Rohit Bhardwaj

ChatGPT for Software Architects & Developers 2025 — Build Agentic, Secure AI

Monday, 9:00 AM EST

In 2025, ChatGPT is no longer just a chatbot—it’s an agent platform that integrates with your stack (via MCP, Realtime API, Azure Agent Service, Amazon Q Dev, Gemini Code Assist, Claude 3.5).
Software Architects and Developers who can harness this ecosystem will design faster, code safer, and ship smarter.

This workshop takes you from basic prompting to building a full-stack AI-powered agent with guardrails, observability, and enterprise-ready governance—all in one day.

Who Should Join?

• Software Architects – to shape AI-augmented blueprints
• Enterprise Architects – to design AI-ready platforms with governance
• Technical Leads & Senior Developers – to scale code, reviews, and CI/CD with AI copilots
• IT Managers & Engineering Leaders – to drive digital transformation with secure AI workflows
• AI Enthusiasts – to future-proof your career with agentic systems skills

What You’ll Learn

• Master prompt engineering that passes acceptance tests
• Build agentic workflows using MCP tool servers and the Realtime API
• Compare GPT-5, Claude 3.x, Gemini 2.x, Amazon Q Dev, Azure Agent Service for enterprise use
• Implement RAG 2.0 with evals & structured outputs
• Secure your systems with OWASP LLM Top 10 (2025) red-team/blue-team exercises
• Integrate AI into CI/CD pipelines, IDEs, and architectural documentation
• Optimize costs, observability, and governance for large-scale AI adoption

Outcomes
By end of day, you will leave with:

A working repo of an MCP-connected agent

A security checklist mapped to OWASP LLM Top 10

Hands-on experience with AI copilots across major vendors

Playbooks for cost, governance, and evaluation

Confidence to lead AI-first architecture initiatives in your org

Agenda

Module 1 — The New AI Landscape

• GPT-4o/o3, Claude 3.5, Gemini 2.5, Amazon Q Dev, Azure Agent Service
• Trade-offs in cost, latency, context, and governance

Module 2 — Agentic Architecture with ChatGPT

• Hands-on: Build a ChatGPT agent using MCP + Realtime API
• Connect to repos, tickets, APIs — simulate enterprise workflows

Module 3 — RAG 2.0 & Structured Outputs

• Retrieval augmentation patterns for architecture tasks
• Eval harnesses, JSON-strict outputs, regression gates

Module 4 — SDLC Integration with AI Copilots

• Amazon Q Dev for code refactor
• Gemini Code Assist “Agent Mode” for multi-file edits
• Azure Agent Service for gated deploys

Module 5 — Security & Risk

• Prompt-injection red team exercise
• OWASP LLM Top 10 walkthrough
• Guardrails for data and compliance

Module 6 — Observability & Governance

• Cost controls, tracing, quotas, model routing
• AI SLOs & governance dashboards

Capstone Lab (90 min)
Build a secure MCP-powered ChatGPT Agent that:

• Reads a repo → files a ticket → generates a patch → passes evals → resists injection.
• Optional: connect via Realtime API voice call (SIP) for a live hotline demo.

Mastering Microservices: A Seven-Step Process

Tuesday, 5:00 PM EST

Join us for a transformative captivating session where you'll embark on a journey of discovery as we unveil a comprehensive seven-step methodology designed to revolutionize your approach to API design and implementation.
Throughout the session, we'll explore practical use cases drawn from diverse industries, allowing you to gain valuable insights into the intricacies of designing APIs for real-world scenarios. From taxi hailing giants like Uber and Lyft to social media titans such as Facebook and Instagram, you'll dissect the unique challenges and requirements driving API design in today's dynamic digital landscape.

Guided by seasoned industry experts, you'll delve into the core principles of RESTful microservices architecture and learn how to apply them effectively in your own projects. Through engaging presentations, interactive exercises, and hands-on, you'll master essential concepts such as OData integration, industry best practices, and innovative design strategies.

