Rohit Bhardwaj

This talk is designed to catapult your productivity, enhance your emotional intelligence, and refine your problem-solving skills. This talk is not just a series of presentations; it's a transformative experience tailored for the ambitious software developer and architect seeking to leave a mark in the fast-paced world of technology.

Dive into the essence of developer and architect productivity, where we unravel the secrets to optimizing your workflow and leveraging your skills for maximum impact. Discover the “24 Hours Instant Happiness” principle, a proven strategy to inject a dose of joy into your daily routine, fostering a positive work environment and personal life.

“Maximizing Your Impact” takes you deeper into the realm of influence, equipping you with the tools to excel in your projects and inspire those around you. Through “Effective Communication” and the intriguing “Mirror Technique,” learn how to build rapport, foster collaboration, and lead with empathy, amplifying your charisma in all professional interactions.

As we delve into the core of success, “Emotional Intelligence is 85% of Success” highlights the paramount importance of self-awareness, self-regulation, motivation, empathy, and social skills in achieving your goals. The “6 Phase Meditation Approach” and “Day Launcher” sessions are designed to refine your focus, creativity, and emotional stability, setting a solid foundation for a productive day ahead.

The inclusion of “Empathy Maps” and “IDEO Case Studies” offers a practical lens through which to view user-centric design and innovation. At the same time, the “SCAMPER Technique” provides a creative framework for problem-solving, ensuring you're equipped to tackle challenges with agility and inventiveness.

Elevate your productivity to new heights with “5 Choices for Super Productivity,” a comprehensive guide to prioritizing effectively, embracing extraordinary outcomes, and mastering your technology. Learn the art of “Managing Energy, Not Time,” a paradigm shift that promises to enhance your efficiency and job satisfaction.

As the talk culminates, “The Paradox of Choice” and the latest “Technology Trends to Focus On” prepare you to navigate the complexities of the modern tech landscape with confidence and curiosity.

This masterclass is more than just a talk; it's an invitation to transform how you work, lead, and innovate. Join us to unlock your full potential and reshape your future in software development and architecture. Whether you're looking to boost your productivity, enhance your emotional intelligence, or simply find more joy in your work, this talk is your gateway to a more fulfilling career and life.

Developers and Architects are designers, problem solvers, and innovative, creative artists. Software design is an art that requires both left and right brains to be active so you can understand what customers need. Next, we will explore habits and tools to plan, learn, research, organize, teach, develop, mentor, and architect.

Agenda
Enhancing Productivity and Personal Growth
– Developer and Architect Productivity
Strategies for improving daily workflow and efficiency in software development and architecture.

– 24 Hours Instant Happiness
Quick wins for boosting morale and happiness within the team and personal life.

– Maximizing Your Impact
Techniques to increase your influence and contributions in projects and teams.

– Effective Communication
Importance of clear communication and the Mirror Technique to improve understanding and rapport.

– Increasing Charisma
Tips for becoming more charismatic and influential in professional settings.
Building Emotional Intelligence and Mindfulness

– Emotional Intelligence is 85% of Success
Discussing the critical role of emotional intelligence in achieving professional success.

– 6-Phase Meditation Approach
Introducing a meditation technique to enhance focus, creativity, and emotional stability.

– Day Launcher
A strategy to start your day with intention and focus, setting the tone for productivity and success.

– Empathy Map
Utilizing empathy maps to better understand user needs and enhance team collaboration.

– IDEO Case Studies
Examining case studies from IDEO to illustrate successful applications of empathy in design.

– Understanding a Problem with SCAMPER Technique
Exploring the SCAMPER technique to creatively solve problems and innovate solutions.
Strategies for Super Productivity

– 5 Choices for Super Productivity
Detailed strategies for enhancing productivity by prioritizing important tasks, aiming for extraordinary outcomes, scheduling priorities (“big rocks”), mastering technology use, and maintaining energy levels.

– Managing Energy, Not Time
Shifting focus from time management to energy management to maximize productivity and well-being.

– Increasing Frequency to Do What You Want
Techniques to align daily actions with personal and professional goals more effectively.

– The Paradox of Choice
Understanding how reducing options can lead to increased satisfaction and productivity.

– Technology Trends to Focus On
Highlighting current technology trends that developers and architects should be aware of to stay ahead in their field.

