEY TAKEAWAYS
•Understand the EA 4.0 shift toward intelligent, agent-driven architecture
•Learn the top technology trends: AI, agents, edge, twins, spatial, blockchain, and machine customers
•See how the ARCHAI Blueprint structures AI-first design and governance
•Get practical patterns for agent safety, digital twins, trust, and ecosystem readiness
•Leave with a concise 12-month roadmap for implementing EA 4.0

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AGENDA

– The Speed of Change
Why traditional enterprise architecture cannot support AI-native, agent-driven systems.

– The EA 4.0 Tech Radar
A 3–5 year outlook across:
•Agentic AI
•Edge intelligence
•Digital twins
•Spatial computing
•Trusted automation (blockchain)
•Machine customers

– The Six Waves of Transformation
Short deep dives into each wave with real enterprise use cases.

– The ARCHAI Blueprint
A clear architectural framework for AI-first enterprises:
•Attention & Intent Modeling
•Retrieval & Knowledge Fabric
•Capability & Context Models
•Human + Agent Co-working Patterns
•Action Guardrails & Safety
•Integration & Intelligence Architecture

This gives architects a single, unified design methodology across all emerging technologies.

– The Architect’s Playbook
Practical patterns for:
•Intelligence fabrics
•Agent-safe APIs
•Digital twin integration
•Trust & decentralized identity
•Ecosystem-ready design

– Operationalizing EA 4.0
How architecture teams evolve:
•New EA roles
•Continuous planning
•Agent governance
•EA dashboards
•The 12-month adoption roadmap

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 Systems: AI-Enhanced Cloud Adoption Frameworks

Achieving Innovation in all systems including modern and legacy, 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

A.R.C.H.A.I. Blueprint - Designing Intelligent, Safe, and Scalable Systems in the Age of AI

9:00 AM MDT

AI systems behave fundamentally differently from traditional software — they reason, retrieve, learn, and act with autonomy.
These behaviors introduce new failure modes: retry storms, inference cost surges, misaligned agent actions, semantic drift, and retrieval errors.
Most system design approaches were never built to handle these risks.

This talk presents the A.R.C.H.A.I. Blueprint
(AI-Ready Contextual Human-Aligned Initiative),
a modern, AI-first architecture methodology that helps organizations design systems that are safe, scalable, resilient, and aligned with human intent.

Through vivid scenarios drawn from Dreamazon—a fictional global retailer—we show how classical architecture breaks when AI agents interact with APIs, data, and user flows.
Attendees learn how to extend the traditional C4 model into C4+, incorporating AI reasoning layers, retrieval paths, guardrails, drift surfaces, and human oversight points.

Participants also engage in an Architecture Lab, applying ARCHAI to design an AI-powered system using real templates, patterns, and safety practices.

This session equips architects, developers, and technical leaders with the frameworks and confidence needed to build AI-first systems responsibly.

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Key Skills You Will Learn
•How to design systems that incorporate AI reasoning and autonomous behavior
•How to extend C4 into C4+, modeling intelligence, retrieval, guardrails, and safety layers
•How to build AI-safe flows with idempotency, retry constraints, and ambiguity handling
•How to architect Retrieval-Augmented Generation (RAG) and agent orchestration
•How to map business capabilities and value chains for AI transformation
•How to identify AI-specific failure patterns and design to prevent them
•How to define drift detection, cost ceilings, and guardrail policies
•How to create an AI-first governance and ownership model

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What You Will Take Away
•A complete understanding of the A.R.C.H.A.I. Blueprint
•A reusable C4+ architecture template for designing AI systems
•Practical patterns to prevent runaway AI behavior, duplication, and cost explosions
•A framework for aligning AI systems with business intent and human oversight
•Tools for modeling retrieval boundaries and agent interaction flows
•A clear professional roadmap for becoming an AI-first architect

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Agenda
•Why traditional system design breaks when AI agents enter the system
•Realistic case studies from Dreamazon’s Black Friday failures
•Introduction to the A.R.C.H.A.I. Blueprint
•How to apply the six pillars to real-world AI use cases
•Extending the C4 model into C4+ for AI architecture
•Modeling reasoning paths, retrieval pipelines, and safety constraints
•Architecture Lab: Building an AI-ready system using ARCHAI
•Design templates, scorecards, and guardrail patterns
•Roadmap for evolving into an AI-first architect