By the end of the session, you'll emerge with a deep understanding of the seven-step process for designing superior cloud-native RESTful microservices APIs. Armed with practical insights and invaluable experience, you'll be ready to tackle the challenges of modern software architecture head-on, driving innovation and excellence within your organization.

Don't miss this opportunity to elevate your skills, expand your knowledge, and unlock the full potential of RESTful microservices architecture. Join us and take the first step towards architectural mastery today!

– Seven-Step Methodology to Design RESTful Microservices APIs
Embark on our journey with a comprehensive overview of the seven-step methodology crucial for crafting robust and scalable RESTful microservices APIs. Learn how to navigate the intricacies of API development while adhering to industry best practices, setting the foundation for success in your microservices architecture endeavors.
Exercise: Industry Best Practices for API Development
Put your newfound knowledge into action as we delve into hands-on exercises designed to reinforce industry best practices for API development. Gain practical insights and hone your skills in designing APIs that meet the highest standards of performance and scalability.

– Unveiling OData - The Best Way to REST
Explore the power of OData (Open Data Protocol) as we unravel its capabilities and advantages in building RESTful microservices. Discover how OData simplifies data access and manipulation, offering unparalleled flexibility and efficiency in your API design endeavors.
Exercise: Online Ecommerce API Design
Engage in a real-world exercise focusing on online ecommerce API design, where you'll apply OData principles to create seamless and intuitive API solutions tailored for the digital marketplace.

– Understanding Use Cases
Delve into the realm of practical use cases as we analyze scenarios from industries like taxi hailing (Uber/Lyft) and social media (Facebook/Instagram). Gain insights into the unique challenges and requirements driving API design in these domains.
Exercise: Use Case Exploration
Immerse yourself in hands-on exercises exploring use cases for taxi hailing and social media companies. Apply your newfound knowledge to design APIs that address specific challenges and optimize performance in these dynamic environments.

Designing Taxi Hailing APIs - Uber/Lyft
Deep dive into the intricacies of designing APIs for taxi hailing services like Uber and Lyft. Explore real-world examples and best practices for architecting APIs that facilitate seamless interactions between users, drivers, and the platform.
Exercise: Design Challenges
Challenge yourself with hands-on design exercises focused on tackling real-world challenges such as duplicate records, data migration, large data volume considerations, data rate limits, decision-making, and data validations.

– Navigating API Challenges
Navigate through the complexities of API challenges as we dissect common pitfalls and obstacles encountered in RESTful microservices architecture. Gain strategies and insights to overcome these challenges and optimize your API solutions for maximum efficiency and scalability.

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

Wednesday, 9:00 AM EST

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.

AI-Enhanced Big Data: Integrating Private LLMs and Vector Databases

Wednesday, 11:00 AM EST

In this dynamic talk, we explore the fusion of AI, particularly ChatGPT, with data-intensive architectures. The discussion covers the enhancement of big data processing and storage, the integration of AI in distributed data systems like Hadoop and Spark, and the impact of AI on data privacy and security. Emphasizing AI's role in optimizing big data pipelines, the talk includes real-world case studies, culminating in a forward-looking Q&A session on the future of AI in big data.

This talk delves into the innovative integration of advanced AI models like ChatGPT into data-intensive architectures. It begins with an introduction to the significance of big data in modern business and the role of AI in scaling data solutions. The talk then discusses the challenges and strategies in architecting big data processing and storage systems, highlighting how AI models can enhance data processing efficiency.

A significant portion of the talk is dedicated to exploring distributed data systems and frameworks, such as Apache Hadoop and Spark, and how ChatGPT can be utilized within these frameworks for improved parallel data processing and analysis. The discussion also covers the critical aspects of data privacy and security in big data architectures, especially considering the implications of integrating AI technologies like ChatGPT.
The talk further delves into best practices for managing and optimizing big data pipelines, emphasizing the role of AI in automating data workflow, managing data lineage, and optimizing data partitioning techniques. Real-world case studies are presented to illustrate the successful implementation of AI-enhanced data-intensive architectures in various industries.