AI inference is no longer a simple model call—it is a multi-hop DAG of planners, retrievers, vector searches, large models, tools, and agent loops. With this complexity comes new failure modes: tail-latency blowups, silent retry storms, vector store cold partitions, GPU queue saturation, exponential cost curves, and unmeasured carbon impact.

In this talk, we unveil ROCS-Loop, a practical architecture designed to close the four critical loops of enterprise AI:
•Reliability (Predictable latency, controlled queues, resilient routing)
•Observability (Full DAG tracing, prompt spans, vector metrics, GPU queue depth)
•Cost-Awareness (Token budgets, model tiering, cost attribution, spot/preemptible strategies)
•Sustainability (SCI metrics, carbon-aware routing, efficient hardware, eliminating unnecessary work)

KEY TAKEAWAYS
•Understand the four forces behind AI outages (latency, visibility, cost, carbon).
•Learn the ROCS-Loop framework for enterprise-grade AI reliability.
•Apply 19 practical patterns to reduce P99, prevent retry storms, and control GPU spend.
•Gain a clear view of vector store + agent observability and GPU queue metrics.
•Learn how ROCS-Loop maps to GCP, Azure, Databricks, FinOps & SCI.
•Leave with a 30-day action plan to stabilize your AI workloads.

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AGENDA
1.The Quiet Outage: Why AI inference fails
2.X-Ray of the inference pipeline (RAG, agents, vector, GPUs)
3.Introducing the ROCS-Loop framework
4.19 patterns for Reliability, Observability, FinOps & GreenOps
5.Cross-cloud mapping (GCP, Azure, Databricks)
6.Hands-on: Diagnose an outage with ROCS
7.Your 30-day ROCS stabilization plan
8.Closing: Becoming a ROCS AI Architect

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.

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

AWS Certified Architect is one of the hottest and most valuable certifications in 2021. Amazon Web Services is one of the most popular cloud platforms around.

Abilities Validated by the Certification are:

– Effectively demonstrate knowledge of how to architect and deploy secure and robust applications on AWS technologies.

– Define a solution using architectural design principles based on customer requirements

– Provide implementation guidance based on best practices to the organization throughout the life cycle of the project

In this workshop, we will explore the following topics with hands-on labs:

– Design using compute, networking, storage, and database AWS services, EC2, S3 storage, RDS, DynamoDB
– AWS deployment and management services, Cloud Formation, Identity Access Management
– API Gateway, Route 53
– Serverless architecture, AWS Lambda, Step Functions
– Simple Notification Service (SNS), Simple Queue Service(SQS)
– AWS Cloud Watch, AWS CloudTrail
– Identify and define technical requirements for an AWS-based application
– Recommended best practices for building secure and reliable applications on the AWS platform
– Architectural principles of building on the AWS Cloud
– AWS global infrastructure
– Network technologies as they relate to AWS, Virtual Private Cloud
– Security features and tools that AWS provides and how they relate to traditional services

This talk is ideal for the following roles:
Architects
Technical Leads
Programers
Integration Architects
Solution Architects

Please get free tier AWS account from following link:
https://aws.amazon.com/

Secure, Efficient, Resilient, High-performing, Sustainable, and Cost-effective

Are your applications well-architected? This talk will explore the best practices for operational excellence, Security, Reliability, Performance Efficiency, and cost optimization. Think of systems and services which provide business values. Do you know if all of these services are well-architected? You will learn how to create mechanisms, a repeatable process that allows you to improve over time. We will explore the best practices using real-world examples to make them more concrete and actionable.

Well-Architected helps cloud architects build secure, high-performing, resilient, and efficient infrastructure for various applications and workloads. They are built around six pillars—operational excellence, security, reliability, performance efficiency, cost optimization, and sustainability.

Join expert Rohit Bhardwaj to gain the knowledge and skills you need to solve current cloud implementation problems.

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

– How to manage identities for people and machines and understand the significance of role-based, service-based, and attribute-based access

– How to design network topology and protect your network resources

– How to design interactions in a distributed system to prevent failures and improve performance and resiliency

– How to select the best-performing architecture and choose performant storage and databases

– How to manage demand and supply resources

– How to take advantage of user behavior patterns to support your sustainability goals

Topics covered:

Design Principles

– Scaling patterns

– Architecture Design Principles

– Capacity calculations

– Impact of data on design decisions

– Shared Responsibility Model

Reliability

– Resilient Architecture principles

– Herds of complex real-time distributed systems

– Hands-on Exercises / Case Studies

– Blast radius- fault isolation to protect your workload

– Availability patterns

– Recovery Point Objective and Recovery Time Objectives

– Data backup data patterns

– Routing Strategies

– Service quotas and constraints

– Design your workload service architecture

– Failure management in a distributed system

– Monitoring workload resources

– Calculating the response times

– Fallacies of Distributed Systems

– Testing reliability

– Cost Optimization

– Design cost-optimized storage

– Cost-optimized compute

– Data transfer costs

– Manage demand and supply resources

– Hands-on Exercises / Case Studies

Sustainability
– User behavior patterns

– Data access and usage patterns

– Development and deployment processes

– Hands-on Exercises / Case Studies

Performance Efficiency
– Select the best-performing architecture

– Choosing performant storage and databases?

– No-SQL for performance

– Caching strategies

– DOS attacks

– Tradeoffs to improve performance

– Evolving your workload

– Handle skewed data

– CDN networks like Cloudfront to solve the caching requirements for static and Dynamic
contents

– Monitor and set alarms for performance and network issues

– Hands-on Exercises / Case Studies

Operational Excellence
– Principles for Perform Operation Infrastructure as code

– Annotate Documentation - PlayBooks - Part of code

– Create Runbooks - Server down

– Capture failures and analyze them using Events and Real-Time Actions

– KPIs for cloud dashboard

– Incidence response - Root Cause Analysis

– Hands-on Exercises / Case Studies

Security, Privacy, and Compliance
– Manage identities for people and machines

– Identify Access Management

 Role-Based, Service-Based, and Attribute-Based Access

– Securely operate your workload.

– Detect and investigate security events

– Web Application Firewall

– Virtual Private Cloud - design network topology

– Protecting your network resources

– Bastion Hosts

– Data classification

– Protecting data in Transit

– Protecting data at Rest

Hands-on Exercises / Case Studies

As a software architect, you're at the forefront of building scalable, secure, and resilient systems that drive innovation while safeguarding critical digital assets. This workshop is designed to equip you with actionable strategies, cutting-edge tools, and deep technical insights into embedding security into every phase of the software development lifecycle.
In this immersive, hands-on session, we will explore how to elevate your DevSecOps practices to meet the challenges of today’s evolving threat landscape while ensuring productivity and operational excellence.

What You'll Learn:

• Integrating Security into CI/CD Pipelines:
Discover how to build efficient, secure workflows that empower teams to deliver faster without compromising security. Learn to implement security gates and automate vulnerability detection with tools like SAST, DAST, and dependency scanning.
• Tracking Metrics for Success:
Understand key DevSecOps metrics such as lead time, deployment frequency, mean-time-to-recover (MTTR), and change fail rate. Learn how observability tools provide actionable insights to optimize performance and reliability.
• Proactive Security with Ethical Hacking:
Dive into penetration testing and simulate real-world attacks to uncover vulnerabilities in your systems before attackers do. Get hands-on with industry-standard tools such as OWASP ZAP.
• OWASP Top 10 for Applications, APIs, LLMs, and Mobile:
  • Applications: Learn to identify and mitigate risks such as injection flaws, broken authentication, and misconfigurations.
  • APIs: Secure APIs with best practices for authorization, schema validation, and rate limiting to combat vulnerabilities like excessive data exposure.
  • LLMs: Explore unique challenges in securing Large Language Models (AI-driven applications), including prompt injection and data leakage.
  • Mobile: Address mobile app vulnerabilities such as insecure storage and transport layer protection, ensuring your applications are robust across platforms.
• Aligning Security with Business Outcomes:
Bridge the gap between technical practices and business goals by aligning DevSecOps with operational KPIs. Demonstrate how secure development drives measurable business value, customer satisfaction, and operational excellence.

Why You Should Attend:

• Hands-On Experience: Participate in live demonstrations of vulnerabilities, attacks, and their mitigation across applications, APIs, LLMs, and mobile systems.
• Cutting-Edge Tools and Techniques: Gain exposure to the latest tools and practices in CI/CD, ethical hacking, and observability.
• Business-Driven Approach: Learn how to connect DevSecOps efforts to organizational success, making security an enabler of productivity.
• Collaborate with Experts: Engage with peers and experts to discuss real-world challenges and share actionable solutions.