1. Introduction (10 mins)

   • Unleashing the power of big data in modern businesses
   • Importance of data-intensive architectures in scaling data solutions
   • Introducing AI's role in big data, with a focus on ChatGPT

2. Part 1: Architecting for Big Data Processing and Storage (25 mins)

   • Understanding the challenges of big data processing
   • Designing scalable data storage solutions
   • Achieving high availability and fault tolerance
   • Integrating AI models like ChatGPT for enhanced data processing

3. Part 2: Distributed Data Systems and Frameworks (25 mins)

   • Leveraging the potential of distributed processing tools
   • Introduction to Apache Hadoop, Spark, and other frameworks
   • Performing parallel data processing and analysis
   • How ChatGPT and similar AI models can be utilized in distributed systems

4. Part 3: Handling Data Privacy and Security in Big Data Architectures (20 mins)

   • Challenges and considerations for data privacy in big data environments
   • Ensuring data security and confidentiality
   • Adhering to compliance regulations in big data projects
   • Discussing the implications of AI like ChatGPT on data privacy and security

5. Part 4: Best Practices for Managing and Optimizing Big Data Pipelines (20 mins)

   • Data workflow orchestration and automation
   • Data lineage and metadata management
   • Data partitioning and optimization techniques
   • Utilizing AI models like ChatGPT for optimizing big data pipelines

6. Case Studies and Real-World Applications (10 mins)

   • Inspiring examples of successful data-intensive architecture implementations
   • Learning from the experiences of leading organizations
   • Case studies involving ChatGPT in big data solutions

7. Conclusion and Q&A (10 mins)

   • Recapitulation of key takeaways
   • Addressing questions and facilitating discussions with the audience
   • Highlighting the future of AI and big data with technologies like ChatGPT

Overall, this talk aims to provide a comprehensive understanding of how AI, especially ChatGPT, can be integrated into data-intensive architectures to enhance big data processing, analysis, and management, preparing attendees to harness AI's potential in their big data endeavors.

Key Takeaways:

1. AI's Impact on Big Data: Insight into how AI, especially ChatGPT, enhances big data processing and scalability.
2. Designing AI-Integrated Systems: Strategies for building scalable, AI-enabled data processing and storage solutions.
3. AI in Distributed Frameworks: Understanding the integration of AI in systems like Hadoop and Spark for improved data analysis.
4. Data Privacy and Security: Best practices for maintaining data integrity and compliance in AI-enhanced big data environments.
5. Optimizing Data Pipelines with AI: Techniques for using AI to automate data workflows and optimize data management.
6. Real-World AI Applications: Learning from case studies where AI in data architectures has driven success.
7. Future of AI in Big Data: Insights into the evolving role and potential of AI technologies like ChatGPT in big data.
8. Interactive Learning: Engaging in discussions and Q&A for a deeper understanding of AI's role in big data.

Graph Mastery to AI: : Unleashing the Power of Connections

Wednesday, 5:00 PM EST

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.

Resilient Cloud Architecture Design Patterns

Thursday, 9:00 AM EST

Resilient architecture is fundamental when working in distributed, cloud-based systems. Designing and architecting large-scale applications managing millions of requests brings unique challenges with availability, performance, and integration. You will need to make difficult choices and evaluate tradeoffs. Luckily, you can use different architecture patterns to make a distributed application more resilient. Based on evolutionary architecture, this approach enables you to create systems designed to evolve with the ever-changing software development ecosystem. Resilient architecture patterns will allow you to create systems that continue functioning even when components fail.

Join expert Rohit Bhardwaj to learn how to implement an evolutionary architecture approach and understand resilient architecture patterns. This training will explore architecture decisions you may need to make when evaluating your architecture to improve performance and resiliency. For example, you will no longer struggle to handle millions of requests per second or face issues when routing traffic.
What you'll learn — and how you can apply it

By the end of this live, hands-on, online course, you'll understand the following:
How to create responsive, maintainable, extensible architecture from resilient, elastic design utilizing message-driven services

How to design cost-effective Recovery Point Objectives (RPOs) and Recovery Time Objectives (RTOs)

How to identify blocking issues with microservices in the cloud

How to evaluate caching strategies that can help lower costs and protect from DOS attacks

And you'll be able to:

Design high availability, high scalability, low latency, and resilient architectures.