Who Should Attend:
This workshop is ideal for:

• Software Architects seeking to embed security into every layer of system design.
• Senior Developers striving to deliver secure, high-performing applications.
• Technical Leads responsible for ensuring compliance and operational excellence in development pipelines.

Join us for this transformative session to gain the skills and knowledge necessary to design secure, scalable, and resilient systems that protect your organization and enable innovation.

Salient Points Architects Will Learn in This Talk

• Shift-Left Security: Embed security early in the CI/CD process to catch and resolve vulnerabilities proactively.
• Key DevSecOps Metrics: Track and optimize lead time, deployment frequency, MTTR, and change fail rate for operational excellence.
• Ethical Hacking: Learn how to simulate real-world attacks to identify and fix vulnerabilities before they become threats.
• OWASP Top 10 Insights: Gain actionable strategies to mitigate risks across applications, APIs, LLMs, and mobile platforms.
• API Security Best Practices: Implement schema validation, token-based authentication, and rate limiting to safeguard APIs.
• AI Security for LLMs: Address unique vulnerabilities in Large Language Models, including prompt injection and data leakage.
• Mobile App Security: Protect mobile systems from insecure data storage and transport vulnerabilities using OWASP Mobile Top 10.
• Business Alignment: Connect DevSecOps practices to measurable business KPIs, demonstrating security’s value in driving success.

This session is a must-attend for architects aiming to design secure, scalable systems while staying ahead in the rapidly evolving security landscape.

Dynamic Programming (DP) intimidates even seasoned engineers. With the right lens, it’s just optimal substructure + overlapping subproblems turned into code. In this talk, we start from a brute-force recursive baseline, surface the recurrence, convert it to memoization and tabulation, and connect it to real systems (resource allocation, routing, caching). Along the way you’ll see how to use AI tools (ChatGPT, Copilot) to propose recurrences, generate edge cases, and draft tests—while you retain ownership of correctness and complexity. Expect pragmatic patterns you can reuse in interviews and production.

Why Now

• DP = #1 fear topic in interviews.
• Used in systems: caching, routing, scheduling.
• 55% faster with Copilot, but needs guardrails.
• AI adoption is surging — structure required.

Key Framework

• Find optimal substructure.
• Spot overlapping subproblems.
• Start brute force → derive recurrence.
• Memoization → tabulation.
• Compare vs. greedy & divide-and-conquer.
• Use AI for tests & recurrences, not correctness.

Core Content

• Coin Change: brute force → DP; greedy fails in non-canonical coins.
• 0/1 Knapsack: DP works, greedy fails; fractional knapsack = greedy.
• LIS: O(n²) DP vs. O(n log n) patience method.
• Graphs: shortest path as DP on DAGs.
• AI Demos: recurrence suggestion, edge-case generation.

Learning Outcomes

• Know when a problem is DP-worthy.
• Build recurrence → memoization → tabulation.
• Decide Greedy vs DP confidently.
• Apply AI prompts safely (tests, refactors).
• Map DP to real-world systems.

AI is moving from pilots to production faster than most enterprises are prepared for. Over the next 3–5 years, architectures must evolve to support agentic workflows, governed AI, secure inference, and cost-efficient operations.

This session gives you a practical blueprint for building an AI-native enterprise architecture—powered by MCP/LangGraph orchestration, GraphRAG retrieval, ISO/IEC 42001 governance, NIST AI RMF safety controls, confidential computing, and post-quantum cryptography.

You’ll leave with a 90-day activation plan and a 3-year roadmap for designing safe, trusted, and scalable AI systems.

KEY TAKEAWAYS
•Clear blueprint for agentic AI architecture
•How to implement governance-as-code (ISO 42001 + NIST AI RMF)
•Patterns for secure & confidential AI (PQC + enclaves)
•FinOps + GreenOps practices for cost & carbon visibility
•AgentOps methods for observability and reliability
•A 90-day plan to start safely
•A 3-year roadmap to modernize EA

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AGENDA
1.The Shift: From AI pilots to agentic platforms
2.Failure Modes: What breaks in real enterprises
3.Blueprint: MCP, LangGraph, GraphRAG, tool safety
4.Governance & Security: ISO 42001, NIST, PQC, confidential compute
5.FinOps & GreenOps: Cost + carbon per inference
6.AgentOps: Observability & drift detection
7.90-Day Activation Plan
8.3-Year EA Roadmap
9.Closing: Architecting the Trusted AI Enterprise

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 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.