Analyze and review implementations.

Identify key scalability challenges in your company.

Prevent cascading failures and preserve functionality.

This training is for you because…

You have an existing need to evaluate your current architecture.

You want to understand best practices.

You need to design new systems and want to evaluate which pattern to use.

Prerequisites

Basic knowledge of software architecture

Familiarity with design principles

Thinking application as stateless for all the API calls makes the system available most of the time and requires creating a cache for common distributed data. Next, we examine how to deal with cascading failures and timeout scenarios. As part of auto-healing, applications need to Detect, Prevent, Recover, Mitigate, and Complement so that the service is resilient.

The key takeaways for the audience are as follows:

*Resiliency is essential for any feature in the cloud.

*Understanding the value chain is critical to identifying failure points.

*Challenges come in determining if there is a failure and designing the system for auto-
healing

*The focus should be first to prevent a failure from occurring.

*Identifying critical challenges in your company and tools and techniques to auto-heal and provide a sustainable solution

Course Schedule

Evolutionary Architecture:

– Scaling to 100 million customers

– Understanding Requirements - Empathy Map

– Fail Points

– Defining KPIs

Resilient Patterns:

– BulkHead pattern

– Routing Strategies

– Design Issues with Microservices

– API Gateway Pattern

– Database per Service Pattern

– Database Sharding Patterns

– Fan out Pattern

– Publish-Subscribe Pattern

– Command Query Responsibility Segregation (CQRS)

– Message filter pattern

– Topic-queue-chaining Pattern

– Message Partitioning Patterns

– Priority Queue Pattern

Caching:

– Caching and Failure Injection

– Distributed system challenges

– Caching Patterns

– Order in Chaos

– Resilient Steps

– Resources

Enterprise Architecture 2025–2028: AI-Native, Agentic & Governed

Thursday, 11:00 AM EST

Over the next three years, the enterprise technology stack will be reshaped by Agentic AI, AI governance platforms, confidential computing, and post-quantum cryptography (PQC)—while sustainability and cost optimization become architectural imperatives.
This keynote gives architects a concrete operating model to turn emerging technologies into trusted, scalable platforms that CIOs and CISOs will approve.
You’ll learn how to design an AI-native enterprise architecture: agentic workflows orchestrated with MCP/LangGraph, retrieval grounded in GraphRAG, governed under ISO/IEC 42001 and the NIST AI RMF, secured with OWASP LLM guardrails and confidential compute, and optimized for both FinOps and GreenOps.
We’ll explore how to measure cost and carbon per request using Software Carbon Intensity (SCI), and how to prepare for a PQC future using FIPS 203/204/205.
The session closes with a 90-day activation plan and a 3-year roadmap template to modernize your EA practice for the intelligent enterprise era.

Agenda

• The Shift: From AI pilots to governed, agentic platforms


• Agentic Architecture Blueprint: MCP + LangGraph + GraphRAG


• Governance & Security: ISO/IEC 42001, NIST AI RMF, OWASP LLM controls


• FinOps & GreenOps: Measuring cost + carbon (SCI)


• PQC & Confidential Compute: Preparing for secure inference


• Roadmap: 90-day activation and 3-year EA modernization plan

Key Takeaways:

• Blueprint for AI-native, agentic enterprise architecture.


• Governance pack aligned to ISO/IEC 42001 + NIST AI RMF.


• GraphRAG and AgentOps patterns for explainability and resilience.


• Practical controls for LLM security, confidential AI, and PQC readiness.


• FinOps + GreenOps playbook with measurable ROI and carbon metrics.


  Target Audience:
  Enterprise and software architects, platform leads, AI program directors, and security/compliance leaders are designing the next generation of enterprise systems.