Enterprise Architecture helps in describing what is the current state and helps build a future roadmap. Come prepared to solve many Enterprise Architecture challenges.

As part of the journey, we will explore TOGAF to build our architecture. First, we will create a Baseline Architecture. Next, we will explore the path for the Target Architecture. Finally, after identifying gaps between the two, we will apply a step-by-step process to prepare a roadmap.

“Organizations no longer want their enterprise architecture (EA) practice to be focused on standards, structure and control,” says Marcus Blosch, research vice president at Gartner.

“They want an EA practice that is focused on driving business outcomes, working in a flexible and creative way to help define the future and how to get there.”

We will explore the following domains:

– Data

– Technology

– Application

– Business
This talk will help you build a long-term IT Strategy which matches your Business Strategy.

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.

⸻

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

⸻

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

⸻

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

“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.

We are all familiar with the 3rd Normal form. Does that scale? What are the best practices for designing resilient, multi-tenant, performant databases? In this talk, we will explore the database evaluation process, where we will make choices on technology stacks based on requirements and analyzing the CAP theorem. We will discover different Consistency, Availability, and Partition Tolerance techniques, investigate No-SQL databases, and help new cloud deployments using the 3rd Platform.

Big data has characteristics in the new Cloud domain, which requires storing various data for different use-cases. We will explore the Document data store, Key-value, Columnar NoSQL, Graph NoSQL and NewSQL databases.
Next, we will look at how to do data modeling for NoSQL columnar databases to support highly available partition tolerant use-cases. We will discover different strategies to help multi-tenant requirements. In the end, we will look at how to choose the right database? We will also see what the future of Databases are comparing based on Consistency Models, Schema Models, Database Languages, and Database storage.

We will look at data quality patterns and issues and how to use MDM strategy to fix these issues. We will explore survivor ship records and how to validate if the data is correct in system. In the end we will also look at GDPR and PII data strategies.

We will be exploring following databases types:

• Key-value stores

• Wide column stores

• Document stores

• Time Series DBMS

• Graph DBMS

• Object oriented DBMS

• Search engines

• RDF stores

• Spatial DBMS

• Event Stores

• Content stores

A few of the technologies we will explore are

• Cassandra

• Amazon DynamoDB

• MongoDB, HBase

• REDIS, MemcacheDB,

• RDF / SPARQL

• Graph Databases, Neo4J

• CockroachDB

This talk is ideal for the following roles:

• Architects

• Technical Leads

• Programers

• Integration Architects

• Solution Architects

This is a dynamic session exploring the integration of cutting-edge AI technologies into software architecture. This talk provides senior developers and architects with actionable insights on leveraging large language models like ChatGPT to enhance design processes, manage architectural tradeoffs, and achieve scalable, innovative solutions.

Overview of the session

Importance of large language models (LLMs) in software architecture
Introduction to ChatGPT and its relevance for software architects

Part 1:
The Role of Large Language Models in Software Architecture
Understanding the capabilities of LLMs like ChatGPT
Benefits of integrating LLMs in modern software development
Real-world examples of AI-enhanced software architecture

Part 2: Prompt Engineering for Architectural Tasks
Crafting effective prompts for ChatGPT
Strategies for creating precise and effective prompts
Examples of architectural prompts and their impact
Interactive Exercise: Participants craft and test their own prompts
Feedback and discussion on prompt effectiveness

Part 3: Optimizing Requirement Analysis with ChatGPT
Leveraging ChatGPT for requirement analysis and design
Integration of AI in empathizing with client needs and journey mapping
Cost estimations, compliance, security, and performance
Case Study: Using empathy map and customer journey map tools in conjunction with AI
Hands-On Exercise: Requirement analysis and design

Part 4: Managing Architectural Tradeoffs
Defining and understanding architectural tradeoffs
Exploring real-world tradeoff scenarios
Case Study 1: Scalability vs. Flexibility
Case Study 2: Time-to-Market vs. Maintainability
Leveraging AI insights to analyze tradeoffs
Group Discussion and Q&A

Part 5: Best Practices for Integrating AI in Software Architecture
Techniques for gathering and prioritizing project requirements
Aligning architectural decisions with business objectives
Evaluating risks and potential outcomes of tradeoffs
Assessing tools, technologies, and architectural patterns
AI-powered decision support with ChatGPT
Collaborative decision-making and involving stakeholders

Part 6: Achieving Sustainable Innovation
Leveraging tradeoffs to drive innovation and creativity
Recap of key points and takeaways
Panel Discussion with Industry Experts
AI in architectural innovation: ChatGPT in action
Q&A and Open Discussion with the Audience
Conclusion

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

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

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.

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

Modern system design has entered a new era. It’s no longer enough to optimize for uptime and latency — today’s systems must also be AI-ready, token-efficient, trustworthy, and resilient. Whether building global-scale apps, powering recommendation engines, or integrating GenAI agents, architects need new skills and playbooks to design for scale, speed, and reliability.

This full-day workshop blends classic distributed systems knowledge with AI-native thinking. Through case studies, frameworks, and hands-on design sessions, you’ll learn to design systems that balance performance, cost, resilience, and truthfulness — and walk away with reusable templates you can apply to interviews and real-world architectures.

Target Audience

Enterprise & Cloud Architects → building large-scale, AI-ready systems.

Backend Engineers & Tech Leads → leveling up to system design mastery.

AI/ML & Data Engineers → extending beyond pipelines to full-stack AI systems.

FAANG & Big Tech Interview Candidates → preparing for system design interviews with an AI twist.

Engineering Managers & CTO-track Leaders → guiding teams through AI adoption.

Startup Founders & Builders → scaling AI products without burning money.

Learning Outcomes

By the end of the workshop, participants will be able to:

Apply a 7-step system design framework extended for AI workloads.

Design systems that scale for both requests and tokens.

Architect multi-provider failover and graceful degradation ladders.

Engineer RAG 2.0 pipelines with hybrid search, GraphRAG, and semantic caching.

Implement AI trust & security with guardrails, sandboxing, and red-teaming.

Build observability dashboards for hallucination %, drift, token costs.

Reimagine real-world platforms (Uber, Netflix, Twitter, Instagram) with AI integration.

Practice mock interviews & chaos drills to defend trade-offs under pressure.

Take home reusable templates (AI System Design Canvas, RAG Checklist, Chaos Runbook).

Gain the confidence to lead AI-era system design in interviews, enterprises, or startups.

Workshop Agenda (Full-Day, 8 Hours)
Session 1 – Foundations of Modern System Design (60 min)

The new era: Why classic design is no longer enough.

Architecture KPIs in the AI age: latency, tokens, hallucination %, cost.

Group activity: brainstorm new KPIs.

Session 2 – Frameworks & Mindset (75 min)

The 7-Step System Design Framework (AI-extended).

Scaling humans vs tokens.

Token capacity planning exercise.

Session 3 – Retrieval & Resilience (75 min)

RAG 2.0 patterns: chunking, hybrid retrieval, GraphRAG, semantic cache.

Multi-provider resilience + graceful degradation ladders.

Whiteboard lab: design a resilient RAG pipeline.

Session 4 – Security & Observability (60 min)

Threats: prompt injection, data exfiltration, abuse.

Guardrails, sandboxing, red-teaming.

Observability for LLMs: traces, cost dashboards, drift monitoring.

Activity: STRIDE threat-modeling for an LLM endpoint.

Session 5 – Real-World System Patterns (90 min)

Uber, Netflix, Instagram, Twitter, Search, Fraud detection, Chatbot.

AI-enhanced vs classic system designs.

Breakout lab: redesign a system with AI augmentation.

Session 6 – Interviews & Chaos Drills (75 min)

Mock interview challenges: travel assistant, vector store sharding.

Peer review of trade-offs, diagrams, storytelling.

Chaos drills: provider outage, token overruns, fallback runbooks.

Closing (15 min)

Recap: 3 secrets (Scaling tokens, RAG as index, Resilient degradation).

Templates & takeaways: AI System Design Canvas, RAG Checklist, Chaos Runbook.

Q&A + networking.

Takeaways for Participants

AI System Design Canvas (framework for interviews & real-world reviews).

RAG 2.0 Checklist (end-to-end retrieval playbook).

Chaos Runbook Template (resilience drill starter kit).

AI SLO Dashboard template for observability + FinOps.

Confidence to design and defend AI-ready architectures in both career and enterprise contexts.